  The number of objects is considered an important factor in a variety of tasks
in the agricultural domain. Automated counting can improve farmers decisions
regarding yield estimation, stress detection, disease prevention, and more. In
recent years, deep learning has been increasingly applied to many
agriculture-related applications, complementing conventional computer-vision
algorithms for counting agricultural objects. This article reviews progress in
the past decade and the state of the art for counting methods in agriculture,
focusing on deep-learning methods. It presents an overview of counting
algorithms, metrics, platforms, and sensors, a list of all publicly available
datasets, and an in-depth discussion of various deep-learning methods used for
counting. Finally, it discusses open challenges in object counting using deep
learning and gives a glimpse into new directions and future perspectives for
counting research. The review reveals a major leap forward in object counting
in agriculture in the past decade, led by the penetration of deep learning
methods into counting platforms.
