Several factors associated with disease diagnosis in plants using deep learning techniques
must be considered to develop a robust system for accurate disease management. A …

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 …

Significant advances in the field of digital image processing have been achieved in recent
years using deep learning, which has significantly exceeded previous methods. Deep …

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 …

It is important to develop accurate disease management systems to identify and segment
corn disease lesions and estimate their severity under complex field conditions. Although …

Grape black measles disease may be one of the best known, longest studied and most
destructive of all plant diseases, which ultimately reduces productivity and quality of …

Plant diseases play a significant role in agricultural production, in which early detection of
plant diseases is deemed an essential task. Current computational intelligence and …

In complex environments, overlap** leaves and uneven light can make pixels of leaf
edges difficult to identify, resulting in a poor segmentation performance of the target leaf. In …

Agriculture's productivity has decreased in the last decade due to climate change and
inappropriate usage of water, fertilizer, and pesticides, which stimulate plant diseases. Plant …

Agriculture has becomes an immense area of research and is ascertained as a key element
in the area of computer vision. In the agriculture field, image processing acts as a primary …