International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
ENHANCED PEST MANAGEMENT IN PEANUT FARMING USING CNN V.Varshini1, V.Pallavi2, K. Vishnu Kumar3, M.Sahithi4 1234Department of Information Technology, TKR College of Engineering and Technology, Telangana, India
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Abstract - Peanut farming is highly vulnerable to pest
one of the most critical factors affecting crop productivity. Pests, insects, and microbial diseases significantly reduce crop yield and quality, resulting in economic losses for farmers. Even small improvements in pest detection and control can lead to substantial gains in productivity and profitability. This study focuses on the application of machine learning and Convolutional Neural Networks (CNN) for effective pest detection in peanut farming, as CNN models are highly suitable for image classification, segmentation, and object recognition tasks.
attacks, which lead to significant yield losses and reduced crop quality. Traditional pest detection methods rely on manual observation, which is time-consuming, inaccurate, and often unable to identify early-stage infestations. To address this challenge, this project presents an Enhanced Pest Management System for Peanut Farming using Convolutional Neural Networks (CNN). The system uses deep learning techniques to analyse leaf images, identify pest-infected regions, and classify pest types with high accuracy. Farmers or users can upload peanut plant images through the application, and the CNN model processes these images to detect the presence of pests. The system generates a prediction along with confidence levels, enabling early intervention and timely pest control. Additionally, the platform stores image data, prediction results, and timestamps, allowing continuous monitoring of crop health. The model is trained on a curated dataset of peanut crop pests, ensuring reliable detection even under varying lighting and environmental conditions. By integrating deep learning with an easy-to-use web interface, this solution provides a fast, accurate, and scalable approach to pest management. The system enhances decision making for farmers, reduces reliance on manual inspection, and contributes to improved yield and sustainable agricultural practices.
1.1 Background and Need for Intelligent Pest Management Peanut (groundnut) farming plays a vital role in the agricultural economy, especially in countries where it is a major source of edible oil and farmer income. However, peanut crops are highly vulnerable to a wide range of pests such as aphids, leaf miners, thrips, and caterpillars. These pests cause severe damage to leaves, stems, and pods, leading to significant yield losses and reduced crop quality. Traditional pest management methods rely heavily on manual field inspection and blanket pesticide application. These approaches are time-consuming, labour-intensive, and often inaccurate, resulting in delayed pest detection and excessive chemical usage. Overuse of pesticides not only increases production costs but also harms soil health, beneficial insects, and the environment. Hence, there is a strong need for an intelligent, precise, and eco-friendly pest management system that can assist farmers in identifying pests early and taking timely action.
Key Words: Peanut Farming, Pest Detection, Convolutional Neural Network, Deep Learning, Image Classification, Agriculture Automation.
1. INTRODUCTION Agriculture plays a vital role in sustaining human and livestock populations worldwide and remains a key contributor to national economies. In recent years, the agricultural sector has increasingly adopted advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) to enhance productivity, efficiency, and sustainability. Agriculture also serves as a major source of raw materials used in the production of food products, chemicals, and pharmaceuticals. Although the total agricultural land area expanded by only about 15% from the 1960s to the early 2000s, global agricultural output nearly tripled due to the adoption of fertilizers, pesticides, improved crop varieties, and precision farming techniques. However, in recent decades, the growth rate of agricultural production has slowed due to challenges such as climate change, population growth, urbanization, and labor shortages. Among these challenges, pest infestation remains
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Fig -1: Background and Need for Intelligent
Pest Management
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