AUTONOMOUS ROBOTIC SYSTEM FOR REAL-TIME SOIL ANALYSIS AND SUSTAINABLE CROP RECOMMENDATION USING AI

Abstract

Autonomous Robotic System for Real-Time Soil Analysis and Sustainable Crop Recommendation Using AI. This invention describes an autonomous robotic device for real-time soil fertility prediction and sustainable crop recommendation, utilizing AI and computer vision technologies. The device autonomously navigates agricultural fields using GPS, LiDAR, and ultrasonic sensors, capturing high-resolution soil images with an adaptive camera system. A soil analysis module, equipped with machine learning algorithms, processes the images to classify soil types and predict fertility parameters, including moisture content, pH, and nutrient levels. Based on this analysis, a crop recommendation engine suggests optimal crops, considering regional agricultural data, climate conditions, and sustainable farming practices such as crop rotation. The system includes a rechargeable battery with solar charging, ensuring continuous operation, and a communication module that allows real-time monitoring and data transmission via a mobile application. This invention aims to enhance precision agriculture by providing actionable soil insights and promoting sustainable farming methods, improving crop yield, and optimizing resource usage for long-term agricultural sustainability.

Specification

Description:[0001] This invention relates to the field of computer sciences more particularly agricultural automation and precision farming, specifically the development of an autonomous robotic system designed for real-time soil fertility analysis and crop recommendation. The invention integrates advanced technologies, including machine learning, computer vision, and autonomous navigation, to optimize the assessment of soil health. It addresses key challenges in agriculture by automating soil analysis processes and delivering data-driven crop recommendations based on soil properties, climate conditions, and sustainable farming practices. The system is intended to enhance decision-making in farming, promoting sustainable agriculture through the efficient use of resources and improving crop yield while minimizing environmental impact.

PRIOR ART AND PROBLEM TO BE SOLVED

[0002] In modern agriculture, soil testing is crucial for determining soil health, fertility, and its suitability for different crops. Traditionally, this process involves manual sampling, followed by laboratory analysis to measure important parameters like nutrient levels (nitrogen, phosphorus, potassium), pH, organic matter, and texture. While effective in providing comprehensive soil data, this traditional method comes with several significant limitations, especially in terms of time, cost, and scalability. For farmers operating large-scale farms, this labor-intensive process can be particularly burdensome.

[0003] Manual soil sampling requires field visits, where samples are carefully collected, labeled, and transported to a laboratory for analysis. Once in the lab, soil samples are subjected to a series of chemical tests to determine their characteristics. This entire process can take several days to weeks, delaying crucial decision-making regarding planting, fertilization, and irrigation schedules. Additionally, the cost of these laboratory tests is often high due to the need for specialized equipment, reagents, and skilled technicians. These costs can be prohibitive for small-scale farmers, limiting their ability to regularly assess their soil health. The traditional methods also depend heavily on human expertise, which introduces the potential for human error, particularly during sample collection and interpretation.

[0004] The rise in global food demand and the need to optimize agricultural productivity have intensified the need for more efficient and accessible soil testing methods. To meet this demand, researchers and technologists have started exploring automation through machine learning (ML) and computer vision techniques. These technologies promise real-time, scalable solutions that can help farmers make more informed decisions, improve crop yields, and enhance overall soil management without the delays associated with traditional methods.

[0005] Traditional soil testing methods are time-consuming, often requiring days or even weeks for results. The process of manually collecting soil samples from the field, sending them to a laboratory, and waiting for chemical analysis does not align with the fast-paced decision-making required in modern farming. Farmers need timely data to make immediate decisions regarding crop health, irrigation, and fertilization, and traditional testing simply cannot provide this information quickly enough.

[0006] The cost associated with traditional laboratory testing is another major drawback. The equipment, reagents, and skilled labor required to perform these tests are expensive, and these costs can add up quickly, especially for large farms or for farmers who need frequent testing. Small-scale farmers, in particular, may find these costs prohibitive, making it difficult for them to access regular soil analysis. This often results in suboptimal farming practices, where important soil health issues go undetected until it is too late to address them.

[0007] Scalability is a critical issue as well. Traditional soil testing methods are not easily scalable across large areas, especially in vast agricultural fields. Farmers may only be able to collect a limited number of samples, which means that the test results might not accurately represent the variability across an entire field. This lack of detailed spatial data can lead to incorrect decisions about irrigation or fertilization, negatively impacting crop yields. Moreover, human error during sampling and analysis compromises the reliability of traditional soil tests.

[0008] In response to these challenges, several technological solutions have been proposed to automate and improve soil testing. One such approach is remote sensing combined with spectral analysis. This method uses satellite imagery or drone-based cameras to capture reflectance data from fields, which can be analyzed to estimate soil properties like moisture content, organic matter, and texture. Machine learning algorithms process the spectral data to provide insights into soil health, allowing farmers to make more informed decisions.

