Open AccessHandling Severity Levels of Multiple Co-Occurring Cotton Plant Diseases using Improved YOLOX Model
Author(s) -
Serosh Karim Noon,
Muhammad Amjad,
Muhammad Ali Qureshi,
Abdul Mannan
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3232751
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Automatic detection of plant diseases has emerged as a challenging field in the last decade. Computer vision-based advancements have helped in timely and accurate identification of diseases, making possible an appropriate treatment and hence ensuring an increased yield. Diseases attack in different formations on a plant; the most severe being multiple diseases appearing on a single leaf. Moreover, as various diseases progress, they generate similar-looking symptoms making the task of identification further difficult. This work addresses these two problems with the help of an improved YOLOX model. We propose a modified Spatial Pyramid Pooling (SPP) layer to effectively extract relevant features at various scales from the training data. It is achieved by concatenating multilevel features pooled from smaller to larger scales. To enhance generalization capability of the design, various skip connections are also introduced. To improve the network convergence and detection accuracy, α IoU based regression loss function was employed. A dataset composed of 1, 112 cotton plant images with co-occurring diseases along with their progressive severity levels was collected from the Southern Punjab region of Pakistan. Apart from healthy images, the dataset comprises of 3 severity stages of cotton leaf curl with co-occurring cotton sooty mold stress on single leaf image. Experimental results revealed that our proposed improved SPP-based YOLOX-s model achieved 73.13% mAP on our self-collected dataset and achieved 3.27% better test accuracy than original YOLOX model.