CLASSIFICATION OF IRRIGATION MANAGEMENT PRACTICES IN MAIZE HYBRIDS USING MULTISPECTRAL SENSORS AND MACHINE LEARNING TECHNIQUES - João L. G de Oliveira; Dthenifer C. Santana; Izabela C de Oliveira; Ricardo Gava; Fábio H. R. Baio; Carlos A da Silva Junior; Larissa P. R. Teodoro; Paulo E. Teodoro; Job T de Oliveira (Engenharia Agrícola)
ABSTRACT The integration multispectral sensors with machine learning algorithms has demonstrated increasing efficacy in the classification of various maize morphophysiological characteristics. The hypothesis of this study is that maize plants subjected to different irrigation management practices exhibit distinct spectral behaviors, allowing for their classification through machine learning modeling. Thus, the objective of this study is to classify maize hybrids in different irrigation management practices using multispectral images. This involves identifying the most effective machine learning algorithms and inputs variables that enhance model performance for accurate classification. The experiment was conducted at the experimental facility of the Federal University of Mato Grosso do Sul, in Chapadão do Sul – MS. Seven hybrids were evaluated: H1 (AS 1868), H2 (DKB 360), H3 (FS 615 PWU), H4 (K 7510 VIP3), H5 (NK 520 VIP3), H6 (P 3858 PWU), and H7 (SS 182E VIP3). These hybrids were subjected to irrigation and non-irrigation management practices. Sixty days after crop emergence, images were captured in the blue (475 nm, B_475), green (550 nm, G_550), red (660 nm, R_660), red edge (735 nm, RE_735), and near-infrared (790 nm, NIR_790) bands using the Sensefly eBee RTK fixed-wing Remotely Piloted Aircraft, equipped with a Parrot Sequoia multispectral sensor and RTK (Real-Time Kinematics) technology. Through the collected band data, the ESRI ArcGIS 10.5 geographic information system software was used to calculate 41 vegetation indices (VIs). Data were analyzed using machine learning techniques, testing six algorithms: Logistic Regression (RL), REPTree (DT), J48 Decision Trees (J48), Random Forest (RF), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). Three accuracy metrics were utilized to evaluate the algorithms in the classification of irrigation management: correct classifications (CC), Kappa coefficient and F-Score. The ANN and RF algorithms demonstrated better accuracy in classifying maize hybrids with respect to irrigation management. The use of Vegetation Indices (IVs) and Spectral Bands + Vegetation Indices (SB+IVs) enhanced performance of these algorithms.