Lesion Detection on Leaves using Class Activation Maps
Enes Sadi Uysal Deniz Sen Ahmet Haydar Ornek Ahmet Emin Yetkin
Lesion detection on plant leaves is a critical task in plant pathology and
agricultural research. Identifying lesions enables assessing the severity of
plant diseases and making informed decisions regarding disease control measures
and treatment strategies. To detect lesions, there are studies that propose
well-known object detectors. However, training object detectors to detect small
objects such as lesions can be problematic. In this study, we propose a method
for lesion detection on plant leaves utilizing class activation maps generated
by a ResNet-18 classifier. In the test set, we achieved a 0.45 success rate in
predicting the locations of lesions in leaves. Our study presents a novel
approach for lesion detection on plant leaves by utilizing CAMs generated by a
ResNet classifier while eliminating the need for a lesion annotation process.