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

Weeds challenge crops by competing for resources and spreading diseases, impacting crop
yield and quality. Effective weed detection can enhance herbicide application, thus reducing …

Traditional means of weed removal, such as human work or the use of pesticides, frequently
require significant amounts of effort, incur high expenses, and can negatively impact the …

Training machine learning (ML) models for artificial intelligence (AI) and computer vision-
based object detection process typically requires large, labeled datasets, a process often …

Mechanical weed management is a drudging task that requires manpower and has risks
when conducted within rows of orchards. However, intrarow weeding must still be conducted …

Maintenance of sufficiently healthy green leaf area after anthesis is key to ensuring an
adequate assimilate supply for grain filling. Tightly regulated age-related physiological …

Recent years have seen the emergence of various precision weed management
technologies in both research and commercial contexts. These technologies better target …

Deep learning (DL) inspired models have achieved tremendous success in locating target
weed species through bounding-box approach (single-stage models) or pixel-wise semantic …

Cotton is one of the world's most economically significant crops. Evaluating and monitoring
cotton crop growth play vital roles in precision agriculture. Unmanned aerial vehicle (UAV) …