Black pepper leaf disease detection using deep learning
Year: 2025
Type: article
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.
Authors: Jagadeesha B G, Ramesh Hegde, Ajith Padyana
Institution: Acharya Institute of Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4408505831