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September 28, 2025Open Access

Pest Detection in Crops Using Image Processing and Deep Learning

Key Points

The CNN model achieved 98.7% accuracy in pest classification, outperforming ResNet.
Automated detection methods in this project reduce labor and costs associated with pest identification.
Implementing deep learning and image processing facilitates early detection of pests in maize and rice.
The approach supports sustainable agriculture by reducing pesticide overuse and promoting preventive measures.

Abstract

ABSTRACT One of the biggest problems in agriculture is pest infestation, which lowers productivity and causes losses. Conventional pest detection methods are not appropriate for large-scale farming because they are laborious, inaccurate, and expensive. This project uses deep learning and image processing techniques to develop an automated earlier pest type detection system. CNN and ResNet models are used to preprocessed pictures of maize and rice leaves. According to experimental data, the CNN model outperformed the ResNet model in terms of pest classification, with a higher accuracy of 98.7% and a smaller loss. The method encourages sustainable agriculture, lessens the overuse of pesticides, and facilitates early pest identification with preventative measures included. Keywords: Pest Detection, Image Processing, Deep Learning, Pest Classification, CNN, Resnet

Pest Detection in Crops Using Image Processing and Deep Learning

Key Points

Abstract

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