The relations of leaf area index with the spray quality and efficacy of cotton defoliant spraying using unmanned aerial systems (UASs)

Liao, Juan; Zhang, Ying; Luo, Xiwen; Zhou, Zhiyan; Zang, Yu; Wang, Pei; Hewitt, Andrew

doi:10.1016/j.compag.2020.105228

Cited by 15 publications

(7 citation statements)

References 22 publications

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“…BPNN is a powerful ML algorithm that has the advantages of strong non-linearity and self-organization ability when dealing with complex and nonlinear approximation problems 45 , 46 . Compared with the LR models, the three-layer BPNN model used in this study has significantly improved the LAI estimation performance in the whole growth period.…”

Section: Discussionmentioning

confidence: 99%

Estimating leaf area index of maize using UAV-based digital imagery and machine learning methods

Yang

Song

et al. 2022

Sci Rep

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Leaf area index (LAI) is a fundamental indicator of crop growth status, timely and non-destructive estimation of LAI is of significant importance for precision agriculture. In this study, a multi-rotor UAV platform equipped with CMOS image sensors was used to capture maize canopy information, simultaneously, a total of 264 ground‐measured LAI data were collected during a 2-year field experiment. Linear regression (LR), backpropagation neural network (BPNN), and random forest (RF) algorithms were used to establish LAI estimation models, and their performances were evaluated through 500 repetitions of random sub-sampling, training, and testing. The results showed that RGB-based VIs derived from UAV digital images were strongly related to LAI, and the grain-filling stage (GS) of maize was identified as the optimal period for LAI estimation. The RF model performed best at both whole period and individual growth stages, with the highest R2 (0.71–0.88) and the lowest RMSE (0.12–0.25) on test datasets, followed by the BPNN model and LR models. In addition, a smaller 5–95% interval range of R2 and RMSE was observed in the RF model, which indicated that the RF model has good generalization ability and is able to produce reliable estimation results.

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Section: Discussionmentioning

confidence: 99%

Estimating leaf area index of maize using UAV-based digital imagery and machine learning methods

Yang

Song

et al. 2022

Sci Rep

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“…Moreover, height and downwind distance of the wind tunnel should be sufficient to contain enough sampling equipment or collectors. According to Table 3, the length of the UASS is generally greater than 2 m, the height range lies between 0.5 and 0.7 m, and the size range of the rotor is 0.53-1.19 m. According to the specifications of the wind tunnel for spray testing mentioned in the literature (Ling et al, 2018;Ding et al, 2019;Liao et al, 2019;Wang et al, 2020,b), the width of most wind tunnel cross-sections ranges between 1.2 and 3 m, while the height is 1.1-2 m high. As the length of the rotor itself interferes with the current cross-sectional dimensions of the wind tunnel, the whole machine or the rotor are difficult to place inside the wind tunnel.…”

Section: Standardization Of Measurement Methodsmentioning

confidence: 99%

Characteristics of unmanned aerial spraying systems and related spray drift: A review

et al. 2022

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Although drift is not a new issue, it deserves further attention for Unmanned Aerial Spraying Systems (UASS). The use of UASS as a spraying tool for Plant Protection Products is currently explored and applied worldwide. They boast different benefits such as reduced applicator exposure, high operating efficiency and are unconcerned by field-related constraints (ground slope, ground resistance). This review summarizes UASS characteristics, spray drift and the factors affecting UASS drift, and further research that still needs to be developed. The distinctive features of UASS comprise the existence of one or more rotors, relatively higher spraying altitude, faster-flying speed, and limited payload. This study highlights that due to most of these features, the drift of UASS may be inevitable. However, this drift could be effectively reduced by optimizing the structural layout of the rotor and spraying system, adjusting the operating parameters, and establishing a drift buffer zone. Further efforts are still necessary to better assess the drift characteristics of UASS, establish drift models from typical models, crops, and climate environments, and discuss standard methods for measuring UASS drift.

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“…Leaf area index (LAI), canopy height and width, and canopy shape can affect the spray deposition as a ratio between the actual and expected deposit (Campos et al, 2019). Liao et al (2020) showed significant correlations between LAI and spray quality and efficacy in cotton defoliant spraying using three different types of rotary-wing UAVs. Meng et al (2019) discovered that UAV spraying of harvest aids resulted in poor penetration in sections with a high density of plants and plants with high LAI.…”

Section: Canopy Morphologymentioning

confidence: 99%

A Review of the Current Unmanned Aerial Vehicle Sprayer Applications in Precision Agriculture

Delavarpour,

Koparan,

Zhang

et al. 2023

Journal of the ASABE

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Highlights A comprehensive review of the current Unmanned Aerial Vehicle Sprayer in precision agriculture applications. Comparison of manned and unmanned aerial sprayers in precision agriculture. Latest developments of commercialized UAV sprayers available on the market. Abstract. Unmanned Aerial Vehicles (UAVs) are becoming more broadly used for improving agricultural spraying applications. However, compared with the nearly 100 years of data accumulated on manned aerial applications, UAV sprayers are relatively new, and associated technologies are in the early stages of development. The objective of this paper is to give a comprehensive review of the current UAV spraying platforms with a comparison to manned aerial sprayers and a discussion of their application, performance, and efficiency. A total of 213 peer-reviewed and non-peer-reviewed articles, extension papers, government websites, and company websites were reviewed and cited in this study. We also discuss factors that could influence the effectiveness of aerial spraying applications, such as release height, wind speed, vortex strength, and droplet size. Finally, we review the latest UAV sprayers available worldwide and present technology gaps in those platforms. We highlight areas that require improvement, particularly in autonomous navigational controllers and spraying systems. Keywords: Droplet distribution, Plant protection, Precision agriculture, Spot spraying, UAV sprayer.

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The relations of leaf area index with the spray quality and efficacy of cotton defoliant spraying using unmanned aerial systems (UASs)

Cited by 15 publications

References 22 publications

Estimating leaf area index of maize using UAV-based digital imagery and machine learning methods

Estimating leaf area index of maize using UAV-based digital imagery and machine learning methods

Characteristics of unmanned aerial spraying systems and related spray drift: A review

A Review of the Current Unmanned Aerial Vehicle Sprayer Applications in Precision Agriculture

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