[0009] However, remote sensing technologies have their limitations. While they provide broad overviews of soil conditions across large areas, they often lack the resolution needed to capture fine-grained variations in soil health. Additionally, environmental factors like cloud cover or atmospheric interference can affect the accuracy of spectral readings, making it difficult to obtain reliable data under certain conditions. This limits the effectiveness of remote sensing in providing precise, real-time insights at the farm level. Portable spectroscopy devices offer another potential solution. These handheld devices allow farmers to analyze soil samples directly in the field using visible and near-infrared (NIR) light. The data from the spectrometers is processed by machine learning models to provide instant feedback on soil composition. However, these devices are still relatively expensive, and their accuracy can vary depending on soil conditions and the operator's expertise. Furthermore, while they offer quick results, their performance in heterogeneous soils-where conditions can vary significantly within short distances-can be inconsistent.

[0010] To resolve the aboeve mentioned problem here an autonomous robotic device designed to enhance precision agriculture by automating soil fertility prediction and crop recommendations. The robot autonomously navigates fields using GPS, LiDAR, and ultrasonic sensors, capturing high-resolution soil images. These images are processed by embedded machine learning models to assess soil properties such as type, texture, and fertility. Based on this analysis, a crop recommendation engine suggests the most suitable crops for the soil conditions, promoting sustainable farming practices. The system includes a mobile app for real-time data monitoring, visualization tools like heat maps, and remote control. Equipped with a rechargeable battery system powered by solar energy, the robot can operate continuously. The learning module allows it to update its models over time, ensuring accuracy and adaptability to changing agricultural conditions. This invention optimizes soil analysis, enhances crop yield, and minimizes environmental impact.

THE OBJECTIVES OF THE INVENTION:

[0011] In modern agriculture, soil testing is vital for evaluating soil health, fertility, and determining the suitability for various crops. Traditionally, this process involves manually collecting soil samples and sending them to laboratories for analysis of key parameters such as nutrient levels, pH, organic matter, and soil texture. While this approach is widely used, it has several limitations in terms of efficiency and cost, particularly for large-scale farming operations.

[0012] It has already been proposed that remote sensing technologies, such as satellite imagery and drones, combined with spectral analysis to assess soil properties. These systems capture reflectance data from large areas and use machine learning algorithms to analyze soil moisture, organic matter, and texture. By providing a broader view of soil conditions, remote sensing helps farmers make informed decisions about soil management. However, remote sensing has its limitations. While it can offer insights over large areas, it often lacks the fine resolution needed to detect small-scale variations in soil health. Additionally, environmental factors such as cloud cover or atmospheric interference can affect the quality of the data collected, reducing the accuracy of the analysis. This method, therefore, may not always provide the level of detail required for precise soil management.

[0013] The principal objective of the invention is an Autonomous Robotic Device for Real-Time Soil Fertility Prediction and Sustainable Crop Recommendation Using AI and Computer Vision, which autonomously navigates agricultural fields, captures high-resolution soil images, analyzes soil properties using machine learning algorithms, and provides real-time crop recommendations. The device integrates an image capturing module, a soil analysis module, and a crop recommendation engine, all powered by advanced AI and computer vision techniques. The system promotes sustainable farming practices by optimizing soil analysis, recommending crop rotations, and allowing farmers to make data-driven decisions in real-time via a mobile interface. It also features solar-powered autonomous operation and continuous machine learning model improvements to adapt to evolving environmental conditions.

[0014] Another objective of the invention is an image capturing module that utilizes high-resolution cameras with adaptive focus, exposure control, and a gimbal-mounted stabilization system. This module, triggered by optimal soil conditions, ensures the capture of accurate, detailed images of the soil surface under varying environmental conditions. The soil analysis module processes these images using machine learning models, such as Convolutional Neural Networks, to classify soil types, assess texture, and predict fertility based on visual indicators like color and granularity.

[0015] The further objective of the invention is a fully autonomous navigation system using GPS, LiDAR, and ultrasonic sensors to enable the robot to traverse agricultural fields. The system will incorporate predefined path planning and real-time path correction algorithms to avoid obstacles and ensure comprehensive soil coverage. This system will enable the robot to work in different field layouts while maintaining accuracy and efficiency.


[0016] The further objective of the invention is to incorporate a crop recommendation engine that analyzes the results from the soil fertility assessment and suggests the most suitable crops. The engine will utilize decision-making algorithms, factoring in regional agricultural data, climate conditions, and crop yield potential. The system will also offer recommendations for crop rotation and mixed cropping strategies to enhance long-term soil health and sustainability.

[0017] The further objective of the invention is an adaptive learning module that improves the system's performance over time by continuously updating the soil analysis and crop recommendation models through a machine learning pipeline. This module will ensure the device can account for seasonal variations and environmental changes, enhancing accuracy and relevance in predicting soil fertility and recommending crops.

