Open AccessComparative Performance Analysis of Real-Time Methods for Cassava Phytoplasma Disease (CPD) Detection based on Deep Learning Neural Networks
Author(s) -
Irma T. Plata,
AUTHOR_ID,
Edward B. Panganiban,
Darios B. Alado,
Allan C. Taracatac,
Bryan B. Bartolome,
Freddie Rick E. Labuanan
Publication year - 2022
Publication title -
international journal emerging technology and advanced engineering
Language(s) - English
Resource type - Journals
ISSN - 2250-2459
DOI - 10.46338/ijetae0222_07
Subject(s) - convolutional neural network , computer science , artificial intelligence , deep learning , phytoplasma , identification (biology) , object detection , pattern recognition (psychology) , field (mathematics) , artificial neural network , machine learning , mathematics , biochemistry , chemistry , botany , biology , restriction fragment length polymorphism , pure mathematics , gene , polymerase chain reaction
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