Description:FIELD OF THE INVENTION
This invention relates to soil health monitoring for sustainable agriculture, recent inventions aim to modernize and automate how soil conditions are assessed. The core invention you're referring to combines IoT (Internet of Things) sensors with computer vision technologies to create a real-time, intelligent soil monitoring system.
BACKGROUND OF THE INVENTION
The invention addresses the critical challenge of monitoring and predicting soil health which is a key factor in sustainable agriculture. The traditional soil analysis methods are often labor-intensive, limited in scope and time-consuming. This invention uses compute vison technology and Internet of Things (IoT) sensors for providing automated, real-time and accurate soil health monitoring. The IoT and computer vision-based system enables the proactive soil management, reduce resource wastage, maintain soil fertility and help farmers in optimization of crop yields for long-term agricultural sustainability by integrating soil parameters such as moisture, nutrient levels, pH and nutrient levels with advanced image processing techniques.
US11215601B2 System for monitoring soil conditions based on acoustic data discloses the system or monitoring soil conditions within an agricultural field may include a furrow forming tool. The system may also include an acoustic sensor configured to detect a sound associated with movement of the furrow forming tool through the soil. Furthermore, the system may include a controller communicatively coupled to the acoustic sensor. The controller may be configured to monitor a soil condition associated with soil within the field based on acoustic data received from the acoustic sensor.
US11026376B2 Customized land surface modeling in a soil-crop system using satellite data discloses an irrigation modeling framework in precision agriculture utilizes a combination of weather data, crop data, and other agricultural inputs to create customized agronomic models for diagnosing and predicting a moisture state in a field, and a corresponding need for, and timing of, irrigation activities. Specific combinations of various agricultural inputs can be applied, together with weather information to identify or adjust water-related characteristics of crops and soils, to model optimal irrigation activities and provide advisories, recommendations, and scheduling guidance for targeted application of artificial precipitation to address specific moisture conditions in a soil system of a field.
With the advancement of smart technologies, particularly the Internet of Things (IoT) and computer vision, there has been a shift toward the use of automated systems for monitoring agricultural parameters. IoT-based sensors are now capable of capturing real-time data on soil moisture, temperature, pH, and nutrient levels. However, current systems still fall short in integrating sensor data with visual information, which can provide critical insights into surface-level changes like erosion, nutrient deficiency symptoms in crops, or irregular plant growth patterns.
Moreover, the lack of integration between sensory data and image-based analytics, and the absence of intelligent machine learning models to make predictive inferences, create a significant research and application gap. Most existing systems are either too simplistic or do not function effectively in remote, off-grid agricultural areas due to power and connectivity constraints.
There exists a need for a comprehensive, automated, and scalable system that combines IoT sensors, edge-based image capturing devices, and machine learning analytics, while also being sustainable and suitable for remote deployment. The present invention addresses this gap by providing a multimodal, real-time soil health monitoring and prediction system that empowers farmers with accurate insights and actionable intelligence for data-driven decision-making in agriculture.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
Soil health monitoring and prediction system is an advanced technological system which uses multiple smart devices for collecting processing and analysing the data for real-time monitoring of soil health. The system uses a combination of IoT-based smart numeric sensory data collection devices (SNSDCD) which are n in number and edge vision devices (EVD) which are p in number and hence provides an automated and comprehensive approach to assess the quality of soil, agricultural practices optimization and sustainability promotion. Also, n is greater-greater than p. This system empowers land managers and farmers by using vision-based devices and sensors by providing real-time and accurate insights about the condition of their soil by enabling data-driven decisions for improved land management and crop production
At the core of the system there are Smart Numeric Sensory Data Collector Devices (SNSDCD) with several essential sensors which are designed to measure key parameters of the soil. These sensors include NPK sensor which helps in measuring the level of nitrogen, phosphorus and potassium of the soil, then there are temperature and humidity sensors which helps in monitoring the surrounding environment, there is also soil conductivity sensors which measure salinity of the soi and also tells the overall soil health, soil moisture sensors are used to determine the moisture levels, pH sensors are used to assess the alkalinity and acidity of the soil. These sensors are interconnected and linked to the computing unit that processes the collected data. The computing unit works as a controlling hub which processes the data collected by the means of the sensors and also make it available for real-time visualization through a display screen. Additionally, the computing unite features a Wi-Fi module, allowing transmission of the data collected through the sensors to the cloud for further analysis. A real-time clock is used to accurately timestamp the collected data. An SD card is also used which stores the data at local level for ensuring that the data can be accessed even if the internet is not working temporarily. The system operates autonomously and is powered by a solar panel and battery, which makes the devices suitable for use in the remote areas where there is lack of traditional electric infrastructure.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: OVERALL ARCHITECTURE
FIGURE 2: SMART NUMERIC SENSORY DATA COLLECTOR
FIGURE 3: EDGE VISION DEVICES
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein 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 scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Soil health monitoring and prediction system is an advanced technological system which uses multiple smart devices for collecting processing and analysing the data for real-time monitoring of soil health. The system uses a combination of IoT-based smart numeric sensory data collection devices (SNSDCD) which are n in number and edge vision devices (EVD) which are p in number and hence provides an automated and comprehensive approach to assess the quality of soil, agricultural practices optimization and sustainability promotion. Also, n is greater-greater than p. This system empowers land managers and farmers by using vision-based devices and sensors by providing real-time and accurate insights about the condition of their soil by enabling data-driven decisions for improved land management and crop production
At the core of the system there are Smart Numeric Sensory Data Collector Devices (SNSDCD) with several essential sensors which are designed to measure key parameters of the soil. These sensors include NPK sensor which helps in measuring the level of nitrogen, phosphorus and potassium of the soil, then there are temperature and humidity sensors which helps in monitoring the surrounding environment, there is also soil conductivity sensors which measure salinity of the soi and also tells the overall soil health, soil moisture sensors are used to determine the moisture levels, pH sensors are used to assess the alkalinity and acidity of the soil. These sensors are interconnected and linked to the computing unit that processes the collected data. The computing unit works as a controlling hub which processes the data collected by the means of the sensors and also make it available for real-time visualization through a display screen. Additionally, the computing unite features a Wi-Fi module, allowing transmission of the data collected through the sensors to the cloud for further analysis. A real-time clock is used to accurately timestamp the collected data. An SD card is also used which stores the data at local level for ensuring that the data can be accessed even if the internet is not working temporarily. The system operates autonomously and is powered by a solar panel and battery, which makes the devices suitable for use in the remote areas where there is lack of traditional electric infrastructure.