SUMMARY OF THE INVENTION

[0018] Despite the exciting advancements in soil testing technologies, several challenges remain unresolved. Machine learning models, while highly effective, require vast amounts of training data to perform well. In the case of soil analysis, collecting this data is particularly difficult due to the inherent variability in soil composition across different regions and climates. Building robust models that can generalize across such diverse conditions is a complex task, and without sufficient data, the accuracy of these models can suffer . Many of the automated soil testing solutions that have been proposed focus on specific parameters like moisture content or texture but fail to provide a comprehensive analysis of soil health. For example, while remote sensing might detect surface-level variations, it does not measure deeper soil properties like microbial activity or nutrient availability, which are critical for long-term soil management. This limitation reduces the overall effectiveness of these methods for farmers who need a holistic understanding of their soil's health. The integration of multiple technologies, such as combining remote sensing with data from soil sensors or portable devices, is another challenge. While each of these technologies provides valuable insights, combining them into a cohesive system requires significant investment in both hardware and software. This complexity can make it difficult for smaller farms to adopt these solutions, as they may lack the resources or technical expertise to implement and maintain such systems. Additionally, the high upfront costs of these technologies make them less accessible to farmers with limited budgets.
[0019] So here in this invention robotic device for precision agriculture is engineered integrating machine learning, computer vision, and autonomous navigation for real-time soil fertility prediction and crop recommendations. The system consists of three main modules: an image capturing unit, a soil analysis module, and a crop recommendation engine. With an autonomous navigation system powered by GPS, LiDAR, and ultrasonic sensors, the robot traverses agricultural fields, capturing high-resolution soil images. These images are processed through machine learning algorithms trained to classify soil types and assess fertility. Based on the analysis, the crop recommendation engine suggests optimal crops, promoting sustainable agriculture. The robot features a real-time data interface accessible via a mobile app, enabling farmers to monitor soil health and adjust farming strategies. Powered by a solar-charged battery, it ensures extended operation, even in remote fields. Additionally, the learning module continuously updates its algorithms, improving accuracy over time and adapting to changing environmental conditions. This device revolutionizes on-site decision-making, enhancing crop yields while promoting sustainability.

DETAILED DESCRIPTION OF THE INVENTION

[0020] While the present invention is described herein by example, using various embodiments and illustrative drawings, those skilled in the art will recognise recognize invention is neither intended to be limited that to the embodiment of drawing or drawings described nor designed to represent the scale of the various components. Further, some features that may form a part of the invention may not be illustrated with specific figures for ease of illustration. Such omissions do not limit the embodiment outlined in any way. The drawings and detailed description are not intended to restrict the invention to the form disclosed. Still, on the contrary, the invention covers all modification/s, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the description's size or the claims. As used throughout this specification, the worn "may" be used in a permissive sense (That is, meaning having the potential) rather than the mandatory sense (That is, meaning, must).

[0021] Further, the words "an" or "a" mean "at least one" and the word "plurality" means one or more unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents and any additional subject matter not recited, and is not supposed to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents acts, materials, devices, articles and the like are included in the specification solely to provide a context for the present invention.

[0022] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is also understood that it contemplates the same component or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group comprising", "including", or "is" preceding the recitation of the element or group of elements and vice versa.

[0023] Before explaining at least one embodiment of the invention in detail, it is to be understood that the present invention is not limited in its application to the details outlined in the following description or exemplified by the examples. The invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for description and should not be regarded as limiting.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. Besides, the descriptions, materials, methods, and examples are illustrative only and not intended to be limiting. Methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention.

[0025] The present invention is an Autonomous Robotic Device for Real-Time Soil Fertility Prediction and Sustainable Crop Recommendation Using AI and Computer Vision is designed to revolutionize precision agriculture by providing farmers with accurate, real-time soil analysis and actionable crop recommendations. The device's primary purpose is to automate the traditionally labor-intensive and time-consuming process of soil assessment, enabling farmers to make data-driven decisions that enhance crop yield, conserve resources, and promote sustainable farming practices. By integrating cutting-edge AI and computer vision technologies, this robot is capable of analyzing various soil properties such as texture, moisture, and fertility without manual intervention. It offers an all-in-one solution by autonomously navigating fields, capturing soil data, and suggesting the most appropriate crops based on the soil's characteristics, ensuring long-term soil health and resource optimization.

The robot is designed for use in diverse agricultural settings, from small farms to large-scale agricultural operations. Its key advantage lies in its ability to continuously monitor soil health across entire fields, providing precise and comprehensive insights that would otherwise be challenging to gather manually. Farmers can use the system to quickly identify soil variability and nutrient deficiencies, enabling them to implement targeted farming strategies. The system also plays a pivotal role in promoting sustainable farming by suggesting crop rotation and mixed cropping practices, which help maintain soil fertility over the long term. With its autonomous operation, the robot reduces the need for human labor and enhances the efficiency of farm management, providing real-time access to soil data through an intuitive mobile interface.

