Wheat is a major grain crop cultivated worldwide, and its production is influenced by various
diseases and pests, leading to significant production losses. Timely disease detection in …

Early detection of plant diseases is critical to prevent disease spread and assist farmers.
Thanks to their high discrimination ability, Convolutional Neural Network (CNN)-based …

Artificial intelligence and neuroscience have a long and intertwined history. Advancements
in neuroscience research have significantly influenced the development of artificial …

Using deep learning-based tools in the field of agriculture for the automatic detection of plant
leaf diseases has been in place for many years. However, optimizing their use in the specific …

Globally, pests and plant diseases severely threaten forestry and agriculture. Plant
protection could be substantially enhanced by using noncontact, extremely effective, and …

Driver behavior features extracted from the controller area network (CAN) have potential
applications in improving vehicle safety. However, the development of a classifier-based …

Global food security is seriously threatened by wheat leaf disease, which makes effective
and precise disease detection and classification techniques necessary. For efficient disease …

The existence of crop disease leads to a substantial loss in quantity and quality in
agricultural production. The regular monitoring of plants helps in preventing leaf disease at …

Few-shot learning aims to solve the problem of learning with limited samples. However,
existing metric-based meta-learning models have the issue of insufficient attention to feature …

Existing approaches often fail to capture the complex spatial-temporal interactions among
crop conditions, environmental factors, and disease progression, leading to suboptimal …