Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management
Nenhuma Miniatura disponível
Data
2023-01-01
Autores
Baio, Fábio Henrique Rojo
Santana, Dthenifer Cordeiro [UNESP]
Teodoro, Larissa Pereira Ribeiro
Oliveira, Izabela Cristina de [UNESP]
Gava, Ricardo
de Oliveira, João Lucas Gouveia
Silva Junior, Carlos Antonio da
Teodoro, Paulo Eduardo [UNESP]
Shiratsuchi, Luciano Shozo
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
Predicting maize yield using spectral information, temperature, and different irrigation management through machine learning algorithms provide information in a fast, accurate, and non-destructive way. The use of multispectral sensor data coupled with irrigation management in maize allows further exploration of water behavior and its relationship with changes in spectral bands presented by the crop. Thus, the objective of this study was to evaluate, by means of multivariate statistics and machine learning techniques, the relationship between irrigation management and spectral bands in predicting maize yields. Field experiments were carried out over two seasons (first and second seasons) in a randomized block design with four treatments (control and three additional irrigation levels) and eighteen sample repetitions. The response variables analyzed were vegetation indices (IVs) and crop yield (GY). Measurement of spectral wavelengths was performed with the Sensefly eBee RTK, with autonomous flight control. The eBee was equipped with the Parrot Sequoia multispectral sensor acquiring reflectance at the wavelengths of green (550 nm ± 40 nm), red (660 nm ± 40 nm), red-edge (735 nm ± 10 nm), and NIR (790 nm ± 40 nm). The blue length (496 nm) was obtained by additional RGB imaging. Data were subjected to Pearson correlations (r) between the evaluated variables represented by a correlation and scatter plot. Subsequently, the canonical analysis was performed to verify the interrelationship between the variables evaluated. Data were also subjected to machine learning (ML) analysis, in which three different input dataset configurations were tested: using only irrigation management (IR), using irrigation management and spectral bands (SB+IR), and using irrigation management, spectral bands, and temperature (IR+SB+Temp). ML models used were: Artificial Neural Network (ANN), M5P Decision Tree (J48), REPTree Decision Tree (REPT), Random Forest (RF), and Support Vector Machine (SVM). A multiple linear regression (LR) was tested as a control model. Our results revealed that Random Forest has higher accuracy in predicting grain yield in maize, especially when associated with the inputs SB+IR and SB+IR+Temp.
Descrição
Palavras-chave
computational intelligence, multispectral bands, random forest, remote sensing, UAV imagery
Como citar
Remote Sensing, v. 15, n. 1, 2023.