Externally, the robotic device boasts a robust, weather-resistant design, engineered to operate in challenging outdoor environments. Its body is encased in a rugged metallic shell with a matte finish, providing protection against dust, water, and varying weather conditions. The device has a compact, rectangular base, which allows it to maneuver easily through narrow field rows or uneven terrain. Atop the robot, sleek solar panels are mounted to harness solar energy, ensuring that the device remains powered throughout extended field operations, even in remote areas. These solar panels blend seamlessly into the design, adding to the overall streamlined appearance of the machine.

[0026] The high-resolution camera, a key feature of the robot, is mounted on a gimbal to ensure stability during movement. This camera protrudes slightly from the top of the robot, with a flexible mount that allows it to adjust focus and exposure depending on the environmental conditions. The camera's positioning ensures that it can capture soil images at various angles and in different lighting conditions, providing optimal data for analysis. The entire robot rests on large, all-terrain wheels that provide exceptional traction and stability, enabling the device to move smoothly across different soil types, including soft, sandy, or uneven ground.

[0027] A set of LED indicators on the side of the device provides real-time feedback on its operational status, such as power levels, connectivity, and activity status, offering a visual cue to users. The robot's surface features a combination of functional panels for maintenance access, but these are discreetly integrated to maintain the aesthetic of a cohesive and professional device. The robot's overall look is utilitarian yet sophisticated, designed to instill confidence in its durability and advanced technological capabilities while ensuring it remains easy to handle and monitor by farmers and agricultural workers.

[0028] The Autonomous Robotic Device for Real-Time Soil Fertility Prediction and Sustainable Crop Recommendation Using AI and Computer Vision is composed of several interconnected and sophisticated components, each playing a critical role in ensuring the system functions seamlessly. These components work together to achieve the overall goal of soil analysis and crop recommendation, with each part designed to fulfill a distinct feature while contributing to the system's integrated operation.

[0029] At the heart of the system is the autonomous navigation module, which is responsible for guiding the robot through agricultural fields without human intervention. This module is powered by a combination of GPS, LiDAR, and ultrasonic sensors, which allow the robot to determine its location, map the terrain, and detect obstacles in real-time. The GPS system provides precise geolocation, ensuring the robot can navigate accurately over large expanses of farmland. LiDAR, which uses laser pulses to measure distance and create detailed 3D maps of the surroundings, enables the robot to avoid obstacles, such as trees or uneven terrain, and plan its path efficiently. Ultrasonic sensors complement this by providing close-range detection, helping the robot avoid smaller obstacles. These sensors work in unison to allow the robot to autonomously traverse fields, ensuring that all areas are covered for soil analysis.

[0030] The image capturing module is another critical component, tasked with obtaining high-resolution images of the soil surface. This unit is equipped with a high-definition camera that can adjust its focus and exposure based on the surrounding light and environmental conditions. The camera is mounted on a gimbal, which stabilizes the device as it moves over uneven ground, ensuring that the images captured are clear and free from distortion. This module integrates closely with the navigation system; the camera is triggered to capture images at specific intervals or under certain conditions, based on the robot's movement and the detection of optimal soil surfaces. The gimbal also allows the camera to remain level and stable during operation, ensuring that even in challenging conditions, the captured data is of high quality.

[0031] Once the images are captured, they are sent to the soil analysis module, which houses the machine learning algorithms that process the visual data. This module utilizes convolutional neural networks (CNNs) trained on large datasets of soil images. The CNNs analyze various visual characteristics of the soil, such as color, texture, and granularity, to determine soil type and fertility. The module can classify the soil into different categories, such as sandy, clay, or loamy, and also make predictions about fertility indicators like moisture levels, pH, and nutrient content. This analysis is done in real-time, allowing the robot to provide immediate feedback on the soil conditions. The integration between the image capturing module and the soil analysis module is seamless, with the latter processing the data as soon as it is collected, ensuring that no time is lost in the analysis process.

[0032] The core functionality of this module begins with the CNNs, which are highly specialized in identifying visual patterns in the images captured by the robot. These CNNs are trained to detect specific features in soil, such as color gradients, texture patterns, and granularity, which are indicative of the soil type and its fertility levels. For example, the CNN can distinguish between sandy soil, which has a distinct light color and coarse texture, and clay soil, which appears darker and finer in texture. The CNN models have been trained on an extensive library of soil images, enabling them to generalize their learning to new environments and soil types that the robot encounters in real-time.

