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 …

Agriculture plays a vital role in the economic growth of any country. With the increase of
population, frequent changes in climatic conditions and limited resources, it becomes a …

With increasing population, the demand for agricultural productivity is rising to meet the goal
of “Zero Hunger”. Consequently, farmers have optimized the agricultural activities in a …

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 …

Weeds are among the major threats to cotton production. Overreliance on herbicides for
weed control has accelerated the evolution of herbicide-resistance in weeds and caused …

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 …

Computer vision technologies have attracted significant interest in precision agriculture in
recent years. At the core of robotics and artificial intelligence, computer vision enables …

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 …

Weeds are a crucial threat to agriculture, and in order to preserve crop productivity,
spreading agrochemicals is a common practice with a potential negative impact on the …

Population growth, climate change, and the worldwide COVID-19 pandemic are imposing
increasing pressure on global agricultural production. The challenge of increasing crop yield …