Deep learning (DL) is a robust data-analysis and image-processing technique that has
shown great promise in the agricultural sector. In this study, 129 papers that are based on …

Abstract Machine learning has created new opportunities for data-intensive study in
interdisciplinary domains as a result of the advancement of big data technologies and high …

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 …

Robotics and variable rate technology (VRT) have shown great potential for site-specific
weed management (SSWM), but these technologies face several challenges like accurate …

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 …

With the escalating global challenges of food security and resource sustainability, innovative
solutions like deep learning and computer vision are transforming agricultural practices by …

Applications of deep-learning models in machine visions for crop/weed identification have
remarkably upgraded the authenticity of precise weed management. However, compelling …

In the perspective of logic mining, the attribute selection, and the objective function of the
best logic is the two main factors that identifies the effectiveness of our proposed logic …

The rapid growth of the world's population has put significant pressure on agriculture to meet
the increasing demand for food. In this context, agriculture faces multiple challenges, one of …

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to
transform various sectors. BC can offer decentralized and secure data storage, while CV …