Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to agricultural informatics, more specifically, relates to agricultural yield prediction system and method thereof.
BACKGROUND OF THE DISCLOSURE
[0002] The proposed invention provides a highly reliable way of forecasting agricultural yield by making use of different kinds of information together in a single framework. Unlike conventional approaches that may be affected by missing data or limited resources, this invention continues to deliver consistent predictions by combining information from multiple sources. Farmers and planners benefit from timely and more trustworthy forecasts, which allows them to organize sowing, harvesting, and marketing in a more effective manner. By focusing on localized variations within fields instead of treating the entire farmland as a single unit, the invention ensures that small but important differences in soil, crop health, and growing conditions are not overlooked. This enables more precise agricultural planning, reduces waste of inputs, and helps increase profitability for farmers. It also provides governments and organizations with dependable data to design food security strategies and policies.
[0003] The invention offers scalability and adaptability across different regions and types of crops, making it suitable for real-world agricultural practices. Many forecasting tools work only for specific crops or under fixed environmental conditions, limiting their use to selected geographies. The present invention, however, can adapt to diverse settings, including areas where data is incomplete or uncertain. This makes it highly useful for developing countries and rural farming communities that often lack detailed ground-level information. By supporting decision-making in such challenging environments, the invention contributes to sustainable farming practices and better use of limited resources like water and fertilizers. Moreover, its ability to handle larger areas and diverse conditions without losing accuracy ensures that it can support both small-scale farmers and large agricultural enterprises. This broad applicability makes the system valuable for long-term agricultural planning and food supply chain management at both local and national levels.
[0004] Another important advantage of the invention is its ability to reduce dependence on detailed manual data collection and human monitoring. Traditional forecasting requires labour-intensive surveys, crop sampling, and extensive field visits, which are both time-consuming and expensive. The invention minimizes this need by learning from available data sources, even if the information is not very detailed. As a result, farmers and agricultural organizations save significant time and resources while still receiving accurate and meaningful yield predictions. This reduces the pressure on manpower and allows professionals to focus on other critical aspects of farming and food management. The system also ensures that predictions are updated regularly, allowing farmers to respond quickly to changes in environmental or crop conditions. Such flexibility and cost-effectiveness make the invention highly practical for widespread adoption, ultimately improving productivity, increasing farmers’ income, and enhancing overall resilience of the agricultural sector against uncertainties.
[0005] Most existing inventions in agricultural forecasting struggle with reliability because they depend heavily on limited types of data. For instance, many systems rely only on satellite images or only on weather information, which can often be incomplete or affected by natural factors such as clouds or seasonal variations. This narrow dependence makes their predictions less accurate and often unsuitable for guiding important farming decisions. Farmers may be misled by incorrect forecasts, leading to poor use of seeds, fertilizers, and water. Furthermore, these systems usually treat a whole field as one uniform area, ignoring the fact that there are variations within the same plot of land. Such oversimplification results in missed opportunities to manage resources better or identify problem areas early. As a result, while these inventions provide general guidance, they often fail to deliver the precision and consistency that is truly required for effective agricultural planning.
[0006] Another major disadvantage of many existing forecasting solutions is that they are not designed to adapt across different regions, crops, or environmental conditions. Often, a model or tool developed for one type of crop or one geographical area performs poorly when applied to another. This lack of scalability means that farmers in diverse regions cannot equally benefit from such inventions, creating limitations in their practical use. For example, a solution developed for large-scale commercial farming in one country may not suit the smallholder farming systems of another. This restricts the impact of these systems and prevents them from addressing the needs of broader populations. Farmers, especially in developing nations, are left without dependable technological support. This limitation reduces the usefulness of existing inventions in addressing global food security challenges, as their effectiveness cannot be assured outside the narrow conditions under which they were originally created.
[0007] Existing systems often demand extensive data collection and detailed annotations to function effectively, which makes them expensive and difficult to implement. Farmers or agricultural organizations may need to invest heavily in manual surveys, ground-based sensors, or specialized field measurements. Such requirements increase both the cost and the effort needed to use these systems, making them impractical for many regions, particularly in rural and underdeveloped areas. Moreover, collecting such detailed data frequently is not feasible, especially during adverse weather conditions or in areas with limited infrastructure. This dependence on costly and labour-intensive processes creates barriers to adoption, meaning that only large and well-funded organizations can fully utilize such solutions. Smallholder farmers, who make up a significant portion of global agriculture, are often excluded from these technologies, thereby widening the gap between technologically advanced farming and resource-constrained agricultural practices.
[0008] Thus, in light of the above-stated discussion, there exists a need for an agricultural yield prediction system and method thereof.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on an agricultural yield prediction system and method thereof which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0011] An objective of the present disclosure is to enable accurate and dependable forecasting of crop production to support improved decision-making in farming practices.
[0012] An objective of the present disclosure is to assist farmers, researchers, and policymakers in managing resources effectively for sustainable agriculture.
[0013] Another objective of the present disclosure is to minimize dependence on costly manual surveys and field-level data collection while still ensuring meaningful insights.
[0014] Another objective of the present disclosure is to remains reliable across diverse environmental conditions and different crop varieties.
[0015] Another objective of the present disclosure is to supports scalable implementation across both smallholder farms and large-scale agricultural enterprises.
[0016] Another objective of the present disclosure is to help governments and organizations in planning food security strategies with more confidence.
