Accurate classification of weed species in crop plants plays a crucial role in precision
agriculture by enabling targeted treatment. Recent studies show that artificial intelligence …

Because weeds compete directly with crops for moisture, nutrients, space, and sunlight, their
monitoring and control is an essential necessity in agriculture. The most important step in …

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 …

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 …

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 …

Weed identification is fundamental toward developing a deep learning-based weed control
system. Deep learning algorithms assist to build a weed detection model by using weed and …

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

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 …

Monitoring crops and weeds is a major challenge in agriculture and food production today.
Weeds compete directly with crops for moisture, nutrients, and sunlight. They therefore have …

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 …