The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …

The rapid advances in Deep Learning (DL) techniques have enabled rapid detection,
localisation, and recognition of objects from images or videos. DL techniques are now being …

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 …

Weeds are one of the most important factors affecting agricultural production. The waste and
pollution of farmland ecological environment caused by full-coverage chemical herbicide …

Solanum rostratum Dunal is a common invasive alien weed that can damage native
ecosystems and biodiversity. Detecting Solanum rostratum Dunal at an early stage of growth …

The growing population and effect of climate change have put a huge responsibility on the
agriculture sector to increase food-grain production and productivity. In most of the countries …

The agriculture industry is undergoing a rapid digital transformation and is growing powerful
by the pillars of cutting-edge approaches like artificial intelligence and allied technologies …

Instance segmentation, an important image processing operation for automation in
agriculture, is used to precisely delineate individual objects of interest within images, which …

Weed detection has become an integral part of precision farming that leverages the IoT
framework. Weeds have become responsible for 45% of the agriculture industry's crop …

Conventional weed management approaches are inefficient and non-suitable for integration
with smart agricultural machinery. Automatic identification and classification of weeds can …