Plants play a crucial role in supplying food globally. Various environmental factors lead to
plant diseases which results in significant production losses. However, manual detection of …

With increasing population, the demand for agricultural productivity is rising to meet the goal
of “Zero Hunger”. Consequently, farmers have optimized the agricultural activities in a …

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 …

In general, most of the existing convolutional neural network (CNN)-based deep-learning
models suffer from spatial-information loss and inadequate feature-representation issues …

Object segmentation in deep learning has been recently used for the detection of Fusarium
head blight (FHB), a worldwide disease in wheat. Such method, however, cannot detect the …

Higher agricultural outputs are required due to the rising worldwide population, shifting
nutritional preferences, and growing demand for food and basic materials for the industry …

The pathogens such as fungi and bacteria can lead to rice diseases that can drastically
impair crop production. Because the illness is difficult to control on a broad scale, crop field …

Wheat leaf diseases are considered to be the foremost threat to wheat yield. In the realm of
crop disease detection, convolutional neural networks (CNNs) have emerged as important …

As wheat is one of the major crops worldwide, therefore, accurate disease detection in
wheat plants is critical for mitigating effects and halting disease spread. Nowadays, the …

The timely and accurate identification of stripe rust and leaf rust is essential in effective
disease control and the safe production of wheat worldwide. To investigate methods for …