Automatic identification and selective spraying of weeds (such as dock) in grass can provide
very significant long-term ecological and cost benefits. Although machine vision (with …

Weeds are a major threat to crops and are responsible for reducing crop yield worldwide. To
mitigate their negative effect, it is advantageous to accurately identify them early in the …

Context Most weed species can adversely impact agricultural productivity by competing for
nutrients required by high-value crops. Manual weeding is not practical for large cropping …

The increasing deployment of deep learning models for distinguishing weeds and crops has
witnessed notable strides in agricultural scenarios. However, a conspicuous gap endures in …

Abstract Machine learning and deep learning are subsets of Artificial Intelligence that have
revolutionized object detection and classification in images or videos. This technology plays …

Reducing the use of pesticides through selective spraying is an important component
towards a more sustainable computer-assisted agriculture. Weed identification at early …

Robotic weed control has seen increased research of late with its potential for boosting
productivity in agriculture. Majority of works focus on developing robotics for croplands …

Weed control is essential in agriculture since weeds reduce yields, increase production cost,
impede harvesting, and degrade product quality. As a result, it is indeed critical to recognize …

Traditional means of on-farm weed control mostly relies on manual labor. This process is
time consuming, costly and contributes to major yield losses. The conventional application of …

Site-specific weed detection and management is a crucial approach for crop production
management and herbicide contamination mitigation in precision agriculture. With the …