[0033] Once the images are fed into the soil analysis module, the CNNs break down the images into layers of information. In each layer, the CNN extracts different features, progressively recognizing more complex and abstract properties of the soil. Initially, it might identify basic color and texture, but in deeper layers, it can assess more complex attributes, such as the arrangement of soil particles and the moisture content inferred from subtle visual cues. These multi-layered networks allow the CNNs to make highly accurate predictions regarding soil type and condition by analyzing every pixel in the image for useful information.

[0034] As the CNN processes the visual characteristics, it also generates predictive insights about the fertility of the soil. Fertility indicators like moisture content, organic matter presence, pH levels, and nutrient content are predicted based on the patterns found in the visual data. For instance, darker soils with specific granularity might suggest higher moisture levels, while certain color shades can indicate nutrient deficiencies or high organic matter content. The CNNs, trained on large datasets, are capable of correlating these visual characteristics with specific fertility markers, allowing the module to predict the soil's condition with impressive precision.

[0035] The real-time aspect of the soil analysis module is one of its most valuable features. The CNN models process the incoming data almost instantly after the images are captured by the robot. This real-time capability is essential for providing immediate feedback to the farmer, ensuring that no delay occurs between data collection and analysis. As soon as the image capturing module sends the visual data, the soil analysis module begins processing, ensuring that the farmer can make quick decisions based on the latest soil information. This seamless integration between the image capturing module and the soil analysis module is critical for maintaining efficiency and accuracy in the field.

[0036] Beyond the basic classification of soil types like sandy, clay, and loamy, the module also delves into predicting key fertility indicators. This is where it integrates advanced regression models alongside the CNNs. These models use the extracted features from the CNN layers to estimate quantitative measures, such as pH levels or nitrogen content. By combining both qualitative classification (e.g., soil type) and quantitative prediction (e.g., moisture levels), the soil analysis module provides a comprehensive assessment of soil health. This dual capacity ensures that the robot can assess both immediate soil needs and long-term soil health trends.

[0037] The data processing within the soil analysis module does not happen in isolation. This module is intricately linked to the crop recommendation engine, which uses the results from the soil analysis to generate appropriate crop suggestions. As soon as the CNNs complete their analysis and deliver the soil condition report, the results are passed to the crop recommendation engine. This ensures that the crop recommendations are made based on the most accurate and up-to-date data, enhancing the decision-making process for the farmer.

[0038] The continuous learning capability of the soil analysis module is another key aspect. As the robot collects more data from different fields and varying soil conditions, the machine learning models within the module are continuously updated. This adaptive learning ensures that the module improves over time, refining its ability to assess new and varied soil conditions with greater accuracy. Each soil sample analyzed contributes to the system's knowledge base, making it more adept at recognizing subtle differences in soil conditions that might influence crop recommendations or soil health management.

[0039] The crop recommendation engine is directly linked to the soil analysis module, using the results of the soil assessment to suggest suitable crops. This engine is based on decision-making algorithms that factor in a range of data, including regional agricultural practices, climate conditions, and historical crop yields. The recommendation system also takes into account the results of the soil analysis, matching specific crops to the soil's properties, such as its fertility and moisture content. This engine is designed to promote sustainable agriculture by recommending crop rotations or mixed cropping strategies, which help maintain soil health and reduce the depletion of nutrients over time. The integration of the crop recommendation engine with the soil analysis module is vital, as it ensures that recommendations are made based on the most up-to-date soil data, allowing for precise and effective decision-making.
# Inputs: Soil data (fertility, moisture, pH, nitrogen content), Climate data (temperature, rainfall), Historical crop data

# Output: Crop recommendation with threshold-based decision-making
class CropRecommendationEngine:
def __init__(self):
# Define threshold values based on agricultural knowledge and research
self.fertility_threshold = 60 # Minimum fertility score out of 100
self.moisture_threshold = 30 # Minimum moisture content in percentage
self.ph_range = (6.0, 7.5) # Ideal pH range for most crops
self.nitrogen_threshold = 10 # Minimum nitrogen content for optimal crop growth

def recommend_crop(self, soil_data, climate_data, historical_crop_data):
# Analyze soil data and compare with threshold values
if soil_data['fertility'] < self.fertility_threshold:
return "Soil too infertile for most crops, consider soil improvement"

if soil_data['moisture'] < self.moisture_threshold:
return "Moisture content too low for most crops, consider irrigation"

if not (self.ph_range[0] <= soil_data['pH'] <= self.ph_range[1]):
return "pH level unsuitable, recommend pH adjustment with lime or sulfur"

if soil_data['nitrogen'] < self.nitrogen_threshold:
return "Nitrogen deficiency detected, recommend nitrogen fertilizers"

