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 …

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 …

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

Crop and weed discrimination in natural field environments is still challenging for
implementing automatic agricultural practices, such as weed control. Some weed control …

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 …

Accurate crop classification map is of great significance in various fields such as the survey
of agricultural resource, the analysis of existing circumstance on land application, the yield …

Artificial intelligence has been incorporated into modern agriculture to increase agricultural
output and resource efficiency. Utilizing deep learning, particularly convolutional neural …

In recent times, the Internet of Things (IoT) and Deep Learning Models (DLMs) can be
utilized for developing smart agriculture to determine the exact location of the diseased part …

Due to current global population growth, resource shortages, and climate change, traditional
agricultural models face major challenges. Precision agriculture (PA), as a way to realize the …

Recently, precision weed management has emerged as a promising solution for reducing
the use of herbicides which is hazardous to crops and human health. Thus, accurate …