DESIGN AND IMPLEMENTATION OF PEST IDENTIFICATION AND CONTROL IN FARM FIELD USING INTELLIGENT COMPUTATIONAL TECHNIQUES

Agricultural productivity is significantly challenged by pest infestations, which result in crop losses, increased production costs, and environmental damage. Traditional methods of pest control, reliant on chemical pesticides and manual interventions, have proven unsustainable due to issues like pesticide resistance, environmental pollution, and negative impacts on human health and biodiversity. This project aims to develop an intelligent computational system that leverages machine learning techniques to identify and manage pests in farm fields. The primary objective is to automate the detection and classification of pests using algorithms such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). The system also integrates environmental data to provide sustainable pest control recommendations. The methodology includes the development of a framework combining machine learning models, 10T sensors, and decision support systems to enhance the efficiency and accuracy of pest management strategies. The results demonstrate the potential of these computational techniques to revolutionize pest management practices, promoting agricultural sustainability and improving food security.