1. A system for multimodel early crop disease prediction, comprising: at least one processor, and a memory storing instructions that, when executed by the at least one processor, cause the system to: the received data from multiple image data (102) input sources associated with crops; the received data acquisition (104) is preprocessed (106) to generate processed data; an extract features (108) from the processed data; the extracted features (108) analyzed using one or more trained machine learning models (110); and wherein the prediction of a crop disease condition generated.

2. The system as claimed in claim 1, wherein the data acquisition (104) module is configured to receive image data associated with crop conditions.

3. The system as claimed in claim 1, wherein the preprocessing (106) module is configured to normalize and enhance the received data prior to feature extraction.

4. The system as claimed in claim 1, wherein the feature extraction (108) module is configured to derive relevant features from the preprocessed data for analysis.

5. The system as claimed in claim 1, wherein the analysis module comprises one or more trained machine learning models (110) configured to analyze the extracted features and generate disease prediction.

6. The system as claimed in claim 1, wherein the output module (112) is configured to generate outputs including predicted crop disease condition and associated confidence score.

7. The system as claimed in claim 1, wherein the system combines outputs (112) from multiple data sources to improve prediction accuracy.

8. The system as claimed in claim 1, wherein the system generates a confidence score associated with the predicted crop disease condition.

6. DATE AND SIGNATURE Dated this on 09th April, 2026 Signature Mr. Srinivas Maddipati (IN/PA 3124) Agent for applicant

Description:4. DESCRIPTION
Technical Field of the Invention

The present invention relates to the field of agricultural technology and machine learning. More particularly, the invention relates to a system for multimodal early crop disease prediction using multiple types of input data, including integration and analysis of heterogeneous data sources to improve accuracy and enable early-stage disease identification.

Background of the Invention

Crop diseases pose a significant challenge to agricultural productivity, often leading to reduction in yield and quality. Early detection of such diseases is important to prevent their spread and to enable timely intervention. However, in many cases, disease symptoms are not easily visible at early stages, making detection difficult through conventional methods.

Traditional approaches for identifying crop diseases rely on manual inspection by farmers or experts. These methods are time-consuming, subjective, and dependent on individual experience. In large-scale farming, continuous monitoring is also not practical, which may result in delayed detection and increased losses.

With the advancement of technology, image-based and data-driven methods have been developed for disease detection. However, many existing systems rely on a single type of input data, such as images, and may not perform reliably under varying environmental conditions such as changes in lighting, weather, and background.

Early-stage disease prediction requires analysis of multiple factors, including visual symptoms, environmental conditions, and other relevant inputs. Existing solutions often lack the ability to effectively combine such diverse data sources, which limits their accuracy and usefulness.

Accordingly, there exists a need for a system that can utilize multiple types of data and provide accurate and early prediction of crop diseases in a reliable and efficient manner, while effectively handling variations in environmental conditions and improving prediction performance through integrated data analysis.

Objects of the Invention

The principal object of the present invention to provide a system for early prediction of crop diseases using multiple types of input data.

Another object of the invention is to improve prediction accuracy by combining different data sources.

Another object of the invention is to enable timely detection of diseases at an early stage.

Another object of the invention is to provide a system that performs reliably under varying environmental conditions.

Another object of the invention is to provide a system that processes and analyses data in an efficient manner.

Another object of the invention is to provide a system that improves prediction performance over time using feedback and updated data.
Brief Summary of the Invention

The present invention provides a system for multimodal early crop disease prediction configured to receive data from multiple sources, including image data and other relevant inputs associated with crop conditions. The received data is processed to ensure consistency and suitability for further analysis.

According to an aspect of the present invention, the processed data is used for extracting relevant features, which are analyzed using one or more trained machine learning models to identify patterns associated with crop diseases. The use of multiple data types enables improved understanding of crop conditions.

In another aspect of the present invention, the outputs obtained from different data sources are combined to generate an early prediction of crop disease, thereby improving accuracy and reliability as compared to conventional single-input approaches.

In another aspect of the present invention, the prediction results include disease identification and associated confidence levels, which assist in timely decision-making and effective crop management.

In another aspect of the present invention, the system incorporate a feedback mechanism to improve prediction performance over time by utilizing updated data and refining the underlying models.