[0017] Another objective of the present disclosure is to assist in reducing risks for farmers by offering timely information for planning sowing, irrigation, and harvesting activities.
[0018] Another objective of the present disclosure is to contribute to better use of agricultural inputs, thereby reducing wastage and improving profitability.
[0019] Another objective of the present disclosure is to ensure accessibility and practicality for rural communities and developing regions where resources are limited.
[0020] Yet another objective of the present disclosure is to promote resilience in the agricultural sector by offering consistent guidance despite uncertainties and challenges.
[0021] In light of the above, in one aspect of the present disclosure, an agricultural yield prediction system is disclosed herein. The system comprises a user device configured to enable interaction between a user and the system. The system includes a user interface embedded within the user device and configured to receive agricultural parameters and to display predicted yield results to the user. The system also includes a communication network operatively connected to the user device and configured to transmit the agricultural parameters and receive yield prediction results. The system also includes a processing unit operatively connected to the user device via the communication network and configured to perform computational operations for yield prediction, the processing unit comprising; a preprocessing module configured to refine the received agricultural parameters and remote sensing data by eliminating distortions, normalizing input formats, and preparing standardized datasets, a feature extraction module configured to derive spatial and temporal features from synthetic aperture radar data, multispectral imaging data, and weather observation data, a data integration module configured to combine the spatial and temporal features derived from the synthetic aperture radar data, multispectral imaging data, and weather observation data into a unified multi-modal dataset, a classification module configured to generate agricultural yield predictions for field-level and sub-field-level regions based on the integrated dataset, a vision transformer configured to capture long-range spatial dependencies, a multiple instance learning model configured to perform weakly supervised patch-wise learning for yield estimation.
[0022] In one embodiment, the user device further comprises a storage unit configured to maintain locally cached agricultural data for offline access and synchronization with the processing unit upon reconnection.
[0023] In one embodiment, the user interface further comprises an input visualization unit configured to provide graphical representation of crop conditions, soil characteristics, and environmental factors prior to transmission.
[0024] In one embodiment, the processing unit further comprises a data quality assessment module configured to evaluate completeness and reliability of the received synthetic aperture radar data, multispectral imaging data, and weather observation data before integration.
[0025] In one embodiment, the preprocessing module further comprises a cloud interference handler configured to identify and mitigate cloud-covered regions in multispectral imaging data using synthetic aperture radar substitution.
[0026] In one embodiment, the feature extraction module further comprises a temporal dynamics analyser configured to capture seasonal and growth-cycle based variations from weather observation data.
[0027] In one embodiment, the data integration module further comprises an attention fusion layer configured to assign differential weights to synthetic aperture radar data, multispectral imaging data, and weather observation data prior to yield estimation.
[0028] In one embodiment, the classification module further comprises a sub-field segmentation engine configured to partition agricultural fields into multiple spatial patches for localized yield prediction.
[0029] In one embodiment, the processing unit further comprises a scalability optimizer configured to adapt the system for cross-region deployment across multiple crop types and environmental conditions.
[0030] In light of the above, in one aspect of the present disclosure, an agricultural yield prediction method is disclosed herein. The method comprises receiving agricultural parameters through a user interface, the agricultural parameters including crop information, soil conditions, and environmental observations. The method includes transmitting the agricultural parameters from the user device through a communication network to a processing unit. The method also includes refining the agricultural parameters and remote sensing data in a preprocessing module by eliminating distortions, correcting irregularities, and generating standardized datasets. The method also includes deriving spatial and temporal features in a feature extraction module from synthetic aperture radar data, multispectral imaging data, and weather observation data. The method also includes combining the derived spatial and temporal features in a data integration module into a unified multi-modal dataset. The method also includes generating agricultural yield predictions for field-level and sub-field-level regions in a classification module based on the unified multi-modal dataset. The method also includes analysing the unified multi-modal dataset through a vision transformer integrated within the processing unit for capturing long-range spatial dependencies. The method also includes applying a multiple instance learning model integrated within the processing unit for performing weakly supervised patch-wise learning of agricultural fields.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1 illustrates a block diagram of an agricultural yield prediction system and method thereof, in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 2 illustrates a flowchart of an agricultural yield prediction system, in accordance with an exemplary embodiment of the present disclosure;
[0038] FIG. 3 illustrates a flowchart of an agricultural yield prediction method, in accordance with an exemplary embodiment of the present disclosure;
[0039] FIG. 4 illustrates a process flow diagram of a multi-modal deep learning pipeline for agricultural yield estimation using Vit and MIL, in accordance with an exemplary embodiment of the present disclosure; and
[0040] FIG. 5 illustrates a bar graph of a multi-modal deep learning pipeline for agricultural yield estimation using ViT and MIL attention, in accordance with an exemplary embodiment of the present disclosure.
[0041] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0042] The agricultural yield prediction system and method thereof is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0043] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0044] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0045] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0046] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0047] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0048] Referring now to FIG. 1 to FIG. 5 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of a agricultural yield prediction system and method thereof, in accordance with an exemplary embodiment of the present disclosure.
