Lesion Detection on Leaves using Class Activation Maps
This addresses lesion detection for plant pathology and agriculture, offering an incremental improvement by eliminating annotation requirements.
The paper tackled the problem of detecting small lesions on plant leaves by using class activation maps from a ResNet-18 classifier, achieving a 0.45 success rate in predicting lesion locations without needing lesion annotations.
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.