Computer Vision in Smart Agriculture and Crop Management

Preface

As editors, we feel privileged to have been asked to edit the 1st edition of Computer Vision in Smart Agriculture and Crop Management. The need for a sufficient food supply and societal demands has driven the evolution of smart agriculture. Smart agriculture leverages information and technology to assist farmers, particularly in remote locations. A key component of advancing agricultural automation systems is Computer Vision (CV) technology. By integrating computer vision with other intelligent systems, such as deep learning, it is possible to manage agricultural productivity using massive datasets, addressing persistent agricultural challenges and enhancing the economic, general, and operational efficiency of agricultural automation tools. Automation in agriculture offers significant benefits, including reduced costs, high performance, and increased accuracy, all contributing to sustained growth. Aerial imaging is commonly used during the growing season to monitor crops. High-throughput phenotyping research is expected to provide substantial insights into yield attributes on a large scale, supporting effective crop management decisions.

Machine learning and computer vision techniques help farmers distinguish between fertile soil, natural remedies, and pest control measures. These technologies can also assess the color, size, thickness, and surface texture of crops to detect impurities in agricultural yields and identify contaminated food products. Computer vision-based ...