[0049] The system 100 may include a user device 102 configured to enable interaction between a user and the system 100. The system 100 may also include a user interface 104 embedded within the user device 102 and configured to receive agricultural parameters and to display predicted yield results to the user. The system 100 may also include a communication network 106 operatively connected to the user device 102 and configured to transmit the agricultural parameters and receive yield prediction results. The system 100 may also include a processing unit 108 operatively connected to the user device 102 via the communication network 106 and configured to perform computational operations for yield prediction, the processing unit 108 comprising; a preprocessing module 110 configured to refine the received agricultural parameters and remote sensing data by eliminating distortions, normalizing input formats, and preparing standardized datasets, a feature extraction module 112 configured to derive spatial and temporal features from synthetic aperture radar data, multispectral imaging data, and weather observation data, a data integration module 114 configured to combine the spatial and temporal features derived from the synthetic aperture radar data, multispectral imaging data, and weather observation data into a unified multi-modal dataset, a classification module 116 configured to generate agricultural yield predictions for field-level and sub-field-level regions based on the integrated dataset, a vision transformer 118 configured to capture long-range spatial dependencies, a multiple instance learning model 120 configured to perform weakly supervised patch-wise learning for yield estimation.
[0050] The user device 102 further comprises a storage unit configured to maintain locally cached agricultural data for offline access and synchronization with the processing unit 108 upon reconnection.
[0051] The user interface 104 further comprises an input visualization unit configured to provide graphical representation of crop conditions, soil characteristics, and environmental factors prior to transmission.
[0052] The processing unit 108 further comprises a data quality assessment module configured to evaluate completeness and reliability of the received synthetic aperture radar data, multispectral imaging data, and weather observation data before integration.
[0053] The preprocessing module 110 further comprises a cloud interference handler configured to identify and mitigate cloud-covered regions in multispectral imaging data using synthetic aperture radar substitution.
[0054] The feature extraction module 112 further comprises a temporal dynamics analyser configured to capture seasonal and growth-cycle based variations from weather observation data.
[0055] The data integration module 114 further comprises an attention fusion layer configured to assign differential weights to synthetic aperture radar data, multispectral imaging data, and weather observation data prior to yield estimation.
[0056] The classification module 116 further comprises a sub-field segmentation engine configured to partition agricultural fields into multiple spatial patches for localized yield prediction.
[0057] The processing unit 108 further comprises a scalability optimizer configured to adapt the system for cross-region deployment across multiple crop types and environmental conditions.
[0058] The method 100 may include receiving agricultural parameters through a user interface 104 the agricultural parameters including crop information, soil conditions, and environmental observations. The method 100 may also include transmitting the agricultural parameters from the user device 102 through a communication network 106 to a processing unit 108. The method 100 may also include refining the agricultural parameters and remote sensing data in a preprocessing module 110 by eliminating distortions, correcting irregularities, and generating standardized datasets. The method 100 may also include deriving spatial and temporal features in a feature extraction module 112 from synthetic aperture radar data, multispectral imaging data, and weather observation data. The method 100 may also include combining the derived spatial and temporal features in a data integration module 114 into a unified multi-modal dataset. The method 100 may also include generating agricultural yield predictions for field-level and sub-field-level regions in a classification module 116 based on the unified multi-modal dataset. The method 100 may also include analysing the unified multi-modal dataset through a vision transformer 118 integrated within the processing unit 108 for capturing long-range spatial dependencies. The method 100 may also include applying a multiple instance learning model 120 integrated within the processing unit 108 for performing weakly supervised patch-wise learning of agricultural fields.
[0059] The user device 102 functions as the primary point of interaction between a user and the agricultural yield prediction system 100. The user device 102 comprises hardware and software elements designed to provide seamless communication with the communication network 106 and the processing unit 108. The user device 102 accepts agricultural parameters entered by the user, including crop variety, sowing date, soil nutrient levels, irrigation schedule, and regional weather conditions. The user device 102 further incorporates functionalities for receiving location data through embedded global positioning system receivers, for capturing imagery through integrated camera sensors, and for storing agricultural datasets in local storage units prior to transmission. The user device 102 operates on secure operating environments ensuring data integrity, authentication, and encryption prior to transmitting sensitive agricultural inputs. The user device 102 further manages data synchronization tasks that align input parameters with remote sensing datasets such as synthetic aperture radar data and multispectral imaging data. The user device 102 communicates with the user interface 104 to ensure that data entry fields, graphical visualization panels, and feedback modules are displayed in a manner that simplifies complex agricultural input processes. The user device 102 is operatively configured to support both real-time and batch data transfer through the communication network 106, allowing uninterrupted operation in varying rural or urban agricultural environments. The user device 102 is scalable for deployment across mobile phones, tablets, laptops, or dedicated handheld agricultural monitoring devices. The user device 102 also facilitates two-way communication, enabling retrieval of yield prediction results generated by the processing unit 108 and displaying those results in user-friendly visualizations. The user device 102 executes computational instructions only to the extent necessary for managing the user interface 104 and coordinating secure transmission of data, while more complex predictive operations are executed within the processing unit 108. The user device 102 ensures compliance with standardized communication protocols, thereby supporting interoperability with various communication network infrastructures and ensuring reliability in large-scale agricultural monitoring deployments. The user device 102 therefore operates as the enabling hardware for bridging human interaction with automated computational analysis within the agricultural yield prediction system 100.
[0060] The user interface 104 embedded within the user device 102 functions as the interaction layer that facilitates direct engagement between the user and the agricultural yield prediction system 100. The user interface 104 provides structured input modules through which the user enters agricultural parameters including crop type, soil fertility indicators, irrigation details, pest and disease records, and environmental factors. The user interface 104 further provides visualization panels that display processed results including agricultural yield predictions for field-level and sub-field-level regions. The user interface 104 incorporates dropdown menus, data entry fields, map-based visualization components, and data upload functionalities designed to simplify interaction for users with varying levels of technical knowledge. The user interface 104 ensures that the user has the ability to monitor, review, and confirm agricultural parameters before transmission to the communication network 106. The user interface 104 also supports multi-language capability, ensuring accessibility for users in diverse geographic regions. The user interface 104 integrates seamlessly with sensor-driven inputs captured through the user device 102, including soil condition data and imagery, and organizes such information into coherent data packages. The user interface 104 displays output in both graphical and tabular formats, thereby facilitating ease of interpretation. The user interface 104 is configured to dynamically adjust content presentation based on device specifications such as screen size, resolution, and processing capacity. The user interface 104 integrates data validation mechanisms that prevent incorrect or incomplete information from being transmitted, thereby ensuring accuracy of input parameters. The user interface 104 further serves as a medium for real-time communication between the user and the processing unit 108 by displaying processing status notifications, progress indicators, and confirmation messages. The user interface 104 maintains an intuitive yet robust design to accommodate a wide range of user profiles, including farmers, researchers, and agricultural advisors. The user interface 104 therefore ensures reliability in receiving user input, organizing agricultural data, and presenting results in a manner that directly supports decision-making processes in agricultural yield prediction.
[0061] The communication network 106 establishes secure and reliable connectivity between the user device 102 and the processing unit 108 within the agricultural yield prediction system 100. The communication network 106 facilitates bidirectional data transfer, enabling agricultural parameters entered through the user interface 104 to be transmitted efficiently while also delivering processed yield prediction results back to the user device 102. The communication network 106 incorporates wired and wireless transmission infrastructures, including cellular networks, satellite links, Wi-Fi protocols, and broadband connections, thereby ensuring accessibility across diverse geographic regions. The communication network 106 adheres to secure communication standards that implement encryption, authentication, and error correction for safeguarding agricultural datasets. The communication network 106 is optimized to handle large volumes of data generated by remote sensing inputs, including synthetic aperture radar data and multispectral imaging data, without causing delays or loss of fidelity. The communication network 106 integrates redundancy mechanisms to ensure continuous operation during network failures or disruptions. The communication network 106 also incorporates adaptive routing strategies that dynamically select optimal data paths based on bandwidth availability, latency, and reliability. The communication network 106 supports real-time data streaming when immediate agricultural analysis is required and also facilitates batch transfer when data is accumulated over extended periods. The communication network 106 interfaces seamlessly with the processing unit 108, ensuring that preprocessing operations, feature extraction operations, data integration operations, classification operations, vision transformer operations, and multiple instance learning model operations receive accurate and complete datasets. The communication network 106 is designed to comply with interoperability standards, allowing integration with governmental, institutional, or commercial agricultural monitoring platforms. The communication network 106 therefore provides the foundational infrastructure that enables uninterrupted flow of information across the agricultural yield prediction system 100.
[0062] The processing unit 108 functions as the computational core of the agricultural yield prediction system 100 and operates as the hardware environment that houses multiple analytical modules configured to perform data-driven prediction tasks. The processing unit 108 receives agricultural parameters and a remote sensing input transmitted through the communication network 106 and processes those inputs through a series of structured computational stages. The processing unit 108 is designed with high-performance computing capabilities including multicore processors, graphical processing units, large-scale memory units, and parallel computing frameworks to handle complex datasets. The processing unit 108 is configured to execute machine learning algorithms, artificial intelligence models, and advanced statistical computations that are required for precise agricultural yield estimation. The processing unit 108 ensures data security by applying controlled access mechanisms, encrypted processing, and audit trails for monitoring computational integrity. The processing unit 108 comprises the preprocessing module 110, the feature extraction module 112, the data integration module 114, the classification module 116, the vision transformer 118, and the multiple instance learning model 120. The processing unit 108 coordinates workflow across all modules to ensure that raw data is refined, features are derived, datasets are unified, predictive models are executed, and results are communicated back to the user device 102. The processing unit 108 further incorporates load balancing mechanisms for distributing computational tasks across multiple processors, thereby maintaining efficiency even when datasets from multiple farms, regions, or seasons are simultaneously processed. The processing unit 108 executes error detection and correction routines to prevent inaccuracies and inconsistencies in processed results. The processing unit 108 is scalable and deployable across local servers, cloud infrastructures, or hybrid platforms, ensuring adaptability to varying deployment scenarios. The processing unit 108 therefore functions as the centralized environment that integrates all analytical processes for agricultural yield prediction within the agricultural yield prediction system 100.
