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 …

Crop production can be greatly reduced due to various diseases, which seriously endangers
food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional …

During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used
to improve agriculture productivity while reducing drudgery, inspection time, and crop …

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 …

Deep Learning (DL) has been described as one of the key subfields of Artificial Intelligence
(AI) that is transforming weed detection for site-specific weed management (SSWM). In the …

Timely and reliable information about crop management, production, and yield is considered
of great utility by stakeholders (eg, national and international authorities, farmers …

In the last few years, large efforts have been made to develop new methods to optimize
stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has …

Artificial intelligence has smoothly penetrated several economic activities, especially
monitoring and control applications, including the agriculture sector. However, research …

Time is an often-neglected variable in biological research. Plants respond to biotic and
abiotic stressors with a range of chemical signals, but as plants are non-equilibrium systems …

Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many
cultures are exposed to them. The disease is difficult to control on a large scale; thus, one of …