Transforming Agriculture: Plant Disease Detection with Transfer Learning and Deep Neural Network

DOI

10.2991/978-94-6463-716-8_38How to use a DOI?

Keywords

Plant Disease Detection; Transfer learning; Deep learning; Convolutional Neural Network (CNN); Agriculture Technology

Abstract

Plant leaf disease detection plays a critical role in ensuring healthy crop yields and preventing severe damage caused by plant diseases. Traditional diagnostic methods, however, are often time-consuming and complex, requiring laboratory practices. In contrast, Artificial Intelligence (AI), particularly Deep Learning (DL) and Machine Learning (ML) techniques, have emerged as a boon to the agricultural industry. Recently, ML and DL approaches have been increasingly applied for diagnosing plant diseases. For this study, the Plant Village dataset was obtained from Kaggle and augmented to enhance model training. The proposed method improves detection accuracy without requiring extensive labeled data by leveraging pre-trained deep learning models for feature extraction from plant leaf images. This methodology encompasses data collection, pre-processing, model selection, and evaluation. The performance of the model was assessed using accuracy, precision, recall, and F1 score metrics. The results demonstrated that transfer learning significantly enhanced the model’s ability to classify both healthy and damaged leaves with high accuracy and a low probability of false positives. Additionally, the model’s adaptability was tested by evaluating its generalization ability across different plant species and types of infections. This paper presents a Convolutional Neural Network (CNN) augmented with pre-trained models to identify and categorize plant leaf diseases. On the PlantVillage dataset, the proposed approach achieved a training accuracy of 99.81% and a validation accuracy of 99.68%.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Abhishek Sharma
AU  - Rohit Bansal
AU  - Trasha Bansal
PY  - 2025
DA  - 2025/05/26
TI  - Transforming Agriculture: Plant Disease Detection with Transfer Learning and Deep Neural Network
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 482
EP  - 495
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_38
DO  - 10.2991/978-94-6463-716-8_38
ID  - Sharma2025
ER  -