In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …

Globally, all the major crops are significantly affected by diseases every year, as manual
inspection across diverse fields is time-consuming, tedious, and requires expert knowledge …

Tomato leaf disease seriously affects the yield of tomato. It is extremely vital for agricultural
economy to identify agricultural diseases. The traditional data augmentation methods, such …

The deep learning methods based on convolutional neural network (CNN) have been
widely explored in dataset augmentation and recognition of plant leaf diseases. The recently …

Deep learning methods such as convolutional neural networks (CNN) have become popular
for addressing crops and weeds classification problems in agricultural robotics. However, to …

Recently many methods have been induced for plant disease detection by the influence of
Deep Neural Networks in Computer Vision. However, the dearth of transparency in these …

Plant disease has a considerable influence on the safety of grain output and quality.
Therefore, it is crucial to detect and diagnose plant diseases. Most plant diseases are …

Polyp segmentation plays an important role in preventing Colorectal cancer. Although Vision
Transformer has been widely introduced in medical image segmentation to compensate the …

The foundation of effectively predicting plant disease in the early stage using deep learning
algorithms is ideal for addressing food insecurity, inevitably drawing researchers and …

Due to the complex environments in real fields, it is challenging to conduct identification
modeling and diagnosis of plant leaf diseases by directly utilizing in-situ images from the …