The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen

Abstract

This study is based on the principle that vegetation indexes (VIs), derived from the RGB color model obtained from digital images, can be used to characterize spectral signatures and classify Brachiaria brizantha cv. Xaraés according to nitrogen status (N). From colorimetric data obtained from leaf blade images acquired in the fi eld, three artifi cial neural networks were evaluated according to the performance in the classifi cation of N status: Feedforward Backpropagation (FFBP), Cascade Forward Backpropagation (CFBP) and Radial Base function (RBFNN). Four N fertilization rates were applied to generate contrasting N contents in the plants. The youngest completely expanded leaves from 60 tillers were detached at each regrowth cycle of 28 days, thus their images and leaf N content were obtained. Samples were then classifi ed as defi cient (< 17 g N kg -1 leaf dry matter (DM), moderately defi cient (from 17.1 to 20.0 g N kg -1 DM), and suffi cient (> 20.1 g N kg -1 DM). The VIs were selected by principal component analysis and the performance of the networks evaluated by the accuracy. The accuracy in classifi cation obtained by the networks were 88%, 86% and 79% for FFBP, CFBP and RBFNN, respectively, indicating that the spectral signatures can be determined from images acquired in the fi eld. So, the proposed method could be used to develop a software that aims to monitor the status of N in real time, providing a fast and inexpensive tool for defi ning the time and the amount of N fertilizer, according to the pasture demand.

Current Scenario and Perspectives for Nitrogen Fertilization Strategies on Tropical Perennial Grass Pastures: A Review

“…In this scenario, a shifting between the contribution of litter and external sources, such as excreta and fertilizers, for supplying nutrients to plants is of higher relevance [17], and N fertilization will probably be required at greater amounts compared with the initial regrowth cycles (Figure 2, in "B" and "C"). Both [21], in Mavuno grass, and [80], in Xaraés palisadegrass, reported that during the intermediate regrowth cycles of the growing season, using the equivalent to 30 kg N ha −1 after each regrowth cycle was enough for the pastures to attain N sufficiency indexes and sustain maximum growth rates. Moreover, [81] observed that tillers that sustain higher pasture growth rates during the beginning of the autumn in stockpiled Marandu palisadegrass correspond to the tillers generations that appeared during the summer season (between January and February), highlighting the importance of an adequate N supply at these periods of the year.…”

Relationship between spectral indices and quality parameters of tifton 85 forage

“…Spectral indices obtained from digital images have been used with recognized success in the evaluation of biophysical parameters of different crops. In forage crops, the use of digital images has enabled the instantaneous, nondestructive quantification of leaf area and leaf weight (HUACCHA-CASTILLO et al, 2023), nitrogen fertilization estimation (MANCIN et al, 2022), aboveground biomass estimation (MAIMAITIJIANG et al, 2019), productivity estimation (SERRET et al, 2020), and chlorophyll monitoring (KŘÍŽOVÁ et al, 2022).…”