[0063] The preprocessing module 110 embedded within the processing unit 108 functions as the primary data refinement stage within the agricultural yield prediction system 100. The preprocessing module 110 is configured to refine agricultural parameters and remote sensing data by applying procedures that include noise reduction, normalization, missing value treatment, and conversion of raw inputs into standardized formats. The preprocessing module 110 processes synthetic aperture radar data by applying speckle noise reduction algorithms and geometric corrections to ensure precise alignment with multispectral imaging data. The preprocessing module 110 further processes multispectral imaging data by conducting radiometric calibration, atmospheric correction, and spectral normalization to ensure uniformity across datasets obtained under varying environmental conditions. The preprocessing module 110 refines weather observation data by removing anomalies, aligning temporal records, and interpolating missing values, thereby generating complete and coherent time-series datasets. The preprocessing module 110 integrates data quality assessment tools that monitor the consistency, reliability, and validity of input datasets prior to transmission into downstream modules. The preprocessing module 110 applies dimensionality reduction techniques that eliminate redundant attributes while preserving essential agricultural features. The preprocessing module 110 executes standardized scaling routines that ensure compatibility between heterogeneous datasets including numerical, categorical, and spatial parameters. The preprocessing module 110 ensures that all refined data is stored in intermediate structured formats that facilitate seamless processing by the feature extraction module 112 and the data integration module 114. The preprocessing module 110 is designed to execute operations in parallel for large datasets, thereby minimizing processing delays and ensuring timely execution of yield prediction workflows. The preprocessing module 110 therefore functions as the foundational module that ensures accuracy, consistency, and compatibility of agricultural parameters and remote sensing data within the agricultural yield prediction system 100.
[0064] The feature extraction module 112 embedded within the processing unit 108 functions as the analytical stage that derives spatial, spectral, and temporal features from preprocessed datasets for agricultural yield prediction. The feature extraction module 112 processes synthetic aperture radar data to derive backscatter coefficients, texture measures, and crop growth indicators that reflect soil moisture, canopy structure, and biomass distribution. The feature extraction module 112 processes multispectral imaging data to generate vegetation indices, chlorophyll content estimators, leaf area indices, and other spectral signatures that provide indicators of crop health and growth stage. The feature extraction module 112 processes weather observation data to derive temporal features including rainfall distribution, temperature variability, humidity trends, and wind velocity fluctuations, which are critical determinants of agricultural yield. The feature extraction module 112 applies spatiotemporal feature engineering techniques that integrate remote sensing data with ground-level agricultural parameters such as sowing dates, irrigation schedules, and soil nutrient levels. The feature extraction module 112 incorporates machine learning-based feature selection algorithms that identify the most predictive variables while eliminating redundant or irrelevant attributes. The feature extraction module 112 supports extraction of both pixel-level and object-level features, ensuring that predictions are accurate at field-level and sub-field-level resolutions. The feature extraction module 112 applies statistical transformations, principal component analysis, and wavelet decomposition to enhance the predictive power of extracted features. The feature extraction module 112 ensures that derived features are aligned in terms of spatial resolution, temporal frequency, and coordinate systems prior to integration by the data integration module 114. The feature extraction module 112 is configured to adapt feature derivation procedures based on crop type, growth stage, and regional conditions to ensure contextual accuracy of agricultural yield prediction. The feature extraction module 112 therefore functions as the specialized analytical component that transforms raw agricultural datasets into structured features optimized for predictive modelling within the agricultural yield prediction system 100.
[0065] The data integration module 114 embedded within the processing unit 108 functions as the unifying stage within the agricultural yield prediction system 100 and is configured to combine spatial features, temporal features, and spectral features derived from the feature extraction module 112 into a single coherent multi-modal dataset. The data integration module 114 ensures alignment of synthetic aperture radar data, multispectral imaging data, and weather observation data by applying coordinate system transformation, temporal synchronization, and spatial resolution resampling. The data integration module 114 employs advanced fusion algorithms including weighted averaging, statistical correlation analysis, and machine learning-based fusion strategies to preserve critical agricultural patterns while minimizing redundancy. The data integration module 114 is configured to generate a unified feature space that accurately reflects crop health, environmental variability, and yield determinants across field-level and sub-field-level regions. The data integration module 114 supports heterogeneous dataset handling, enabling effective combination of numerical data, categorical data, and spatially distributed data. The data integration module 114 applies consistency checks to ensure that integrated datasets remain free from inconsistencies, duplication, or missing alignment. The data integration module 114 prepares data batches optimized for input into the classification module 116, the vision transformer 118, and the multiple instance learning model 120. The data integration module 114 supports dynamic adaptability by incorporating additional data modalities when required, including hyperspectral imaging, soil property measurements, or phenological observations. The data integration module 114 functions as the bridging mechanism that transforms disparate datasets into a unified structure optimized for predictive analytics within the agricultural yield prediction system 100.
[0066] The classification module 116 embedded within the processing unit 108 functions as the predictive analysis component within the agricultural yield prediction system 100 and is configured to generate agricultural yield predictions at field-level and sub-field-level scales. The classification module 116 processes the integrated datasets generated by the data integration module 114 and applies machine learning models, statistical classifiers, and deep learning networks to assign yield categories or continuous yield values. The classification module 116 incorporates supervised learning architectures for regions with historical ground-truth data and semi-supervised learning architectures for regions where field-level labels are limited. The classification module 116 employs optimization strategies including cross-validation, regularization, and hyperparameter tuning to enhance predictive accuracy. The classification module 116 supports output generation in multiple formats including tabular predictions, georeferenced yield maps, and time-series yield forecasts. The classification module 116 is configured to quantify prediction confidence by embedding probabilistic estimation methods and uncertainty quantification frameworks. The classification module 116 ensures that predictions are consistent across spatial and temporal scales by applying calibration techniques that align outputs with agronomic realities. The classification module 116 communicates prediction outputs back to the user device 102 through the communication network 106 for visualization on the user interface 104. The classification module 116 incorporates scalable architectures capable of adapting to regional variations in crop type, soil condition, and climate pattern. The classification module 116 is configured to interact with the vision transformer 118 and the multiple instance learning model 120 to enhance yield prediction accuracy through integration of advanced spatial and weakly supervised learning mechanisms. The classification module 116 therefore functions as the predictive decision-making component that transforms integrated agricultural datasets into actionable yield forecasts within the agricultural yield prediction system 100.
