Deep Convolutional Neural Networks-Based Machine Vision System for Detecting Tomato Leaf Disease
2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)
(2022), pp. 1-5
Dennis C. Malunao
a
,
Roger S. Tamargo
a
,
Ricky C. Sandil
b
,
Christopher Franco Cunanan
c
,
Jovencio V. Merin
d
,
Roldan D. Jallorina
e
a College of Computing Sciences, Ifugao State University, Ifugao, Philippines
b Department of Computer Engineering, FEU Institute of Technology, Manila, Philippines
c Computer Engineering Department, Bulacan State University, Bulacan, Philippines
d Department of Electrical Engineering, Technological University of the Philippines, Manila, Philippines
e Agriculture Department, Southern Luzon State University, Infanta, Quezon, Philippines
Abstract: Immediate identification of plant disease is one of the important solutions in Agricultural problems. In this study, the researchers develop an early detection system for tomato leaf diseases. It is important to create a system that will detect and classify a certain disease present in the leaf to prevent further loss. In order to do that, the researchers used an algorithm called YOLOv3 for training a model that accurately detects specific diseases for tomato leaves. The proposed model is able to classify the diseases and has a mean average precision(mAP) of 98.28 %. The result of the trained model varied with the accuracies ranging from 75% - 99%, for detecting the two common tomato leaf diseases such as, Early Blight and Septoria Leaf Spot.