Comparative performance of four CNN-based deep learning variants in detecting Hispa pest, two fungal diseases, and NPK deficiency symptoms of rice (Oryza sativa)

• Published in: Computers and electronics in agriculture 2022-11, Vol.202, p.107340, Article 107340
• Main Authors: Dey, Biplob, Masum Ul Haque, Mohammed, Khatun, Rahela, Ahmed, Romel
• Format: Article
• Language: English
• Subjects: CNN; Diseases; NPK deficiency; Rice; Transfer learning
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2022.107340

[Display omitted] •Public and real field sample images of rice were analyzed using CNN and CNN-based four advanced models.•Each model was found to have a distinct capability to identify symptom from the complex dataset used in our study.•Combined dataset performed better than the single source; VGG19 showed the highest prediction accuracy (91.8%).•Higher accuracy in detection indicates the robustness of the proposed models to use in precision agriculture. Crop production can be significantly increased if stresses are detected at the earliest possible time to facilitate the implementation of necessary mitigation measures. This present study aims to evaluate the comparative performance of the Convolutional Neural Network (CNN) and four pre-trained deep CNN models, for automatic and rapid detections of Hispa, brown spot, leaf blast, and NPK deficiency symptoms from public and real field images. Deep learning models were trained using different public and field datasets combinations. Best accuracy was achieved for mixed public and field datasets rather than solely field or solely public datasets, with VGG19 models achieving 91.8% accuracy. Relatively simple structured CNN was found to predict phosphate deficient leaves with better performance (96% accuracy) than the other four advanced models, while VGG16 performed better for leaf blast and N deficiency identification. Likewise, ResNet50 could be recommended among the five models for the Potassium deficient leaf identification, while for the Hispa pest, VGG19 outperforms the InceptionV3, ResNet50, and VGG16 models, and was slightly better than the basic CNN. The implication of the study is enormous considering practical application as it deals with the complex dataset of pest, disease and nutrient deficiency. This non-invasive way of detecting different stresses of rice could help farmers in practicing precision agriculture. The findings from this study could be used to develop a user-friendly interface for the rapid and inexpensive detection of diseases and nutrition status by farmers, in a non-destructive way. Similar to rice, various biotic and abiotic stress symptoms in other economic crops could also be captured and identified by deep learning algorithms.