# If soil conditions are good, recommend crops based on historical data and climate
suitable_crops = []
for crop in historical_crop_data:
if (climate_data['temperature_range'][0] <= crop['ideal_temperature'] <= climate_data['temperature_range'][1]
and climate_data['rainfall'] >= crop['rainfall_requirements']
and soil_data['fertility'] >= crop['min_fertility']):
suitable_crops.append(crop['name'])

# If no crops match, suggest mixed cropping or rotation based on sustainability
if not suitable_crops:
return "No ideal crops found, consider mixed cropping or crop rotation for soil health"

# Return the best-matching crop or list of crops
return f"Recommended crops based on soil and climate data: {', '.join(suitable_crops)}"

# Example usage
soil_data = {'fertility': 65, 'moisture': 35, 'pH': 6.5, 'nitrogen': 12}
climate_data = {'temperature_range': (20, 30), 'rainfall': 500}
historical_crop_data = [
{'name': 'Wheat', 'ideal_temperature': 25, 'rainfall_requirements': 450, 'min_fertility': 50},
{'name': 'Rice', 'ideal_temperature': 27, 'rainfall_requirements': 600, 'min_fertility': 60}
]

engine = CropRecommendationEngine()
recommendation = engine.recommend_crop(soil_data, climate_data, historical_crop_data)
print(recommendation)

[0040] The process for the crop recommendation engine starts by assessing soil properties such as fertility, moisture content, pH, and nitrogen levels. These values are compared against pre-defined threshold values, which are derived from agricultural research and regional farming knowledge. The thresholds help ensure that the soil is suitable for planting crops. For example, the fertility threshold of 60 means that soils with a fertility score below this value are too poor to support most crops, and thus soil amendments (like compost or fertilizers) might be needed.

[0041] If the soil analysis passes these thresholds, the algorithm proceeds to analyze historical crop data and climate conditions. It evaluates whether the current climate, including temperature and rainfall, matches the ideal conditions for crops in the historical dataset. The algorithm also considers minimum soil fertility requirements for each crop. Based on this data, it selects crops that are most likely to thrive under the given conditions. The algorithm is designed with flexibility in mind. If no crops are immediately suitable due to soil or climate constraints, it suggests alternative strategies like mixed cropping or crop rotation. This is key to sustainable agriculture because it helps maintain soil health over time, ensuring that nutrients are not depleted by continuous monocropping.

[0042] The fertility threshold of 60 out of 100 is based on agricultural research indicating that crops generally require moderately fertile soil to grow well. Soils that fall below this threshold often lack essential nutrients, which can lead to suboptimal crop growth or complete crop failure. In such cases, soil amendments, such as compost or fertilizers, may be necessary to improve the nutrient content and bring the fertility up to a level that supports healthy crop development. This threshold ensures that the crops recommended by the system are likely to thrive without significant interventions, but also alerts farmers when additional soil management is required.

[0043] The moisture threshold of 30% ensures that the soil contains adequate moisture to support crop growth. Moisture is crucial for the transportation of nutrients within the soil and into the plants. In regions where drought or inconsistent rainfall is a concern, maintaining soil moisture becomes especially important. If the moisture level falls below this threshold, the system may recommend the use of irrigation techniques or water retention strategies such as mulching. By ensuring the soil has enough moisture, the algorithm helps prevent water stress, which can severely reduce crop yields.

[0044] The pH range of 6.0 to 7.5 is chosen because most crops thrive in soils that are neutral to slightly acidic. Soils outside this range can hinder a plant's ability to absorb key nutrients, leading to poor growth and reduced crop quality. If the soil's pH level is either too low (acidic) or too high (alkaline), the system might recommend corrective measures such as adding lime to increase pH or sulfur to lower it. By keeping the soil's pH within this ideal range, farmers can ensure optimal nutrient availability for their crops.

[0045] The nitrogen threshold of 10 ensures that there is an adequate amount of nitrogen in the soil, which is a critical nutrient for plant growth. Nitrogen plays a fundamental role in photosynthesis and the development of healthy foliage. If the nitrogen level in the soil falls below this threshold, crop growth can be stunted, and yields will likely be lower. The system uses this threshold to alert farmers when nitrogen levels are insufficient, recommending the use of nitrogen-rich fertilizers to restore balance. By monitoring and maintaining appropriate nitrogen levels, the system helps ensure robust crop health and higher yields.

[0046] The robot's power system is based on a rechargeable battery that is supplemented by solar panels. The solar panels are designed to continuously charge the battery during daylight hours, extending the robot's operational time, especially in remote fields where access to conventional power sources is limited. The power system is integrated with the navigation and operational modules to ensure energy efficiency. When the battery level drops below a certain threshold, the robot autonomously navigates back to a designated charging station, ensuring continuous operation without human intervention. This self-sustaining power system ensures that the robot can perform its tasks over extended periods without requiring frequent recharging.

