UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area  
[PERSON]  
1 Department of Agricultural Engineering, Federal Rural University of Pernambuco, [PERSON], Dois Irmao, Recife 52171-900, PE, Brazil; [EMAIL_ADDRESS] (O.B.d.A. );  
1 [PERSON]  
1 [PERSON]  
1 [PERSON]  
2 Department of Civil Engineering, MRAE--Marine and Environmental Sciences Centre, ARNET--Aquatic Research Network, Faculty of Science and Technology, University of Coimbra, [PERSON], Polo II, 3030-788 Coimbra, Portugal; plimadbec.uc.pt  
3 Academic Unit of Serran Lindada, Federal Rural University of Pernambuco, Averinda Gregoito Ferraz Nogueira, Serra Talahda 5690-535, PE, Brazil; [EMAIL_ADDRESS]  
4 Department of Statistics and Informatics, Federal Rural University of Pernambuco, [PERSON], Dois Irmao, Recife 52171-900, PE, Brazil; [EMAIL_ADDRESS] (V.W.C.d.M. );  
4 FORGEmos  
the forage cactus stands out. When irrigated using conservation techniques, it shows high development and production, yielding positive results when grown intercropped with some other crop species [6]. The ability to accurately forecast the crop types is highly necessary for estimating cultivated area, predicting yield volume, and determining crop water requirements [7]. Detailed Monitoring of agricultural lands is pivotal in precision agriculture, contributing to enhancing crop production and water conservation [8]. Remote sensing, within the precision agriculture approach, provides crucial information for monitoring natural resources, including crop growth, land use, soil moisture, plant health, and crop forecasting [9]. For widespread adoption in agriculture, it is essential to rely on measured data and integrate sources to ensure practical robustness [10]. Unmanned Aerial Vehicles (UAVs) equipped with multispectral (MS) sensors offer several benefits in precision agriculture, enabling the acquisition of high-resolution data that capture the spatial variability of attributes and crops [11, 12, 13]. [PERSON] et al. [14] compared both RGB and multispectral imagery from UAV to map Stay Green (SG) phenotyping of diversified wheat germplasm. Although visible Red-Green-Blue (RGB) images could be valuable information, spectral indices containing red edge or near-infrared band were more effective for proper crop classification. Unmanned Aerial Vehicles (UAVs) in agriculture allow for capturing aerial images with high spatial resolution due to their low flight altitude [15, 16, 17, 12]. The adoption of Artificial Intelligence (AI) techniques optimizes productivity and irrigation management by identifying zones requiring interventions and reducing water wastage [18]. Machine learning may be associated with resource savings, such as in autonomous irrigation, where the algorithm adjusts the irrigation volume and time based on crop needs, optimizing irrigation application, thus enhancing water productivity and agricultural sustainability [19], being increasingly strategic for implementation of Sustainable Objectives Goals [20]. Studies such as [PERSON] et al. [21] highlight the potential of remote sensing in monitoring crop health, weed control, and estimating evapotranspiration. [PERSON] et al. [22] developed machine learning (ML) applications for crop classification to identify invasive plants in Pakistan, demonstrating the high performance of Gaussian Mixture Model (GMM), support vector machine (SVM), and Random Forest (RF) algorithms. Meanwhile, [PERSON] and [PERSON] [23] detected weeds from high-resolution images, emphasizing the potential of Unmanned Aerial Vehicles (UAVs) in agricultural research. [PERSON] et al. [24] used UAV hyperspectral data to investigate methods for crop classification (16 crop species) and status monitoring (tea plant and rice growth), including K-nearest Neighbors (KNN), Random Forest (RF), and a genetic algorithm coupled with a support vector machine (GA-SVM).