In addition to the SNSDCD there are also Edge Vision Devices (EVD) which are occupied with Raspberry Pi computers, HD cameras and data storage capabilities. The central processor is Raspberry Pi for the EVDs, which captures the high-quality images of the crops and the soil. These images are important for assessing the soil condition visually and also for identifying the issues like nutrient deficiency, erosion or irregular growth patterns. The data collected by these EVDs is stored on the Raspberry Pi and transmitted to the cloud server by the mean of an onboard Wi-Fi modem. Multimodal dataset is created with this integration of visual data with sensor data which offers a more complete understanding of the soil health by combining qualitative visual insights with quantitative sensor data.
Once the data is reached to the cloud server, it is stored and pre-processed for further analysis. The cloud server is central hub of the system which manages these large volumes of the data generated by the devices. The main data collected soil and environmental parameters as well as the visual information which can be used to gain insights into soil health trends over time. This data is used to train the multimodal machine learning model then. By learning from the existing datasets, the model now can-do pattern identification and correlations between the conditions of the soil and crop health. Once trained, the machine learning model can easily predict the future of soil health based on new data received from the field.
When the model is ready to use then it is applied to the new data which is gathered by the system. The ML model processes the data coming from the SNSDCDs and EVDs., analyzing factors such as soil nutrient levels, visual cues and nutrient levels to predict soil health. These predictions can help the farmers in making informed decisions about fertilisation, irrigation and other soil health management strategies. For example, the system can predict whether the soil needs extra nutrition, additional water or whether the specific crops would survive in the current condition. Such prediction enables proactive soil management, optimizes resources usage and reduces the risk of crop failure.
The system is designed such as it is adaptable and scalable to the various agricultural settings. It is well suitable for the large-scale commercial farms or smaller, more localised operations. The combination of the image-based analysis and the sensor-based monitoring enables more precise management of soil health which ensures the farmers receive information timely for maintaining necessary conditions for the growth of plant. This technology aims to improve efficiency of the resources, reducing the labour costs and ultimately enhance the crop yields.
Furthermore, the system uses the solar energy to operate which also ensures that it operates sustainably, which makes it particularly beneficial for the regions which are having limited access to the electricity. By combining the visual insights with the sensor data farmers are with a comprehensive tool for soil health management. This system provides a powerful solution for improving the agricultural productivity while also promotes sustainable farming practices. Through its use of IoT sensors, edge vision devices and machine learning models this system of soil health monitoring shows the future of precision agriculture by enabling smarter and more efficient decisions of farming.
There are four main steps involved in the algorithm for soil health prediction. First step is data collection by gathering inputs from the virous sensor used i.e. NPK sensor, pH sensor, temperature & humidity sensor, soil conductivity sensor, GPS sensor and an HD camera attached to the raspberry pi. Second step is preprocessing in which normalization of the sensor data is done and also preparation of images for analysis is made. Third step is featured extraction in which identification of the key characteristics is made from both the sensor data i.e. nutrient levels, humidity & temperature etc. and images data like soil texture, surface anomalies. In fourth step, finally a Machine learning model is trained by using the combined features for predicting soil health which offs real-time insights for precision agriculture.
The novelty lies in the synergistic use of IoT and computer vision for real-time, automated, and predictive soil health monitoring, creating a system that goes beyond the capabilities of existing soil analysis tools.
