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 …

Deep learning methods have become an integral part of computer vision and machine
learning research by providing significant improvement performed in many tasks such as …

The excessive consumption of herbicides has gradually led to the herbicide resistance weed
phenomenon. Managing herbicide resistance weeds can only be explicated by applying …

Detection, segmentation and tracking of fruits and vegetables are three fundamental tasks
for precision agriculture, enabling robotic harvesting and yield estimation applications …

The past decade has witnessed many great successes of machine learning (ML) and deep
learning (DL) applications in agricultural systems, including weed control, plant disease …

Plant leaf diseases cause a decrease in crop yield and degrade the quality, which presents
the urgent need for leaf disease identification. Recently, deep learning technologies …

Robotics has been increasingly relevant over the years. The ever-increasing demand for
productivity, the reduction of tedious labor, and safety for the operator and the environment …

As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has
garnered increasing attention in recent years. While self-supervised learning is emerging as …

Computer vision can lead toward more sustainable agricultural production by enabling
robotic precision agriculture. Vision‐equipped robots are being deployed in the fields to take …

Detecting weeds at an early stage is crucial in reducing herbicide usage and preventing
significant losses in agricultural productivity. The emergence of new computer vision …