An Efficient Plant Disease Recognition System Using Hybrid Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs) for Smart IoT Applications in Agriculture

Abstract

Abstract In recent times, the Internet of Things (IoT) and Deep Learning Models (DLMs) can be utilized for developing smart agriculture to determine the exact location of the diseased part of the leaf on farmland in an efficient manner. There is no exception that convolutional neural networks (CNNs) have achieved the latest accomplishment in many aspects of human life and the farming sector. Semantic image segmentation is considered the main problem in computer vision. Despite tremendous progress in applications, approximately all semantic image segmentation algorithms fail to achieve sufficient hash results because of the absence of details sensitivity, problems in assessing the global similarity of image pixels, or both. Methods of post-processing improvement, as a wonderfully critical means of improving the underlying flaws mentioned above from algorithms, depend almost on Conditional Random Fields (CRFs). Therefore, plant disease prediction plays important role in the premature notification of the disease to alleviate its effects on disease forecast investigation purposes in the smart farming arena. Hence, this work proposes an efficient IoT-based plant disease recognition system using semantic segmentation methods such as FCN-8 s, CED-Net, SegNet, DeepLabv3, and U-Net with the CRF method to allocate disease parts in leaf crops. Evaluation of this network and comparison with other networks of the state art. The experimental results and their comparisons proclaim over F1-score, sensitivity, and intersection over union (IoU). The proposed system with SegNet and CRFs gives high results compared with other methods. The superiority and effectiveness of the mentioned improvement method, as well as its range of implementation, are confirmed through experiments.

Keywords

• Internet of Things (IoT)
• Deep learning
• Disease leaf segmentation
• Smart farming
• Semantic segmentation
• Computer vision