Black pepper leaf disease detection using deep learning

Jagadeesha B G (), Ramesh Hegde () and Ajith Padyana ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 2, 897-907

Abstract: Advances in deep learning techniques have achieved spectacular success in the detection of plant diseases. A new method for detecting black pepper leaf disease using deep learning was proposed. In the proposed scheme, the SqueezeNet model is used, which is a Convolutional Neural Network (CNN), where the CNN is a subset of deep learning networks. The disease detection is based on the visual characteristics of the black pepper leaves. Thus, the proposed method is an image classification scheme using a trained SqueezeNet that detects whether the pepper leaves are healthy or diseased. The detection accuracy is found to be more than 99%. The early detection of defects, such as deformation and discoloration of pepper leaves, forewarns the onset of diseases, and the cultivator of pepper wines can undertake appropriate countermeasures.

Keywords: Black pepper diseases; Convolutional Neural Networks; Deep learning networks; Image data store; Leaf image classification; SqueezeNet. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:2:p:897-907:id:5389

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