Brief Description of the Drawings

The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:
Fig. 1 illustrates a flowchart of a system for multi model (110) early crop disease prediction, in accordance with an exemplary embodiment of the present invention;

Detailed Description of the Invention

It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. In addition, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. 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. Further, the use of terms “first”, “second”, and “third”, and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

According to an exemplary embodiment of the present invention, a system for multimodal early crop disease prediction using multiple types of input data. The system is designed to improve early detection by combining different data sources. This enables more reliable and accurate prediction compared to conventional approaches.

In accordance with an exemplary embodiment of the present invention, wherein the system comprises a data acquisition module configured to receive data from multiple sources associated with crops, including image data and other relevant inputs. The data may be collected from user devices or external sources, thereby ensuring availability of diverse information for analysis.

In accordance with an exemplary embodiment of the present invention, wherein the received data is processed using a preprocessing module to ensure consistency and suitability for further analysis. The preprocessing includes normalization, noise reduction, and enhancement of the data, which helps in handling variations in input conditions.

In accordance with an exemplary embodiment of the present invention, wherein a feature extraction module is configured to extract relevant features from the processed data. These features represent important characteristics required for identifying crop disease conditions and improve the effectiveness of the prediction process.

In accordance with an exemplary embodiment of the present invention, wherein an analysis module comprising one or more trained machine learning models is configured to analyze the extracted features. The models identify patterns and generate predictions related to crop diseases, thereby enabling accurate and early detection.

In accordance with an exemplary embodiment of the present invention, wherein the system is configured to combine outputs from multiple data sources. This multimodal approach improves prediction accuracy and reliability and reduces errors caused by dependency on a single data type.

In accordance with an exemplary embodiment of the present invention, wherein an output module is configured to generate outputs including predicted crop disease condition and associated confidence level. The results are presented in a user-understandable format to support effective decision-making.

In accordance with an exemplary embodiment of the present invention, wherein the system is capable of performing prediction at an early stage, even when visible symptoms are minimal or not clearly identifiable. This enables timely preventive and corrective actions.

In accordance with an exemplary embodiment of the present invention, wherein the system operates effectively under varying environmental conditions, including changes in lighting, weather, and background. This ensures consistent performance in real-world agricultural scenarios.

In accordance with an exemplary embodiment of the present invention, wherein the system further comprises a feedback module configured to utilize new data or user input. The feedback is used to improve prediction performance over time, enabling continuous learning.

In accordance with an exemplary embodiment of the present invention, wherein the system provides a reliable and efficient solution by integrating multiple data sources and applying machine learning-based analysis, thereby supporting improved crop monitoring and disease management.

In reference to the figures, fig. 1 illustrates a flowchart of a system for multi model early crop disease prediction, wherein image data (102) is provided as input to a data acquisition (104) module, followed by a preprocessing (106) module configured to normalize and enhance the input data. The processed data is then passed to a feature extraction (108) module to derive relevant characteristics, which are analyzed using a machine learning model (110) to generate prediction results. The output (112) module provides the predicted crop disease condition along with a confidence score, thereby enabling early and reliable decision-making.
, Claims:5. CLAIMS
I/We Claim:
1. A system for multimodel early crop disease prediction, comprising:
at least one processor, and
a memory storing instructions that, when executed by the at least one processor, cause the system to:
the received data from multiple image data (102) input sources associated with crops;
the received data acquisition (104) is preprocessed (106) to generate processed data;
an extract features (108) from the processed data;
the extracted features (108) analyzed using one or more trained machine learning models (110); and
wherein the prediction of a crop disease condition generated.

2. The system as claimed in claim 1, wherein the data acquisition (104) module is configured to receive image data associated with crop conditions.

3. The system as claimed in claim 1, wherein the preprocessing (106) module is configured to normalize and enhance the received data prior to feature extraction.

4. The system as claimed in claim 1, wherein the feature extraction (108) module is configured to derive relevant features from the preprocessed data for analysis.

5. The system as claimed in claim 1, wherein the analysis module comprises one or more trained machine learning models (110) configured to analyze the extracted features and generate disease prediction.

6. The system as claimed in claim 1, wherein the output module (112) is configured to generate outputs including predicted crop disease condition and associated confidence score.

7. The system as claimed in claim 1, wherein the system combines outputs (112) from multiple data sources to improve prediction accuracy.

8. The system as claimed in claim 1, wherein the system generates a confidence score associated with the predicted crop disease condition.

6. DATE AND SIGNATURE

Dated this on 09th April, 2026
Signature

Mr. Srinivas Maddipati
(IN/PA 3124)
Agent for applicant