How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

Abstract

Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.

Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control

“…The following steps were followed in order to obtain the proposed mechanisms in Sections 4.6 and 4.7, and in order to verify the plausibility of these mechanisms for weeding operation: (1) the HGA-based and QNM-based methods were implemented in MATLAB ® (the MathWorks Inc., Natick, MA, USA) to synthesize two different mechanisms. (2) To validate these mechanisms, some design indicators of them, such as tracking error function, transmission angle's deviation from right angle (90 degrees) and the effective stroke of their blades, were compared with each other and with other existing studies employing different methodologies to design a four-bar mechanism for weed management. The importance of these design indicators was described in Sections 2.2 and 3.…”

“…In smart farming, the presence of weeds is one of the major problems that contribute to yield losses in terms of the agricultural production level, and they should be removed [1,2]. Overall, weeds are more harmful than other biotic factors, such as animal pests and pathogens, and have the potential to reduce the crop yield up to 34%, while this quantity is 18% and 16% for animal pests and pathogens, respectively [3,4].…”

Perspective Chapter: Perspectives on Pathogenic Plant Virus Control with Essential Oils for Sustainability of Agriculture 4.0

“…Interesting techniques, advanced technologies of automated and robotic systems are developed for precision agriculture and plant management in open fields. Plant health monitoring by remote sensing technique of unmanned aerial vehicle (UAV) or drone and ground robot (unmanned ground vehicle, UGV) can be applied for various agricultural management including crop monitoring [65], field mapping [66], plant population counting [67], weed management [68], biomass estimation [69], crop nutrient diagnosis [70], plant disease diagnosis and detection [71], and spraying [72]. Tillett and Hague [73] reported that a machine vision system could detect and remove weeds up to 80% as well as weeds could serve as susceptible hosts and reservoir alternative hosts of pathogens and their vectors.…”

Weed Detection in Maize Fields by UAV Images Based on Crop Row Preprocessing and Improved YOLOv4

“…Excessive weeds in maize fields affect the growth and yield of maize. Weeds not only compete with crops for living space but also lead to the spread of diseases and pests, resulting in crop failure [2][3][4]. Therefore, we need to identify weeds in maize fields and ascertain their distribution and quantity.…”