Authors - Dhanalakshmi R, Prashaanth S, Hari Prasath S, Dhanaselvam J, Harish R Abstract - India is mainly an agricultural country, where almost three-fourths of the country's population works on farms. Several crops are grown according to regional situations. High-quality production of these crops can be achieved only with new techniques. The appropriate management of crops and identification of diseases and their respective treatments are very significant to prevent losses after harvesting as it usually happens. Diseases in crops deviate from their normal functions and show symptoms that hinder growth. Pests and insects always devastate major crops like rice, wheat, maize, and soyabeans. Consequently, productivity becomes low. With the adoption of deep learning technologies, pest infestation detection and management in agriculture have accuracy and efficiency. A solution is proposed in this paper that integrates image processing techniques with the MATLAB platform for the classification of pests and the proper fertilizers and pesticides to be applied. An autonomous robotic sprayer is used by this system to remotely traverse crop fields, ensuring pinpoint treatment applications. On the other hand, the infrastructure cost is reduced by the proposed solution. The camera setup density in an agricultural IoT monitoring system is minimized by it. Thus, the advanced technology is integrated with agricultural practice by this approach to promote sustainable farming. A validation accuracy of 99.80% is achieved by it to maximize crop production while minimizing losses due to pests and diseases.
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