Agricultural Pest Detection Using Convolutional Neural Networks: A Smart Farming Solution

Abstract

Pest infestation remains a significant challenge in agriculture, leading to reduced crop yield and economic losses. Accurate and timely identification of pests is essential for implementing effective pest management strategies. This research proposes a deep learning-based solution for pest classification using Convolutional Neural Networks (CNN) integrated with computer vision techniques. A custom dataset comprising images of various agricultural pests was created and used to train a CNN model capable of recognizing and classifying pest species with high accuracy. The trained model is deployed through a Django-based web application, providing a user-friendly interface for uploading pest images and receiving real-time classification results via a RESTful API. The system was tested with multiple pest images under different conditions, demonstrating robust performance and rapid inference capabilities. This approach not only automates pest detection but also supports early intervention, contributing to smarter and more sustainable agricultural practices. The model's scalability and ease of integration make it a valuable tool for farmers, agronomists, and researchers working in the domain of precision agriculture.