[0047] A wireless communication system allows the robot to transmit real-time data to a cloud-based platform or directly to a farmer's mobile device. This system includes both Wi-Fi and LTE capabilities, ensuring that the robot can send data even in areas with limited connectivity. The real-time transmission of data allows farmers to monitor soil conditions and crop recommendations remotely, providing a level of convenience and immediacy that traditional methods cannot offer. The communication system also facilitates updates to the robot's machine learning models, as new data can be transmitted to improve the accuracy of the soil analysis and crop recommendation algorithms over time.

[0048] Finally, the data storage module records all the data collected and analyzed by the robot, allowing for long-term tracking of soil conditions and fertility trends. This historical data is invaluable for farmers, as it enables them to make informed decisions based on patterns observed over multiple growing seasons. The data storage module interacts with the communication system to back up information to a cloud platform, ensuring that even if the robot is not immediately accessible, the data can still be retrieved.

[0049] Each of these components works in harmony to fulfill the robot's primary objective: to provide real-time, data-driven soil analysis and crop recommendations that enhance farming efficiency and sustainability. The interaction between the navigation, image capturing, soil analysis, and crop recommendation modules ensures a continuous flow of information, allowing the robot to operate autonomously while delivering actionable insights to farmers. The power and communication systems ensure that the robot remains operational and connected, even in remote areas, making it a reliable tool for modern agriculture.

[0050] The Autonomous Robotic Device for Real-Time Soil Fertility Prediction and Sustainable Crop Recommendation Using AI and Computer Vision operates as a fully autonomous system designed to optimize soil analysis and provide farmers with precise crop recommendations. The robot begins its process by navigating the agricultural field using its advanced autonomous navigation system, which is equipped with GPS, LiDAR, and ultrasonic sensors. These sensors allow the robot to map the field, detect obstacles, and efficiently cover the designated area without human intervention. The navigation system works in tandem with real-time path planning algorithms, ensuring that the robot can move smoothly across various types of terrain, from flat fields to more complex landscapes.

[0051] As the robot moves through the field, its high-resolution image capturing module, mounted on a stabilized gimbal, captures detailed images of the soil. This camera is fine-tuned to adjust its focus and exposure based on environmental conditions, ensuring that the captured images are clear and accurate even in varying lighting or on uneven terrain. Once the images are collected, they are sent to the soil analysis module, where machine learning algorithms, specifically convolutional neural networks (CNNs), process the visual data in real time. The CNNs analyze the visual features of the soil-such as color, texture, and granularity-identifying key soil characteristics like type, moisture content, and fertility.

[0052] The data from the soil analysis is then fed into the crop recommendation engine. This engine uses decision-making algorithms that factor in not only the results of the soil analysis but also regional agricultural practices, climate conditions, and historical crop data. Based on this analysis, the system suggests crops that are best suited to the current soil conditions. The crop recommendation engine is designed to promote sustainable agriculture, offering suggestions for crop rotation or mixed cropping strategies to maintain soil health over time. Farmers can access all the real-time data and crop recommendations through a mobile application, which also features tools for visualizing soil variability across the field.
[0053] In terms of energy, the robot is powered by a rechargeable battery that is supplemented by solar panels. This allows for continuous operation, especially in remote areas where power sources may be scarce. When the battery runs low, the robot autonomously returns to its charging station, ensuring minimal downtime and maximizing operational efficiency.

[0054] To illustrate the operation of this device, consider a case study where the Autonomous Robotic Device is deployed in a marshy land for farming purposes. Marshy soils are typically waterlogged and have a high moisture content, which presents unique challenges for farming. When the robot is set up in this environment, it begins by navigating the marshy terrain. The navigation system, using its LiDAR and ultrasonic sensors, detects the soft, uneven ground and adjusts the robot's movement accordingly, avoiding areas that are too saturated or dangerous to cross. The GPS and path planning algorithms ensure that the robot covers the entire marshland efficiently, scanning areas that may be suitable for planting.

[0055] As the robot traverses the marshy field, its image capturing module takes detailed photos of the soil surface, despite the reflective nature of the wet ground. These images are then processed by the soil analysis module. The CNNs identify the high moisture content characteristic of marshy soils, along with other visual features such as the soil's texture and organic matter content. Based on these observations, the soil analysis module may determine that the fertility of the marshy land is low due to the excess water, which could lead to poor oxygenation and limited nutrient availability.

[0056] The crop recommendation engine receives the soil analysis results and compares them with historical data and climate conditions. In this case, the engine would recognize that crops that thrive in waterlogged or high-moisture environments, such as rice or water spinach, would be suitable for cultivation. The system might also recommend a soil management strategy to improve fertility, such as introducing raised beds or drainage systems to reduce waterlogging. Additionally, the recommendation engine might suggest a rotation plan, where crops like legumes are planted in alternating seasons to replenish the soil's nitrogen levels and improve overall soil structure.

