Investigating Transfer Learning based CNN for Multi-Crop Disease Recognition in Horticulture Crops using Wireless Multimedia Sensor Network

Abstract

Introduction: Achieving sustainable growth in agricultural productivity is crucial for addressing various challenges confronting food systems globally. One of the key aspects is effectively identifying and addressing diseases that can adversely affect crop quality and yield. Methods: This research study proposes the use of Wireless Multimedia Sensor Network (WMSN) in conjunction with deep transfer learning model for disease detection in horticulture crops. The proposed system utilizes the image data of horticulture crops obtained using WMSN for performing a comparative assessment of several pre-trained deep learning models, encompassing VGG, ResNet, MobileNet, Inception, Xception, DenseNet, and EfficientNet, to ascertain their effectiveness in identifying diseases across a range of horticultural plants. The models undergo fine-tuning using transfer learning technique and are assessed on different measures, such as accuracy, precision, recall, F1 score, and the number of epochs needed for convergence using the image data obtained from WMSN. Results and Discussion: Among the compared models, EfficientNetB3 appears as the best performing model, surpassing others in all evaluation metrics while also exhibiting low parameters and a compact size. Notably, EfficientNetB3 achieves a testing accuracy of 93.69% and a training accuracy of 98.85% after only 21 epochs of training. In contrast, DenseNet201, DenseNet169, and EfficientNetB0 demonstrate performance close to the best-performing models, but they have higher parameters or sizes. On the contrary, ResNet50, InceptionV3, and Xception exhibit poorer performance, presumably due to their substantial size in relation to the dataset. Conclusion: This analysis provides valuable insights for optimizing the top-performing models for disease classification across various plant species. Moreover, the fine-tuned EfficientNetB3 model shows potential for integration with mobile devices, facilitating real-time disease classification.

Plant phenotyping relevance

植物画像から病害状態を推定する深層学習手法を提案し、複数モデルを比較評価しているため、病害フェノタイピング手法が中心です。

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