[0067] The vision transformer 118 embedded within the processing unit 108 functions as the deep learning component configured to capture long-range spatial dependencies within agricultural datasets of the agricultural yield prediction system 100. The vision transformer 118 processes image patches derived from synthetic aperture radar data and multispectral imaging data and converts them into tokenized embedding’s that preserve spatial and spectral relationships. The vision transformer 118 applies multi-head self-attention mechanisms to compute pairwise interactions between image patches across entire agricultural fields, thereby enabling detection of spatial correlations extending beyond local neighbourhoods. The vision transformer 118 leverages positional encoding strategies to maintain the structural arrangement of image patches during model training and prediction. The vision transformer 118 generates high-dimensional feature representations that capture crop growth variability, soil moisture distribution, and canopy structure differences at both field-level and sub-field-level scales. The vision transformer 118 interacts with the data integration module 114 to ensure that spatial embeddings are aligned with temporal and environmental features derived from weather observation data. The vision transformer 118 enhances classification module 116 performance by supplying refined feature representations that capture global agricultural patterns. The vision transformer 118 is designed to support weakly supervised learning pipelines in conjunction with the multiple instance learning model 120, thereby enabling yield prediction even when detailed ground-truth labels are not available at pixel-level resolution. The vision transformer 118 incorporates optimization routines including gradient-based learning, dropout regularization, and batch normalization to stabilize training and prevent overfitting. The vision transformer 118 ensures scalability across diverse agricultural datasets by adapting tokenization strategies based on input image resolution, sensor modality, and crop type. The vision transformer 118 therefore functions as the advanced deep learning component that models global spatial interactions for precise agricultural yield prediction within the agricultural yield prediction system 100.
[0068] The multiple instance learning model 120 embedded within the processing unit 108 functions as the weakly supervised deep learning framework within the agricultural yield prediction system 100 and is configured to perform patch-wise learning for yield estimation when only field-level yield labels are available. The multiple instance learning model 120 operates on grouped inputs called bags, where each bag contains multiple image patches or data segments derived from synthetic aperture radar data, multispectral imaging data, and weather observation data. The multiple instance learning model 120 assigns attention-based weights to individual patches within each bag, thereby identifying the most informative regions that contribute significantly to yield variation. The multiple instance learning model 120 does not require pixel-level or patch-level labels for supervision, which reduces dependency on expensive and time-consuming manual annotation. The multiple instance learning model 120 interacts with the vision transformer 118 by processing the high-dimensional embeddings generated from tokenized patches and applying aggregation strategies to combine them into yield-relevant bag-level representations. The multiple instance learning model 120 employs attention pooling, maximum pooling, and statistical aggregation techniques to generate predictions that accurately reflect field-level yield. The multiple instance learning model 120 integrates temporal variability by incorporating weather-derived features into bag representations, thereby enabling a holistic understanding of both spatial and environmental influences on crop yield. The multiple instance learning model 120 functions as a mechanism for handling missing data and cloud-covered satellite imagery by redistributing attention away from noisy or incomplete patches toward patches with higher informational content. The multiple instance learning model 120 ensures scalability by supporting bags of variable size, enabling application across smallholder plots, large-scale commercial farms, and multi-regional datasets. The multiple instance learning model 120 applies optimization strategies including gradient-based learning, attention weight normalization, and stochastic regularization to stabilize model performance across heterogeneous agricultural datasets. The multiple instance learning model 120 is configured to output refined yield predictions to the classification module 116, thereby contributing to final decision-making and visualization of results on the user device 102 through the user interface 104. The multiple instance learning model 120 represents the novelty and inventive step of the agricultural yield prediction system 100 by enabling effective prediction under weak supervision, integrating spatial embeddings from the vision transformer 118, and dynamically attending to critical sub-field regions that traditional machine learning frameworks fail to capture. The multiple instance learning model 120 therefore functions as a cornerstone component that addresses limitations of existing systems and ensures robust, scalable, and detailed agricultural yield forecasting within the agricultural yield prediction system 100.
[0069] In one embodiment, the preprocessing module 110 embedded within the processing unit 108 is configured to handle cloud interference in remote sensing data by automatically identifying cloud-affected image patches and applying synthetic aperture radar data as a corrective substitute, thereby ensuring that the integrated dataset processed by the data integration module 114 remains consistent and uninterrupted for agricultural yield prediction.
[0070] In one embodiment, the feature extraction module 112 embedded within the processing unit 108 is configured to capture intra-field variability by segmenting satellite images into sub-field patches before generating spatial and spectral features, wherein these sub-field patches are further processed by the vision transformer 118 to preserve differences across crop growth zones that directly affect yield outcomes.
[0071] In one embodiment, the multiple instance learning model 120 embedded within the processing unit 108 is configured to learn from weak supervision by receiving field-level labels instead of pixel-level or patch-level labels, wherein the multiple instance learning model 120 applies attention-based aggregation across grouped image patches to generate accurate field-level and sub-field-level yield predictions.