[0057] Throughout the operation, the farmer can monitor the robot's progress and access the real-time soil analysis through the mobile app. The app would show visualizations of the soil's moisture distribution, helping the farmer understand which parts of the marshy field are more viable for planting. Based on the robot's recommendations, the farmer can implement the necessary changes to the soil or follow the crop suggestions, ensuring that even in challenging environments like marshy lands, farming is optimized for sustainable, high-yield practices.

[0058] While there has been illustrated and described embodiments of the present invention, those of ordinary skill in the art, to be understood that various changes may be made to these embodiments without departing from the principles and spirit of the present invention, modifications, substitutions and modifications, the scope of the invention being indicated by the appended claims and their equivalents.

FIGURE DESCRIPTION

[0059] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate an exemplary embodiment and explain the disclosed embodiment together with the description. The left and rightmost digit(s) of a reference number identifies the figure in which the reference number first appears in the figures. The same numbers are used throughout the figures to reference like features and components. Some embodiments of the System and methods of an embodiment of the present subject matter are now described, by way of example only, and concerning the accompanying figures, in which:

[0060] Figure - 1 illustrates the flwo chart showing the working . , Claims:1. An autonomous robotic device for real-time soil fertility prediction and sustainable crop recommendation, comprising:
a navigation module configured to autonomously traverse agricultural fields, the navigation module including GPS, LiDAR, and ultrasonic sensors for path planning, obstacle detection, and real-time terrain mapping;
an image capturing module equipped with a high-resolution camera mounted on a gimbal for capturing stabilized soil images, the camera configured with adaptive focus and exposure settings to account for environmental conditions such as lighting, soil texture, and moisture;
a soil analysis module operatively connected to the image capturing module, wherein the soil analysis module comprises machine learning algorithms, including convolutional neural networks (CNNs), trained on soil image datasets to analyze visual characteristics of soil, including color, texture, and granularity, for classifying soil types and predicting soil fertility parameters, such as moisture content, pH levels, and nutrient content;
a crop recommendation engine in communication with the soil analysis module, wherein the crop recommendation engine utilizes decision-making algorithms that incorporate soil analysis results, regional agricultural data, climate conditions, and historical crop yield information to recommend suitable crops for planting, the engine configured to suggest sustainable practices such as crop rotation and mixed cropping strategies based on soil conditions;
a power module comprising a rechargeable battery with solar charging capability, enabling continuous field operation, the device configured to autonomously return to a designated charging station when battery levels fall below a predefined threshold;
a communication module configured for wireless transmission of real-time data, wherein the communication module provides data accessibility through a mobile or web-based application for monitoring soil analysis results and crop recommendations, comprising visualization tools for field soil variability.
2. The autonomous robotic device as claimed in claim 1, wherein the navigation module is configured with a predefined path planning algorithm based on field layout data, and includes a real-time path correction system for navigating uneven terrain and avoiding obstacles autonomously.
3. The autonomous robotic device as claimed in claim 1, wherein the image capturing module includes a sensor for detecting optimal conditions for image capture, wherein the sensor is configured to trigger the camera based on soil surface characteristics and environmental factors such as moisture levels and ambient light.
4. The autonomous robotic device as claimed in claim 1, wherein the machine learning algorithms in the soil analysis module include regression models trained to predict quantitative soil properties, including nitrogen levels, organic matter content, and electrical conductivity, based on image-derived data.
5. The autonomous robotic device as claimed in claim 1, wherein the crop recommendation engine is configured to dynamically update its crop recommendations by incorporating real-time weather data and seasonal changes, adjusting crop suggestions based on predicted climate variations over the planting cycle.
6. The autonomous robotic device as claimed in claim 1, wherein the power module is configured with energy optimization protocols, enabling the device to minimize power consumption during idle periods, and wherein the solar charging system is operatively connected to ensure sustained operation in remote locations without external power sources.
7. The autonomous robotic device as claimed in claim 1, wherein the communication module is configured to store historical soil analysis data on a cloud-based platform, allowing for long-term tracking of soil health, and wherein the platform provides advanced data analytics and reporting functionalities accessible through a user interface.
8. The autonomous robotic device as claimed in claim 1, wherein the crop recommendation engine is configured to provide sustainability metrics, including soil nutrient depletion rates and projected crop yield, to support long-term agricultural planning and resource conservation efforts.
9. The autonomous robotic device as claimed in claim 1, wherein the soil analysis module is operatively configured to continuously improve its machine learning algorithms through an adaptive learning pipeline, wherein newly acquired soil data is used to retrain the models and enhance the accuracy of future soil assessments.
10. The autonomous robotic device as claimed in claim 1, wherein the device is equipped with a failure detection and diagnostic module that monitors system performance, identifying malfunctions in the navigation, image capturing, or soil analysis modules, and transmits alert notifications to the user through the communication module for timely intervention.

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