AI-ASSISTED ROBOTIC SYSTEM FOR PRECISION AGRICULTURE AND AUTOMATED CROP MANAGEMENT

Abstract

AI-ASSISTED ROBOTIC SYSTEM FOR PRECISION AGRICULTURE AND AUTOMATED CROP MANAGEMENT The present invention relates to the use of agricultural robotics systems that rely on vision, where RGB cameras are the most popular sensor. Furthermore, it clarified that AI can yield positive results and that different AI techniques each have unique advantages for resolving specific agronomic problems rather than a single algorithm that outperforms all others. By using AI-assisted smart systems to monitor crops, agricultural land, and the factors influencing crop health, modern farming techniques could boost crop productivity. Precision agriculture uses deep learning (DL)-based systems and convolutional neural network (CNN) models to detect pest-infested plants and treat them before they spread disease. Light, temperature, humidity, precipitation, and fertilizer concentration are just a few of the related parameters that AI devices collect input data on. Along with challenges like scalability and data privacy concerns, the assessment examines prospective advancements and prospects in the field. This will assist researchers and practitioners in making well-informed choices regarding farming practices that are technologically sound, sustainable, and productive.

Specification

Description:AI-ASSISTED ROBOTIC SYSTEM FOR PRECISION AGRICULTURE AND AUTOMATED CROP MANAGEMENT

Technical Field
[0001] The embodiments herein generally relate to a method for AI-assisted robotic system for precision agriculture and automated crop management.
Description of the Related Art
[0002] The agricultural robots have drawn a lot of attention and are widely regarded as one of the most promising solutions for a more productive and sustainable agriculture industry. Agricultural robots, however, are intricate systems made up of a number of components, including wheels, grippers, manipulators, navigational aids, and perception devices. Furthermore, agricultural robots must possess the intelligence to carry out intricate tasks like navigating between rows, identifying objects of interest, and dodging field obstacles.
[0003] Precision agriculture is a data-driven approach that analyzes and manages field variability using a variety of tools, including sensors, drones, GPS guidance systems, and machine learning algorithms. In order to make well-informed decisions specific to particular areas of their farm, farmers can use this strategy to evaluate changes in crop health, soil composition, and moisture levels. By effectively using farm inputs like fertilizer, pesticides, and water through real-time data collection and analysis, precision agriculture seeks to increase food production worldwide while enhancing efficiency, yield, and environmental sustainability. In recent years, advances in artificial intelligence (AI) and vision systems have improved robot perception. Bump sensors, soil sensors, sonar systems, and RGB cameras are just a few of the sensing devices that have been tested and are currently being used for this particular task. Each of these devices has advantages and disadvantages that make it appropriate for a particular agricultural task. The same is true of the wide variety of AI algorithms in use, which range from extremely intricate and computationally demanding to simpler and quicker to implement. The remarkable development of machine learning (ML) and artificial intelligence (AI) vision in agriculture has accelerated progress in many agricultural areas. Machine learning techniques were first used for crop monitoring and production prediction, but they have since expanded to include a range of agricultural applications. Large datasets are used by the applications for decision-making and predictive analytics.
[0004] A vast array of agricultural tasks, including weeding, crop monitoring, phenotyping, disease detection, spraying, navigation, and harvesting, are handled by scientific agricultural robotic systems. Since technical information about sensors and algorithms is typically kept private, commercial systems are not covered. Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and conventional machine learning methods like linear regression are among the AI algorithms used to exploit the vision-based sensors covered by the review, which include RGB, IR, and spectral cameras as well as stereo vision devices. All-terrain vehicles (ATVs) are essential to the precision farming revolution because they are responsive and adaptable platforms for integrating new technology into farming operations. The vehicles' robust navigation systems, sensors, and automation features enable them to traverse difficult terrain and carry out precise, location-specific tasks. By making it easier to use a variety of agricultural tools, including seeders, sprayers, and sensors, ATVs enable farmers to complete specific tasks more quickly while reducing crop damage and soil compaction. Despite advancements, precision agriculture still faces obstacles and limitations.

