Journal of Agriculture and Food Research, Journal Year: 2024, Volume and Issue: 18, P. 101208 - 101208

Published: July 6, 2024

Nitrogen is the most important nutritional element during vegetative growth phase of pineapple crop; however, its presence in soil insufficient to meet plant demands. In this study, nine machine learning techniques were validated estimate total nitrogen (TN) content MD2 crops from data multiple sources. These sources included multispectral images captured by an unmanned aerial vehicle (UAV); situ sensors, which collected information on ecological factors such as pH, temperature, solar radiation, relative humidity, moisture, wind speed and direction, well SPAD values indicating leaf chlorophyll content. Total taken tissue samples, then analyzed a laboratory. To introduce variability, complete randomized block experimental design was implemented, applying five different treatments blocks, each with 12 replications, 6-month period crop located Tauramena, Colombia. address inherent variability agricultural environmental data, dimensionality reduced using Principal Component Analysis (PCA). addition, regularization applied, including cross-validation, feature selection, boost methods, L1 (Lasso) L2 (Ridge) regularization, hyperparameter optimization. strategies generated more robust accurate models, multilayer perceptron regressor (MLP regressor) extreme gradient boosting (XGBoost) algorithms standing out. On first sampling date, XGBoost achieved R2 86.98 %, being highest. following dates, MLP 59.11 % second date; 68.00 third last 69.4 %. results indicate that integration use models could greatly improve precision nitro-gen (N) diagnostics crops, especially real-time applications. findings highlight promising potential developing integrate multisensor fusion for various applications agriculture.

Language: Английский