Analysis of Learned Features and Framework for Potato Disease Detection

2023/08/29 by Shikha Gupta, Gupta, Shikha, Soma Chakraborty +3
Agricultural and Biological Sciences · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Plant Disease Management Techniques #Plant Virus Research Studies #Smart Agriculture and AI

paper · pdf · doi:10.48550/arxiv.2310.05943

openalex publication_date 2023/08/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

For applications like plant disease detection, usually, a model is trained on publicly available data and tested on field data. This means that the test data distribution is not the same as the training data distribution, which affects the classifier performance adversely. We handle this dataset shift by ensuring that the features are learned from disease spots in the leaf or healthy regions, as applicable. This is achieved using a faster Region-based convolutional neural network (RCNN) as one of the solutions and an attention-based network as the other. The average classification accuracies of these classifiers are approximately 95% while evaluated on the test set corresponding to their training dataset. These classifiers also performed equivalently, with an average score of 84% on a dataset not seen during the training phase.

Related

• Potato Leaf Disease Classification using Deep Learning: A Convolutional Neural Network Approach
• Transfer Learning With Densenet201 Architecture Model For Potato Leaf Disease Classification
• PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification
• Small data deep learning methodology for in-field disease detection
• Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation