Advance control strategies using image processing, UAV and AI in agriculture: a review

Abstract

Purpose The purpose of this paper is to provide an overview of smart agriculture systems and monitor and identify the technologies which can be used for deriving traditional agriculture system to modern agriculture system. It also provides the reader a broad area to work for the advancement in the field of agriculture and also explains the use of advanced technologies such as spectral imaging, robotics and artificial intelligence (AI) in the field of agriculture. Design/methodology/approach Smart uses of modern technologies were reviewed in the field of agriculture, which helps to monitor stress levels of plants and perform operations according to requirements. Operations can be irrigation, pests spray, monitoring crops, monitoring yield production, etc. Based on the literature review, a smart agriculture system is suggested. The parameters studied were spectral image processing, AI, unmanned aerial vehicle (UAVs) (fixed and rotatory), water or soil moisture, nutrients and pesticides. Findings The use of autonomous vehicles and AI techniques has been suggested through which the agriculture system becomes much more efficient. The world will switch to the smart agriculture system in the upcoming era completely. The authors conclude that autonomous vehicle in the field of science is time-saving and health efficient for both plants and workers in the fields. The suggested system increases the productivity of crops and saves the assets as well. Originality/value This review paper discusses the various contemporary technologies used in the field of agriculture and it will help future researchers to build on this research. This paper reveals that the UAVs along with multispectral, hyperspectral or red, green and blue camera (depends on the need) and AI are more suitable for the advancement of agriculture and increasing yield rate.

A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure

“…The cluster ‘IMAGE PROCESSING’ ( Figure 4 f) presents strong linkages with agricultural management tools (e.g., ‘MACHINE VISION’ and ‘COMPUTER VISION’) that provide data for the farmer’s decision-making. Previous literature reviews explored advanced control strategies and image processing techniques in Digital Agriculture (see Ngugi et al (2021) [ 125 ], Syeda et al (2021) [ 126 ], and Sohail et al (2020) [ 127 ]). Zhao et al [ 128 ] used machine vision to exploit color images to detect immature green citrus.…”

Categorizing agricultural crops using unmanned aerial vehicle images: An in-depth review

“…This model backbone affected the accuracy, but the depth of the backbone increased, and the accuracy of the segmentation did not increase. The outcomes showed that the majority of segmentation models used either ResNet34 or ResNet50 as their suitable backbone (Syeda et al 2021). The author utilized UAV images to develop an enhanced YOLOv5-based early wildfire smoke detection system.…”

Economic Fruit Trees Recognition in Hillsides: A CNN-Based Approach Using Enhanced UAV Imagery

“…In recent decades, the utilization of image processing has expanded its reach into various sectors including education [1], healthcare [2], agriculture [3], unmanned aerial vehicles (UAVs) [4], and numerous other domains. Advancements in technology have helped the agriculture industry apply a profound transformation to prosper farming and eliminate challenges and issues [5]. Employing UAVs in agriculture has made a revolution because of providing real-time data for farmers to take action on time with more productivity (e.g., [6], [7]).…”