Weeds and crops engage in a relentless battle for the same resources, leading to potential
reductions in crop yields and increased agricultural costs. Traditional methods of weed …

The growing demand for precision agriculture necessitates efficient and accurate crop-weed
recognition and classification systems. Current datasets often lack the sample size, diversity …

The past 50 yr of advances in weed recognition technologies have poised site-specific weed
control (SSWC) on the cusp of requisite performance for large-scale production systems …

With the development of the latest remote sensing techniques, farmers have more control
over their fields, yield forecasting and maximization. In this respect, the classification of …

Efficient plant cultivation depends in large measure on weed control effectiveness. Weed
conservation within the first six to eight weeks since planting is critical, because during this …

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 …

Background Weed recognition is key for automatic weeding, which is a challenging problem.
Weed recognition is mainly based on different features of crop images. The extracted image …

Precision herbicide application can substantially reduce herbicide input and weed control
cost in turfgrass management systems. Intelligent spot-spraying system predominantly relies …

Deep Learning is playing an important role in big data processing for more accurate
modeling of common productive processes. It is being widely used in artificial vision …

The accurate and rapid acquisition of crop and weed information is an important prerequisite
for automated weeding operations. This paper proposes the application of a network model …