Avaliação do estado nutricional de nitrogênio e estimativa da produtividade de biomassa de trigo por meio de mineração de dados de sensoriamento remoto

Abstract

Estimating biomass productivity in agriculture is a key part in crop management, providing information that can help the complex decision making in the field. Nitrogen (N), for being a nutrient that participates in the structure and vital cellular functions to the plant, has a close correlation with biomass productivity, mainly in wheat crop (Triticum aestivum L.). Remote sensing (RS), which consists of acquiring information from an object without contact between the sensor and the target, is a widely employed technique in estimating biomass and nutritional status of N. There are three RS platforms for obtaining data: orbital, with satellites; aerial, with aircraft, helicopters and remotely piloted aircraft (RPA); and terrestrial, with optical sensors and spectral radiometers. When determining biomass productivity and N foliar content estimation models, both RS platforms are employed, and commercial products for these purposes already exists. However, there is a lack of information regarding the efficiency of the three platforms in the same experimental area. Traditionally, predictive models with RS data in agriculture are generated by classic statistical techniques, such as linear regression. However, data mining (DM) techniques can provide more relevant results. Due to its generalization capacity and feature of creating linear and nonlinear models, support vector machine for regression (SVR) is a DM technique with intensive use over RS data. The goals of this work were: (i) to evaluate the correlation between data obtained from the three RS platforms for estimating dry biomass productivity and N concentration in wheat leaves, and (ii) to compare the results obtained with a classical linear regression technique against those of the SVR technique. Were cultivated wheat plants, TBIO Sinuelo variety, in different environments involving distinct management of nitrogen fertilization. The sensors evaluation was performed in two ways: (i) with random samples at different wheat crop development stages for each nitrogen fertilization treatment, aiming to verify the sensor ability to detect variability in areas with the same treatment, and (ii) considering the mean value of the samples in each treatment, evaluating the ability of the sensor to detect the differences caused by varied management of nitrogen fertilization. The results showed that data generated by the equipment (GREENSEEKER terrestrial sensor, RAPIDEYE satellites and RPA EBEE) displayed correlation with dry biomass productivity and N concentration in wheat leaves. More expressive correlation coefficients (r) were obtained with SVR against those of linear regression in the data obtained with all equipment used. Considering the approach with the random samples in the field, data generated with the RPA EBEE showed a closer correlation with the biomass estimation and the foliar concentration of N. When considering the mean value of nitrogen fertilization treatments, both RPA EBEE and RAPIDEYE satellites presented similar results for estimating biomass productivity, however, the RPA EBEE provided results slightly higher than those obtained with the RAPIDEYE satellites for the prediction of N foliar content. It was concluded that, for estimating the biomass productivity and the N concentration in the wheat leaves RPA EBEE platform is more efficient than the terrestrial (GREENSEEKER) and orbital platforms (RAPIDEYE satellites) when there is greater variability in the study area. Also, SVR was a more efficient technique than linear regression for data analysis of the three platforms: orbital, aerial and terrestrial.