The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …

Deep learning has emerged as a potential tool for crop yield prediction, allowing the model
to automatically extract features and learn from the datasets. Meanwhile, smart farming …

Deep Learning (DL) has been described as one of the key subfields of Artificial Intelligence
(AI) that is transforming weed detection for site-specific weed management (SSWM). In the …

The increasing world population makes it necessary to fight challenges such as climate
change and to realize production efficiently and quickly. However, the minimum cost …

The key elements that underpin food security require the adaptation of agricultural systems
to support productivity increases while minimizing inputs and the adverse effects of climate …

Background and aim: Machine learning methods are examined by many researchers to
identify weeds in crop images captured by drones. However, metaheuristic optimization is …

Timely and accurate information on the spatial distribution of urban trees is critical for
sustainable urban development, management and planning. Compared with satellite-based …

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

Abstract Purpose of Review The goal of this review is to provide an overview of current
robotic approaches to precision weed management. This includes an investigation into …

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 …