Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

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 …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Deep learning is a branch of artificial intelligence. In recent years, with the advantages of
automatic learning and feature extraction, it has been widely concerned by academic and …

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 …

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 …

Crop diseases cause a substantial loss in the quantum and quality of agricultural production.
Regular monitoring may help in early stage disease detection an d thereby reduction in crop …

Plants are recognized as essential as they are the primary source of humanity's energy
production since they are having nutritious, medicinal, etc. values. At any time between crop …

Plant-leaf disease detection is one of the key problems of smart agriculture which has a
significant impact on the global economy. To mitigate this, intelligent agricultural solutions …

The agricultural production rate plays a pivotal role in the economic development of a
country. However, plant diseases are the most significant impediment to the production and …