The timely identification of diseases in maize leaf offers several benefits such as increased
crop productivity, reduced reliance on harmful chemicals, and improved production of …

Wheat rusts, caused by pathogenic fungi, are responsible for significant losses in Wheat
production. Leaf rust can cause around 45–50% crop loss, whereas stem and stripe rust can …

This review article provides a comprehensive examination of the state-of-the-art in maize
disease detection leveraging Convolutional Neural Networks (CNNs). Beginning with the …

In the era of frequently changing climatic conditions along with ever increasing world
population, it becomes imperative to ensure food security. The burden of biotic stresses …

The automatic recognition of crop diseases based on visual perception algorithms is one of
the important research directions in the current prevention and control of crop diseases …

In practice, acquiring and annotating data in specialized domains can be costly, thereby
constraining the performance and applicability of deep learning. Utilizing generative models …

With the advancement of technologies, automatic plant leaf disease detection has received
considerable attention from researchers working in the area of precision agriculture. A …

Deep learning technologies have enabled the development of a variety of deep learning
models that can be used to detect plant leaf diseases. However, their use in the identification …

Maydis leaf blight (MLB) of maize (Zea Mays L.), a serious fungal disease, is capable of
causing up to 70% damage to the crop under severe conditions. Severity of diseases is …

Controlling plant leaves disease helps in upholding their health. This augments the overall
strength of the plant productivity both in terms of higher quality and quantity. Recently, with …