Classification and Detection of Rice Diseases Using a 3-Stage CNN Architecture with Transfer Learning Approach

• Published in: Agriculture (Basel) Vol. 13; no. 8; p. 1505
• Main Authors: Gogoi, Munmi, Kumar, Vikash, Begum, Shahin Ara, Sharma, Neelesh, Kant, Surya
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2023
• Subjects: Accuracy; Agricultural production; Analysis; Artificial intelligence; Artificial neural networks; Classification; Crop diseases; Data collection; Data processing; Datasets; Deep learning; Developing countries; Disease detection; Disease susceptibility; Food production; Food security; Food supply; image classification; LDCs; Learning strategies; Leaves; Machine learning; Medical imaging; Neural networks; Plant diseases; PReLU; progressive re-sizing; Rice; Simulation methods; Sizing; Sustainable food systems; Training; Transfer learning; India
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture13081505

Rice is a vital crop for global food security, but its production is vulnerable to various diseases. Early detection and treatment of rice diseases are crucial to minimise yield losses. Convolutional neural networks (CNNs) have shown great potential for disease detection in plant leaves, but training CNNs requires large datasets of labelled images, which can be expensive and time-consuming. Here, we have experimented a 3-Stage CNN architecture with a transfer learning approach that utilises a pre-trained CNN model fine-tuned on a small dataset of rice disease images. The proposed approach significantly reduces the required training data while achieving high accuracy. We also incorporated deep learning techniques such as progressive re-sizing and parametric rectified linear unit (PReLU) to enhance rice disease detection. Progressive re-sizing improves feature learning by gradually increasing image size during training, while PReLU reduces overfitting and enhances model performance. The proposed approach was evaluated on a dataset of 8883 and 1200 images of disease and healthy rice leaves, respectively, achieving an accuracy of 94% when subjected to the 10-fold cross-validation process, significantly higher than other methods. These simulation results for disease detection in rice prove the feasibility and efficiency and offer a cost-effective, accessible solution for the early detection of rice diseases, particularly useful in developing countries with limited resources that can significantly contribute toward sustainable food production.