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A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices

2020; Elsevier BV; Volume: 178; Linguagem: Inglês

10.1016/j.compag.2020.105791

1872-7107

Ana Paula Marques Ramos, Lucas Prado Osco, Danielle Elis Garcia Furuya, Wesley Nunes Gonçalves, Dthenifer Cordeiro Santana, Larissa Pereira Ribeiro Teodoro, Carlos Antônio da Silva, G. Gasparotto, Jonathan Li, Fábio Henrique Rojo Baio, José Marcato, Paulo Eduardo Teodoro, Hemerson Pistori,

Spectroscopy and Chemometric Analyses

Random Forest (RF) is a machine learning technique that has been proved to be highly accurate in several agricultural applications. However, to yield prediction, how much this technique may be improved with the adoption of a ranking-based strategy is still an unknown issue. Here we propose a ranking-based approach to potentialize the RF method for maize yield prediction. This approach is based on the correlation parameter of individual vegetation indices (VIs). The VIs were individually ranked based on a merit metric that measures the improvement on the Pearson’s correlation coefficient by using RF against a baseline method. As a result, only the most relevant VIs were considered as input features to the RF model. We used 33 VIs extracted from multispectral UAV-based (unmanned aerial vehicle) imagery. The multispectral data were generated with two different sensors: Sequoia and MicaSense; during the 2017/2018 and 2018/2019 crop seasons, respectively. Amongst all the evaluated indices, NDVI, NDRE, and GNDVI were the top three in the ranking-based analysis, and their combination with RF increased the maize yield prediction. Our approach also outperformed other known machine learning methods, like support vector machine and artificial neural network. Additive regression, using the RF as the base weak learner, provided a higher accuracy with a correlation coefficient and MAE (Mean Absolute Error) of 0.78 and 853.11 kg ha−1, respectively. We conclude that the ranking-based strategy of VIs is appropriate to predict maize yield using machine learning methods and data derived from multispectral images. We demonstrated that our approach reduces the number of VIs needed to determine a high accuracy and relative low MAE, and the approach may contribute to decision-making actions, resulting in accurate management of maize fields.