BEST METHOD OF WORKING:
An integrated soil health monitoring and prediction system is disclosed, comprising a plurality of Smart Numeric Sensory Data Collector Devices (SNSDCDs), each equipped with at least one sensor selected from among an NPK sensor, a temperature sensor, a humidity sensor, a soil conductivity sensor, a soil moisture sensor, a pH sensor, and a GPS sensor. Each SNSDCD further includes a computing unit configured with a display, an SD card for local storage, a Wi-Fi communication module for cloud connectivity, and a real-time clock. The system is autonomously powered by a solar panel and a rechargeable battery to support deployment in remote or off-grid environments.
In addition, the system comprises at least one Edge Vision Device (EVD), incorporating a Raspberry Pi microcomputer, an HD camera for visual capture, onboard data storage, and a Wi-Fi module, also powered by solar energy. The data collected from both SNSDCDs and EVDs is transmitted to a cloud server where it undergoes preprocessing and feature extraction. A machine learning model processes this multimodal data for the purpose of predicting soil health conditions. The results are then made accessible to users in real time via an interactive display interface.
The computing unit of the SNSDCD is further configured to normalize and preprocess sensor data locally before uploading it to the cloud server, thus improving transmission efficiency and responsiveness. The Edge Vision Device captures high-resolution images of soil and crops to facilitate visual analysis for detecting nutrient deficiencies, soil erosion, or growth irregularities. The machine learning model hosted on the cloud server is trained on both sensor-based numeric data and visual features, enabling more accurate soil health predictions. Image features are extracted using a pretrained convolutional neural network model, such as ResNet50, which enhances the system’s ability to analyze complex visual inputs.
The cloud server also includes a decision support engine that provides actionable recommendations such as optimal irrigation, fertilization, or crop selection based on predicted soil conditions. Furthermore, the SD card integrated within the SNSDCD ensures that data is stored locally during periods of limited or no network access, while the real-time clock module ensures accurate timestamping of all data collected. The system’s prediction capabilities include assessments of nutrient levels, moisture content, and pH balance, aiding in the formulation of effective agricultural interventions. The entire system is designed to be scalable and adaptable, making it suitable for deployment in diverse agricultural environments ranging from smallholder farms to large-scale commercial operations, by allowing modular configuration of SNSDCD and EVD units according to specific field requirements.
ADVANTAGES OF THE INVENTION:
1. The system helps the farmers by providing real time data which helps the farmers in making timely decisions about fertilization, irrigation and other soil management practices.
2. Old traditional methods of soil testing are time-consuming and labor-intensive. This invention helps in automation of soil analysis process by providing continuous monitoring and reducing the need for manual testing which reduces operational cost and improves efficiency.
3. The system helps in optimization of the use of fertilizers, water, and other resources by providing accurate and precise soil health data.
4. As the system is solar powered, it is highly sustainable and also suitable for the remote locations where there is no access to electrical grids.
5. It reduces the need for the manual labor and traditional soil testing methods, hence also saves the costs of farmers for equipment, soil analysis and labor.
6. With the help of machine learning model farmers also can optimize their soil conditions which leads to maximize crop growth.
, Claims:1. An integrated soil health monitoring and prediction system comprising:
a plurality of Smart Numeric Sensory Data Collector Devices (SNSDCDs), each equipped with at least one sensor selected from the group consisting of an NPK sensor, a temperature sensor, a humidity sensor, a soil conductivity sensor, a soil moisture sensor, a pH sensor, and a GPS sensor,
each SNSDCD further comprising a computing unit with a display, SD card for local storage, Wi-Fi module for cloud communication, real-time clock, and powered by solar panel and rechargeable battery;
at least one Edge Vision Device (EVD), comprising a Raspberry Pi microcomputer, HD camera, data storage, and Wi-Fi communication module, also solar-powered,
wherein data collected from the SNSDCDs and EVDs is transmitted to a cloud server for preprocessing, feature extraction, and machine learning-based prediction of soil health,
and wherein the processed results are accessible to users in real time via a display interface.
2. The system as claimed in claim 1, wherein the SNSDCD computing unit is configured to normalize and preprocess the sensory data locally before transmission to the cloud server.
3. The system as claimed in claim 1, wherein the EVD captures high-resolution soil and crop images for visual assessment of soil condition, nutrient deficiency, erosion, or growth anomalies.
4. The system as claimed in claim 1, wherein the machine learning model deployed on the cloud server is trained using multimodal data comprising both numeric sensor readings and visual image features.
5. The system as claimed in claim 1, wherein image features are extracted using a pretrained convolutional neural network model such as ResNet50.
6. The system as claimed in claim 1, wherein the cloud server further comprises a real-time decision engine for recommending actions such as irrigation, fertilization, or crop selection based on the predicted soil health.
7. The system as claimed in claim 1, wherein the SD card in the SNSDCD stores sensor data locally to ensure data retention during periods of network unavailability.
8. The system as claimed in claim 1, wherein the real-time clock module timestamps all collected data for accurate temporal correlation during analysis.
9. The system as claimed in claim 1, wherein the prediction of soil health includes assessing nutrient levels, moisture adequacy, and pH balance to guide agricultural interventions.
10. The system as claimed in claim 1, wherein the system is scalable and adaptable to different agricultural environments, including smallholder and large-scale farms, through modular deployment of SNSDCD and EVD units.