Work

Defoliation Categorization in Soybean with Machine Learning Algorithms and UAV Multispectral Data

Year: 2024

Type: article

Abstract: Traditional disease severity monitoring is subjective and inefficient. This study employs a Parrot multispectral sensor mounted on an unmanned aerial vehicle (UAV) to apply machine learning algorithms, such as random forest, for categorizing defoliation levels in R7-stage soybean plants. This research assesses the effectiveness of vegetation indices, spectral bands, and relative vegetation cover as input parameters, demonstrating that machine learning approaches combined with multispectral imagery can provide a more accurate and efficient assessment of Asian soybean rust in commercial soybean fields. The random forest algorithm exhibited satisfactory classification performance when compared to recent studies, achieving accuracy, precision, recall, F1-score, specificity, and AUC values of 0.94, 0.92, 0.92, 0.92, 0.97, and 0.97, respectively. The input variables identified as most important for the classification model were the WDRVI and MPRI indices, the red-edge and NIR bands, and relative vegetation cover, with the highest Gini importance index.

Source: Agriculture

DOI: 10.3390/agriculture14112088

Authors: Marcelo Araújo Junqueira Ferraz, Afrânio Gabriel da Silva Godinho Santiago, Adriano Teodoro Bruzi, Nelson Júnior Dias Vilela, Gabriel Araújo e Silva Ferraz

Institution: Universidade Federal de Lavras

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Zero hunger, Life on land

Open Access status: gold

Estimated APC paid: $2,955