Abstract
The development of approaches to determine the spatial variability of nitrogen (N) into coffee leaves is essential to increase productivity and reduce production costs and environmental impacts associated with excessive N applications. Thus, this study aimed to assess the potential of the Random Forest (RF) machine learning method applied to vegetation indices (VI) obtained from Remotely Piloted Aircraft (RPA) images to measure the N content in coffee plants. A total of 10 VI were obtained from multispectral images by a camera attached to a rotary-wing RPA. The RGB orthomosaic was used to determine sampling points at the crop area, which were ranked by N levels in the plants as deficient, critical, or sufficient. The chemical analysis of N content in the coffee leaves, as well as the VI values in sample points, were used as input parameters for the image training and its classification by the RF. The suggested model has shown global accuracy and a kappa coefficient of up to 0.91 and 0.86, respectively. The best results were achieved using the Green Normalized Difference Vegetation (GNDVI) and Green Optimized Soil Adjusted Vegetation Index (GOSAVI). In addition, the model enabled the evaluation of the spatial distribution of N in the coffee trees, as well as quantification of N deficiency in the crop for the whole area. The GNDVI and GOSAVI allowed the verification that 22% of the entire crop area had plants with N deficiency symptoms, which would result in a reduction of 78% in the amount of N applied by the producer.
Highlights
Introduction published maps and institutional affilTo succeed in coffee production, good fertility of the soil, along with an intense fertility care program is necessary [1]
Based on the hypothesis that the learning method Random Forest applied to vegetation indices obtained from Remotely Piloted Aircraft (RPA) can contribute to the more efficient management of the nitrogen content in coffee crops, the objectives of this study were (i) to map the spatial variability of the nitrogen content in the coffee plantations, (ii) quantify the deficiency of nitrogen in coffee plants, and (iii) determine the most efficient vegetation index to predict
In the third step, values of the vegetation indices and chemical analysis of N in the leaves in each sample point were used as input parameters to calibrate the algorithm Random Forest and classify the images in three N content categories
Summary
Introduction published maps and institutional affilTo succeed in coffee production, good fertility of the soil, along with an intense fertility care program is necessary [1]. Without the proper assessment of soil fertility, nutritional deficiencies will affect the survival and productivity of coffee plants [2,3]. Among the essential nutrients that are necessary for coffee crops, nitrogen (N) is considered the one that limits the development and productivity of the coffee [4]. The proper management of N in coffee crops is still a challenging task for most producers. Cases of both excessive and deficient N application are problems in coffee production [6]. Excessive application of nitrogen fertilizers is a common practice among small and big producers, being the main cause of low-efficiency fertilization, reducing the iations
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