Comparative Performance Analysis of Real-Time Methods for Cassava Phytoplasma Disease (CPD) Detection based on Deep Learning Neural Networks

Abstract

Cassava Phytoplasma Disease (CPD) is a crop disease that reduces cassava output and quality. As a result, detection is essential in precision agriculture. On the greater area of the cassava field, manual identification of CPD illnesses takes more time and effort. Convolutional Neural Networks (CNN), a deep learning method, may be used to detect illnesses on leaves and other sections of cassava plants with greater accuracy. The approaches utilized in this study assisted in the identification of CPD by completing customized training/fine-tuning on three CNN models for object recognition: Faster R-CNN, SSD Mobilenet v2, and YOLO v4. The Faster R-CNN inception v2 has a 95 percent training accuracy, SSD Mobilenet v2 has a 73 percent training accuracy, and YOLOv4 has an 85 percent training accuracy, according to the data. Finally, the study found that the YOLOv4 outperforms the Faster R-CNN inception v2 and SSD MobileNet v2 in terms of image computing capacity. However, Faster R-CNN inception v2 performs the best compared to the two other models in terms of accuracy. Hence, these two models can be used depending on the purpose of CPD detection. However, since CPD detection is the main purpose of this study, the Faster R-CNN model is recommended for adoption to detect CPD in a real-time environment. Keywords — cassava phytoplasma disease, convolutional neural networks, faster R-CNN, image processing, precision agricultur