SUMMARY
[0001] In view of the foregoing, an embodiment herein provides a method for AI-assisted robotic system for precision agriculture and automated crop management. In some embodiments, wherein the predetermined eligibility requirements were established, including the following, the references must be relevant to and specifically designed for agricultural robotic systems, and the suggested solutions must have been implemented and evaluated in the field. However, there is no restriction on the type of environment for which the solutions are designed, such as an orchard or greenhouse. Numerous scientific publications fall under the category of science and are stored in a variety of databases, including Web of Science (WoS), PubMed, Scopus, and Google Scholar. Because of their numerous peer-reviewed research publications and citations in this field in numerous journals worldwide, the authors of this review chose to use the Scopus database among them.
[0002] In some embodiments, whereas MIoU is utilized for instance segmentation, mAP is utilized for object detection. At other times, certain operations, like harvesting or phenotyping, will require numerical continuous outputs. The Root Mean Squared Error (RMSE), the Coefficient of Variation (CV), the Mapping in Absolute Errors (MAE), and the coefficient of determination (R2) will all be utilized in this situation. Finally, highly specific application-oriented performance metrics that are difficult to extrapolate to other related works may be reported in other papers. The keyword "precision agriculture" is highly connected in the network, indicating that researchers and stakeholders are becoming more interested in using precision agriculture to automate farms. However, the keyword "ATV" was found to have a poor correlation with other keywords, which raises questions about whether AI-ML models can be successfully implemented in these vehicles to carry out on-field operations efficiently.
[0005] In some embodiments, wherein one of the most tiresome and time-consuming agricultural chores is weeding. Weeding can be done mechanically with blades, fire, etc., or by spraying chemicals on the plants and weeds. Since pesticides are selective enough to harm only weeds and not the crop, even if the crop is covered by them, uniform chemical weeding applications were and remain the most widely used method of controlling weeds. However, spot spraying-where chemicals are applied only to specific areas of the fields-and mechanical weeding-where no chemicals are applied-have become more popular in recent years as a result of ongoing pressure to move toward more sustainable agriculture. The main causes might be the difficulty of comprehending vehicle dynamics, the incompatibility of the developed model in terms of cutting down on computation time, and the implementation of appropriate AI models for ground navigation at different slopes. To increase model accuracy and lower computational costs, multiple models with adaptive boosting algorithms must be implemented. Accessibility and cost issues posed by advanced technology may prevent small-scale farmers from implementing it. A major turning point in contemporary farming has been reached with the introduction of AI-controlled robotics in smart agriculture systems, which have transformed farming methods by increasing productivity, sustainability, and precision.
[0003] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
BRIEF DESCRIPTION OF THE DRAWINGS
[0001] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0002] FIG. 1 illustrates a method for AI-assisted robotic system for precision agriculture and automated crop management according to an embodiment herein.
[0003] FIG. 2 illustrates a method for application of ML and AI vision in ATV according to an embodiment herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0001] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0002] FIG. 1 illustrates a method for AI-assisted robotic system for precision agriculture and automated crop management according to an embodiment herein. In some embodiments, the automation of both mechanical weeding and spot spraying was the answer to their becoming commonplace. Applications of spot spraying are simple, but mechanical weeding is more complicated because there are two types of it: inter-row weeding, which removes weeds only between crop rows, and intra-row weeding, which targets the space between crops. The latter is more difficult because it needs to identify the crop to avoid damaging it. A branch of artificial intelligence called machine learning is focused on developing models and techniques that let computers learn from data and get better without the need for explicit programming. Creating mathematical models to analyze data patterns in order to make predictions or decisions is known as machine learning. Through iterative analysis of historical data, these models find trends and improve their accuracy over time.
[0003] In some embodiments, the one of the most important things that growers do is crop scouting, which is sometimes disregarded because it frequently goes hand in hand with other tasks like weeding, spraying, etc. In the field, the grower is always trying to categorize plants according to their number of fruits, blossoms, growth rate, etc. All of these activities are carried out to forecast future crop requirements and maximize expenses. However, the grower cannot multitask while performing this task because it requires a high level of concentration. Furthermore, it cannot be objective and cannot be carried out by inexperienced staff because it is based on the grower's experience. By analyzing real-time data from sensors on automated ATVs, machine learning algorithms can optimize planting depths, spacing, and seed types for maximum crop yield. The application of machine learning to automated ATV precision planting shows how technology enhances precision agriculture by streamlining planting techniques to increase productivity and efficiency.
