In recent years, computer vision and machine learning (CV-ML) is continuously evolving,
with new applications, techniques, and models, and its domain CV-ML has touched the …

In recent years, computer vision, a noncontact-as well as a nondestructive-technique
involving the development of theoretical and algorithmic tools for automatic visual …

AI branches many areas including computer vision and machine learning which are growing
in a variety of application sectors. In this perspective, the agriculture sector is a promising …

Approximately 135 million people in Bangladesh use rice as their primary source of food.
Nowadays, paddy diseases constitute a serious agricultural problem that influences both …

The productivity and efficiency of a traditional agriculture crop production cycle can be
improved by computer vision and machine learning. Innovative Information and …

Traditional methods of plant disease detection are cumbersome and prone to errors that
cannot be avoided. Plant disease detection can help prevent crop losses and ensure food …

Agricultural sustainability can be attained through vision-enabled autonomous machines
that work together as a phenomenon to ensure global food security. The demand for efficient …

Sugarcane diseases are major concern for the global sugarcane market, as they can
significantly impact crop yield and quality. This can result in economic losses for farmers and …

Computer vision is the area through which machines “see”. Instead of eyes, it uses cameras
to locate, monitor, and quantify the targets for further image processing. In precision …

The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial
Intelligence (AI) have sparked a revolution in the agricultural industry, with applications …