Multispectral aerial images for the evaluation of maize crops

Abstract

<jats:p>ABSTRACT The combination of multispectral aerial images and computational processing is emerging as one of the solutions used in precision agriculture to observe the nutritional status of plants. The objective of this study is therefore to associate the nitrogen content and dry weight of the aerial part of maize plants (DW) with the vegetation indices obtained by multispectral aerial images (NDVIA and NDREA), and with the SPAD index and the Greenseeker NDVI, in the vegetative stage V6. To this end, randomized blocks in a factorial scheme of 6x4 (six nitrogen doses at the base and four different flight altitudes) were used, with three replications. The collected data was submitted to ANOVA with the F test (p = 0,05) and subsequent regression analysis. The study showed that it is possible to estimate the dry weight of the aerial part of maize plants and the nitrogen content in the leaves through the processing of multispectral aerial images, using the NDVI and NDRE spectral vegetation indices. The portable chlorophyll meter SPAD (model SPAD-502) also had promissing results in the estimation of nitrogen content, while the Greenseeker NDVI sensor accurately estimated nitrogen content and dry weight.</jats:p>

Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review

“…As highlighted in Table 2, most of the studies related to the use of VIs in maize research focused on NDVI index alone or in combination with other VIs. Generally, researchers used NDVI to discriminate maize crops [45][46][47], to predict yields [6,[48][49][50][51][52], to detect stress [53], and to make decisions related to fertilization [49]. While NDVI is a prominent choice, other VIs such as EVI and GNDVI were also employed in maize research, depending on the specific objectives and data available.…”

“…Promising results could be achieved also by using UAV data, as long as the operator of the equipment is highly trained and performs the requested adjustments according to each crop specificity. Hoss et al [48] assessed the NDVI of a maize crop by employing a DJI Matrice 200 RPA drone fitted with a multispectral camera. Their findings revealed that flying at higher altitudes during data collection (90 m and 120 m) yielded more accurate results compared to low altitudes (30 m and 60 m).…”

Mapping Soil and Pasture Attributes for Buffalo Management through Remote Sensing and Geostatistics in Amazon Biome

“…Therefore, above-ground forage mass parameters are often used as indicators of available forage quality and quantity for the animal unit, providing information for farm management [ 17 ]. Traditional forage mass analysis entails laborious and destructive actions [ 18 , 19 ]. These methods have low sampling precision, as they are based on random sample means without considering the spatial variation of biomass [ 20 , 21 ].…”

Estado de nitrogênio, índice SPAD, teor de açúcar e produtividade de genótipos de milho doce (Zea Mays L. Saccharata) em função de doses de nitrogênio

“…Destaca-se a utilização do medidor SPAD (Soil Plant Analysis Development) (modelo SPAD-502), ferramenta que mede o índice SPAD (Soil Plant Analysis Development) para avaliar de forma simples, rápida, e não destrutiva, o verde ou conteúdo relativo de clorofila das folhas (FELSEMBURGH e TRIBUZY, 2012). O clorofilômetro SPAD apresenta resultados promissores na estimativa do teor de nitrogênio na cultura do milho (HOSS et al, 2020;RHEZALI, A., & AISSAOUI, 2021) podendo ser ferramenta bastante eficaz na quantidade recomendada de N sem afetar a produtividade do milho (ISLA et al, 2020).…”