Residual Ash Mapping and Coffee Plant Development Based on Multispectral RPA Images

Lucas Santos Santana, Gabriel Araújo e Silva Ferraz,Mozarte Santos Santana,Nicole Lopes Bento, Josiane Maria da Silva,Rafael de Oliveira Faria

Remote Sensing(2024)

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Abstract
Residues mapping can provide essential information about soil chemical elements’ behaviors and contribute to possible interferences in coffee tree development. Thus, the research objective was to monitor plant residue burning effects by analyzing the chemical elements in ash, using soil analysis, and applying vegetative indices obtained by RPA images. The samples were submitted for conventional soil analysis and atomic emission spectrometry (pure ash). The RPA multispectral images were used to form thirty-one vegetative indices. Thus, at the soil and ash collection points, the index performance was evaluated for six months and divided into three collection times. Then, the data were statistically analyzed to evaluate which index best separated the plants in regions with ash and ash-free soil. The pure ash deposits revealed expressive presences of K, Ca, Mg, and Al in addition to pH elevation. In areas with ash, the high temperature at the burning time may have caused elemental chemical transformations in the Al composition, making this element unavailable in soil analysis. The vegetative indices showed a significant difference only in coffee four months after planting. Among the thirty-one evaluated indices, only twenty were satisfactory for ash analysis. The burning of plant residues promoted the neutralization of Al. In addition, ash deposits in the soil added some essential elements for plant development. Negatively, they raised the PH and made micronutrients unavailable. The best vegetative indices for ash monitoring were the Normalized Near Infrared Index (NNIRI) and Normalized Green Index (NGI). Prior ash mapping can contribute to localized application in macro, such as K and limestone, reusing the number of elements already deposited by burning vegetables.
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Key words
precision agriculture,remote sensing,sustainability,soil management,coffee farm
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