[0072] In one embodiment, the classification module 116 embedded within the processing unit 108 is configured to produce georeferenced yield maps that represent prediction results in a spatial visualization format, wherein the classification module 116 communicates the georeferenced yield maps to the user device 102 through the communication network 106 for display on the user interface 104.
[0073] In one embodiment, the data integration module 114 embedded within the processing unit 108 is configured to combine synthetic aperture radar data, multispectral imaging data, and weather observation data with additional agricultural datasets including soil property measurements and phenological growth records, thereby extending the scope of the integrated dataset and enhancing the predictive capability of the agricultural yield prediction system 100.
[0074] In one embodiment, the vision transformer 118 embedded within the processing unit 108 is configured to tokenize image patches from synthetic aperture radar data and multispectral imaging data into embeddings, wherein the positional encoding of the vision transformer 118 preserves both spatial order and temporal sequence to support long-range interaction analysis across cropping seasons.
[0075] In one embodiment, the user interface 104 embedded within the user device 102 is configured to allow the user to input crop type, planting date, and soil condition parameters, wherein these agricultural parameters are transmitted through the communication network 106 to the processing unit 108 for preprocessing and subsequent prediction analysis.
[0076] In one embodiment, the agricultural yield prediction system 100 is configured to operate in a scalable manner across diverse geographical regions by dynamically adjusting the preprocessing module 110 to match local resolution levels of synthetic aperture radar data, multispectral imaging data, and weather observation data, thereby enabling cross-region adaptability in agricultural yield forecasting.
[0077] In one embodiment, the agricultural yield prediction system 100 integrates an uncertainty quantification mechanism within the classification module 116, wherein the classification module 116 attaches confidence scores to predicted outputs and transmits both yield predictions and uncertainty values to the user device 102 for visualization through the user interface 104.
[0078] In one embodiment, the agricultural yield prediction system 100 employs a temporal synchronization mechanism embedded within the data integration module 114, wherein the temporal synchronization mechanism aligns weather observation data with satellite imagery acquisition times, ensuring that all integrated datasets processed by the classification module 116 and the multiple instance learning model 120 reflect accurate temporal correspondence in agricultural yield forecasting.
[0079] FIG. 2 illustrates a flowchart of an agricultural yield prediction system, in accordance with an exemplary embodiment of the present disclosure.
[0080] At 202, user device provides agricultural parameters through the user interface.
[0081] At 204, communication network transmits agricultural parameters to the processing unit.
[0082] At 206, preprocessing module refines and standardizes received parameters and remote sensing data.
[0083] At 208, feature extraction module derives spatial and temporal features from SAR, MSI, and weather data.
[0084] At 210, data integration module combines extracted features into a unified multi-modal dataset.
[0085] At 212, vision transformer and multiple instance learning model process integrated dataset for pattern detection and patch-wise yield estimation.
[0086] At 214, classification module generates yield prediction results and sends them to the user interface for display.
[0087] FIG. 3 illustrates a flowchart of an agricultural yield prediction method, in accordance with an exemplary embodiment of the present disclosure.
[0088] At 302, receiving agricultural parameters through a user interface the agricultural parameters including crop information, soil conditions, and environmental observations.
[0089] At 304, transmitting the agricultural parameters from the user device through a communication network to a processing unit.
[0090] At 306, refining the agricultural parameters and remote sensing data in a preprocessing module by eliminating distortions, correcting irregularities, and generating standardized datasets.
[0091] At 308, deriving spatial and temporal features in a feature extraction module from synthetic aperture radar data, multispectral imaging data, and weather observation data.
[0092] At 310, combining the derived spatial and temporal features in a data integration module into a unified multi-modal dataset.
[0093] At 312, generating agricultural yield predictions for field-level and sub-field-level regions in a classification module based on the unified multi-modal dataset.
[0094] At 314, analysing the unified multi-modal dataset through a vision transformer integrated within the processing unit for capturing long-range spatial dependencies.
[0095] At 316, applying a multiple instance learning model integrated within the processing unit for performing weakly supervised patch-wise learning of agricultural fields.
[0096] FIG. 4 illustrates a process flow diagram of a multi-modal deep learning pipeline for agricultural yield estimation using Vit and MIL, in accordance with an exemplary embodiment of the present disclosure.
[0097] Satellite imagery 402 serves as the foundational input in the multi-modal deep learning pipeline for agricultural yield estimation. Satellite imagery 402 provides spatial and temporal information necessary to assess crop development across varying geographical regions. The collected data from satellite imagery 402 includes spectral bands, vegetation indices, and land surface details, which are essential for capturing field-level variability. Satellite imagery 402 ensures continuous and large-scale monitoring of agricultural fields, supplying high-dimensional data that becomes the initial source for downstream processing in the prediction pipeline.
[0098] Patch extraction 404 processes the satellite imagery 402 by dividing the collected data into smaller, manageable patches. Patch extraction 404 ensures that localized variations within agricultural fields are effectively captured, allowing the framework to focus on sub-field patterns. Patch extraction 404 enhances the representation of intra-field heterogeneity by creating patch-level datasets from the large satellite imagery 402 inputs. Through patch extraction 404, the system preserves fine-grained spatial detail that supports accurate agricultural yield estimation at both field-level and sub-field-level regions.
