Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …

In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …

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 …

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 …

In the face of increasing agricultural demands and environmental concerns, the effective
management of weeds presents a pressing challenge in modern agriculture. Weeds not only …

The precision spraying robot dispensing herbicides only on unwanted plants based on
machine vision detection is the most appropriate approach to ensure the sustainable agro …

Alternative non-chemical or chemical-reduced weed control tactics are critical for future
integrated weed management, especially for herbicide-resistant weeds. Through weed …

The past decade has witnessed the rapid development and adoption of machine and deep
learning (ML & DL) methodologies in agricultural systems, showcased by great successes in …

Automation, including machine learning technologies, are becoming increasingly crucial in
agriculture to increase productivity. Machine vision is one of the most popular parts of …

The past decade has witnessed many great successes of machine learning (ML) and deep
learning (DL) applications in agricultural systems, including weed control, plant disease …