The aim of the proposed work is to review the various AI techniques (fuzzy logic (FL),
artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO) …

Recently, agriculture has gained much attention regarding automation by artificial
intelligence techniques and robotic systems. Particularly, with the advancements in machine …

Weeds are among the major factors that could harm crop yield. With the advances in
electronic and information technologies, machine vision combined with image processing …

Reducing the use of agrochemicals is an important component toward sustainable
agriculture. Robots that can perform targeted weed control offer the potential to contribute to …

Deep learning methods such as convolutional neural networks (CNN) have become popular
for addressing crops and weeds classification problems in agricultural robotics. However, to …

Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient.
The high-throughput field phenotyping systems and in particular robotic systems play an …

Agricultural robots rely on semantic segmentation for distinguishing between crops and
weeds to perform selective treatments and increase yield and crop health while reducing the …

Deep learning has brought a huge improvement in the area of machine learning in general
and most particularly in computer vision. The advancements of deep learning have been …

Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use
of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead …

This paper describes a light detection and ranging (LiDAR)‐based autonomous navigation
system for an ultralightweight ground robot in agricultural fields. The system is designed for …