[0099] Vision transformer encoder 406 processes the patches generated by patch extraction 404 to capture long-range spatial dependencies across the dataset. Vision transformer encoder 406 extracts deep representations by analysing positional embeddings and multi-head self-attention mechanisms applied to each patch. Vision transformer encoder 406 ensures that patterns such as crop growth structures and spatial interactions are effectively modelled. By handling the extracted patches from patch extraction 404, vision transformer encoder 406 generates robust feature embeddings that contribute to precise yield forecasting within the integrated pipeline.
[0100] Yield prediction 408 generates the final estimation of agricultural output by utilizing the feature embeddings produced by vision transformer encoder 406. Yield prediction 408 aggregates patch-level information into field-level outcomes using multiple instance learning principles. Yield prediction 408 ensures that predictions reflect both localized variations and aggregated field-scale results. The processed features derived from vision transformer encoder 406 are translated into accurate yield prediction 408 outputs, which are then conveyed to the user interface for decision-making in agricultural management and planning applications.
[0101] FIG. 5 illustrates a bar graph of a multi-modal deep learning pipeline for agricultural yield estimation using ViT and MIL attention, in accordance with an exemplary embodiment of the present disclosure.
[0102] The bar graph represents the relative performance contributions of satellite data, vision transformer encoder, multiple instance learning attention, and yield prediction. Satellite data provides foundational multi-modal inputs, the vision transformer encoder extracts deep spatial representations, and multiple instance learning attention emphasizes relevant patch-level features. The yield prediction component integrates the processed outputs into accurate agricultural yield forecasts. The graphical representation highlights the collective effectiveness of each stage in the pipeline for robust yield estimation.
[0103] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0104] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0105] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0106] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0107] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A agricultural yield prediction system (100) comprising:
a user device (102), configured to enable interaction between a user and the system (100);
a user interface (104), embedded within the user device (102) and configured to receive agricultural parameters and to display predicted yield results to the user;
a communication network (106), operatively connected to the user device (102) and configured to transmit the agricultural parameters and receive yield prediction results;
a processing unit (108), operatively connected to the user device (102), via the communication network (106) and configured to perform computational operations for yield prediction, the processing unit (108) comprising:
a preprocessing module (110), configured to refine the received agricultural parameters and remote sensing data by eliminating distortions, normalizing input formats, and preparing standardized datasets;
a feature extraction module (112), configured to derive spatial and temporal features from synthetic aperture radar data, multispectral imaging data, and weather observation data;
a data integration module (114), configured to combine the spatial and temporal features derived from the synthetic aperture radar data, multispectral imaging data, and weather observation data into a unified multi-modal dataset;
a classification module (116), configured to generate agricultural yield predictions for field-level and sub-field-level regions based on the integrated dataset;
a vision transformer (118), configured to capture long-range spatial dependencies;
a multiple instance learning model (120), configured to perform weakly supervised patch-wise learning for yield estimation.
2. The system (100) as claimed in claim 1, wherein the user device (102), further comprises a storage unit configured to maintain locally cached agricultural data for offline access and synchronization with the processing unit (108) upon reconnection.
3. The system (100) as claimed in claim 1, wherein the user interface (104), further comprises an input visualization unit configured to provide graphical representation of crop conditions, soil characteristics, and environmental factors prior to transmission.
4. The system (100) as claimed in claim 1, wherein the processing unit (108), further comprises a data quality assessment module configured to evaluate completeness and reliability of the received synthetic aperture radar data, multispectral imaging data, and weather observation data before integration.
5. The system (100) as claimed in claim 1, wherein the preprocessing module (110), further comprises a cloud interference handler configured to identify and mitigate cloud-covered regions in multispectral imaging data using synthetic aperture radar substitution.
6. The system (100) as claimed in claim 1, wherein the feature extraction module (112), further comprises a temporal dynamics analyser configured to capture seasonal and growth-cycle based variations from weather observation data.
7. The system (100) as claimed in claim 1, wherein the data integration module (114), further comprises an attention fusion layer configured to assign differential weights to synthetic aperture radar data, multispectral imaging data, and weather observation data prior to yield estimation.
8. The system (100) as claimed in claim 1, wherein the classification module (116), further comprises a sub-field segmentation engine configured to partition agricultural fields into multiple spatial patches for localized yield prediction.
9. The system (100) as claimed in claim 1, wherein the processing unit (108), further comprises a scalability optimizer configured to adapt the system for cross-region deployment across multiple crop types and environmental conditions.
10. A agricultural yield prediction method (100) comprising:
receiving agricultural parameters through a user interface (104), the agricultural parameters including crop information, soil conditions, and environmental observations;
transmitting the agricultural parameters from the user device (102), through a communication network (106) to a processing unit (108);
refining the agricultural parameters and remote sensing data in a preprocessing module (110), by eliminating distortions, correcting irregularities, and generating standardized datasets;
deriving spatial and temporal features in a feature extraction module (112), from synthetic aperture radar data, multispectral imaging data, and weather observation data;
combining the derived spatial and temporal features in a data integration module (114), into a unified multi-modal dataset;
generating agricultural yield predictions for field-level and sub-field-level regions in a classification module (116), based on the unified multi-modal dataset;
analysing the unified multi-modal dataset through a vision transformer (118), integrated within the processing unit (108), for capturing long-range spatial dependencies;
applying a multiple instance learning model (120), integrated within the processing unit (108), for performing weakly supervised patch-wise learning of agricultural fields.