The use of vegetation index via remote sensing allows estimation of soybean application rate

Souza, Fernando Henrique Queiroz; Martins, Pedro Henrique Alves; Martins, Tulio Henrique Dresch; Teodoro, Paulo Eduardo; Baio, Fábio Henrique Rojo

doi:10.1016/j.rsase.2019.100279

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Data captured from UAVs is expressed using indices, including the vegetation index. Souza et al (2020) used active and passive sensors to obtain vegetation index maps to assess crop growth. The electromagnetic spectrum is a range of all types of electromagnetic radiation based on frequency and wavelength.…”

Section: Vegetation Indices For Paddy Growth Monitoringmentioning

confidence: 99%

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

Mohidem,

Jaafar,

Che’Ya

2024

JST

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Using a conventional approach via visual observation on the ground, farmers encounter difficulties monitoring the entire paddy field area, and it is time-consuming to do manually. The application of unmanned aerial vehicles (UAVs) could help farmers optimise inputs such as water and fertiliser to increase yield, productivity, and quality, allowing them to manage their operations at lower costs and with minimum environmental impact. Therefore, this article aims to provide an overview of the integration of UAV and multispectral sensors in monitoring paddy growth applications based on vegetation indices and soil plant analysis development (SPAD) data. The article briefly describes current rice production in Malaysia and a general concept of precision agriculture technologies. The application of multispectral sensors integrated with UAVs in monitoring paddy growth is highlighted. Previous research on aerial imagery derived from the multispectral sensor using the normalised difference vegetation index (NDVI) is explored to provide information regarding the health condition of the paddy. Validation of the paddy growth map using SPAD data in determining the leaf’s relative chlorophyll and nitrogen content is also being discussed. Implementation of precision agriculture among low-income farmers could provide valuable insights into the practical implications of this review. With ongoing education, training and experience, farmers can eventually manage the UAV independently in the field. This article concludes with a future research direction regarding the production of growth maps for other crops using a variety of vegetation indices and map validation using the SPAD metre values.

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Section: Vegetation Indices For Paddy Growth Monitoringmentioning

confidence: 99%

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

Mohidem,

Jaafar,

Che’Ya

2024

JST

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“…Within this range, studies have shown increased spray deposition with increased [2] or decreased [3] application rates for perennial plants. In annual plants, different results in deposition and control efficacy have also been reported at different application rates and droplet sizes [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis Applied to the Ground Application of Pesticides in the Corn Crop

León

Cunha

2023

AgriEngineering

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Including the correct combination of factors for the application technology of pesticides can improve their distribution on their targets. The aim of this work was to use multivariate analysis to study the effect size and the order of influence of three factors that interfere with pesticide application technology in corn crops. A 2 × 2 × 3 factorial experiment was conducted with two droplet size classes (fine and coarse), two application rates (80 and 150 L ha−1), and the presence of adjuvants (mineral oil one and two, and no adjuvant). A knapsack boom sprayer was used for the applications. Droplet deposition on the corn leaves was evaluated by detecting a tracer added to the spray via spectrophotometry and the droplet spectrum by analyzing water-sensitive papers. Univariate and multivariate statistical analyses were performed to integrate the variables analyzed. Droplet size has proven to be the most important factor in spraying planning, and the second factor is the application rate. With the association between fine droplets and higher application rates, a better performance was obtained in coverage, droplet density, and droplet deposition.

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“…Comparing different application rates, reports of increased deposition with higher (Derksen et al, 2008) and lower rates (Alves et al, 2020) were found, but the latter was also related with an increased droplet size (Fritz et al, 2006). The application rate should be regulated according to the crop foliage spray retention capacity; exceeding this level may result in runoff from the upper third of the field, with part of the spray still retaining in the lower third (Souza et al, 2020) or in the runoff to soil (Cunha et al, 2011b).…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis Applied to Spray Deposition in Ground Application of Phytosanitary Products in Coffee Plants

et al. 2021

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An adequate combination of factors involved in the technology used for phytosanitary product application contributes to an efficient spray deposition on the target. The objective of this study was to use multivariate analysis to characterize the magnitude of effects and the order of influence of three factors that interfere with the quality of phytosanitary product application in coffee plants. An entirely randomized design was adopted, with four repetitions, using a 2 × 2 × 3 factorial scheme, with two classes of droplets quality (fine and coarse), two application rates (250 and 400 L ha -1 ), and the use of adjuvants (with no adjuvant or with Fighter ® and Aureo ® adjuvants). The quality of the application was determined by jointly analyzing the spray deposition on three thirds of leaves, in their internal and external layers, the runoff to soil, coverage, droplet density, relative amplitude, and the volumetric median diameter. The results underwent analysis of variance (ANOVA) to measure the effect sizes (η 2 ). After testing the assumptions of multivariate analysis, clustering and principal component analyses were performed. The class of droplets was found to be the most influential factor in the quality of the phytosanitary product application (spray deposition and runoff to soil). When focusing on spray deposition on leaves, the second-most influential factor was the application rate and the relation between the application rate and the adjuvants. For the other variables, the second-most influential factor was the application rate.

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The use of vegetation index via remote sensing allows estimation of soybean application rate

Cited by 4 publications

References 32 publications

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

Multivariate Analysis Applied to the Ground Application of Pesticides in the Corn Crop

Multivariate Analysis Applied to Spray Deposition in Ground Application of Phytosanitary Products in Coffee Plants

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