[0004] In some embodiments, the plant phenotyping is not only a laborious task, but it also requires high levels of precision and concentration, since accurate and consistent measurements are of the utmost importance to assure high-quality results. Because it enables the plant research community to precisely measure a wide range of plant characteristics (such as height, biomass, tolerance, resistance, architecture, and leaf shape), plant phenotyping is essential for crop selection and adaptation to a variety of environments, new policy constraints, and trends like low-input agriculture and resource-limiting crop cultivation. In order to maximize yield and quality, the algorithms use predictive modeling to determine the best time to harvest crops. This ensures that crops are picked when they are at their most mature. By using machine learning to selectively detect, classify, and harvest crops based on specific criteria, ATVs with advanced sensors and vision systems can reduce field loss and boost productivity.
[0005] FIG. 2 illustrates a method for application of ML and AI vision in ATV according to an embodiment herein. In some embodiments, while some solutions add additional vision sensors, such as spectral and thermal cameras as well as RGB-D sensors, to increase detection rates and accuracy, most of them use color cameras because not all occluded symptoms can be detected in the visible spectrum. Furthermore, the majority of developed systems concentrate on diseases caused by bacteria, viruses, and fungi, with fewer focusing on insects. This is because diagnosing insect infestations often involves identifying the insect, and the movement of the robot may cause the insect to move and hide, making it more challenging to take pictures of it. Real-time crop monitoring, identifying areas that require treatment, and facilitating accurate, automated agrochemical distribution based on identified needs are all made possible by AI-driven vision systems on ATVs. The ability of machine learning algorithms to improve farm input management and lessen environmental harm in agriculture is demonstrated by their integration into ATV automation for fertilization and spraying tasks.
[0006] In some embodiments, due to their uniform application method, conventional spraying techniques have been shown to be highly detrimental to the environment. At the moment, the rate of spraying is unaffected by the stage of plant growth or the presence of weeds or diseases. To guarantee maximum coverage, farmers essentially spray as much as they can. The local ecosystem and the customers who will eventually buy the product are permanently harmed by this practice, which results in excessive contamination of the soil and groundwater supplies. In addition, operators must wear protective gear to avoid contamination during the labor-intensive and dangerous spraying process. The models accurately distinguish between crops and weeds in real-time by using data from sensor and camera systems. Automated ATV operations that incorporate machine learning-based pest detection systems forecast and identify potential pest outbreaks using environmental data, enabling targeted pest management preventive measures. In ATV operations, machine learning facilitates automated decision-making for precise insect and weed control, which can minimize environmental impact, optimize agricultural practices, improve crop health, and use fewer pesticides.
[0007] In some embodiments, the models accurately distinguish between crops and weeds in real-time by using data from sensor and camera systems. Automated ATV operations that incorporate machine learning-based pest detection systems forecast and identify potential pest outbreaks using environmental data, enabling targeted pest management preventive measures. In ATV operations, machine learning facilitates automated decision-making for precise insect and weed control, which can minimize environmental impact, optimize agricultural practices, improve crop health, and use fewer pesticides. Assessing crop health on a broad geographic scale and identifying trends and anomalies are made easier with the use of satellite imaging. With their high-quality imagery and data-gathering capabilities, drones are now crucial instruments for precision agriculture's detailed aerial data collection. Precision decision-making and targeted farming method interventions are facilitated by the vast and localized data that drones equipped with specialized sensors and cameras provide on crops, soil, and topography.

, Claims:1. A method for AI-assisted robotic system for precision agriculture and automated crop management, wherein the method comprising:
enhancing crop health monitoring by utilizing AI algorithms to analyze real-time data from sensors, drones, and robotic platforms;
improving precision in pest and disease detection through machine learning models trained on extensive agricultural datasets;
automating irrigation management by integrating soil moisture sensors and predictive analytics to optimize water usage;
facilitating targeted pesticide and fertilizer application using ai-driven recommendations and robotic spraying systems;
streamlining crop yield forecasting by combining environmental data, historical records, and ai prediction models;
increasing operational efficiency through autonomous navigation of robots in fields for tasks such as planting, weeding, and harvesting;
reducing human labor dependency by enabling robots to perform repetitive and labor-intensive agricultural tasks with high accuracy;
monitoring environmental conditions continuously through ai-enabled drones and IoT devices for temperature, humidity, and soil analysis;
promoting sustainable farming practices by minimizing resource waste through AI-optimized farming decisions; and
adapting to variable farming conditions using self-learning ai systems capable of adjusting to changes in weather and crop requirements.

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