Identification and Classification of Potato and Pepper bell Disease Using Convolution Neural Network

Madhur Nagrath

International Journal of Scientific Research in Artificial Intelligence and Machine Learning, 2026, vol. 2, issue 2, 16-25

Abstract: India’s economy heavily relies on agriculture. Identification of plant disease is essential to understand India’s economy in terms of agricultural productivity. Plant diseases classification is a necessity as they can impair a species’ ability to expand and develop. Plant disease detection is critical for preventing crop loss and disease spread. Disease can be accurately predicted and prevented if visi- ble signs are recognised. This technique is frequently used by farmers or plant pathologists to manually inspect the plant’s leaves and identify disease. Sev- eral deep learning models have been trained to classify major plant diseases. Because convolutional neural network models are so effective at image classifica- tion. Pre-trained deep learning transfer learning models provide faster and more precise predictions than manual plant leaf inspection. CNN model is trained using fine-tuning techniques before being optimised for hyperparameters. Pre-training models include the VGG family and the XceptionNet models. The VGG19 model produced promising accuracy results.

Keywords: Plant Disease; MobileNet; Convolution Neural Network (CNN); transfer learning; Deep Learning; Agriculture; PlantVillage; VGG16; VGG19; RESNET50 (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML26223
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