Sensor system for acquisition of vegetation indexes

Abstract

Vegetation indexes are important indicators of the health and yield of agricultural crops. Among the sensors used to evaluate vegetation indexes, proximal sensors can be used for real-time decision-making. Thus, the objective of this study was to develop a proximal sensor system based on phototransistors to acquire and store the following vegetation indexes: normalized difference vegetation index, simple ratio, wide dynamic range vegetation index, soil-adjusted vegetation index, and optimized soil-adjusted vegetation index. The sensor system was developed using an analog circuit to acquire reflectance data from red and near-infrared bands. The sensor system was calibrated according to the results of a spectroradiometer, using Zoysia japonica grass as the target. An algorithm that calculates and stores vegetation indexes in a file was developed. The Pearson correlation between the vegetation indexes obtained with the sensor system and the spectroradiometer was evaluated. The vegetation indexes presented a Pearson correlation higher than 0.92 to the estimated values by the spectroradiometer. Under the evaluation conditions, the proposed sensor system could be used to determine all vegetation indexes evaluated.

Estimation of percentage of impurities in coffee using a computer vision system

“…This is so in the cases of Khoje & Bodhe (2015), who determined the extent of damage to guava skin, Costa et al (2018), who determined the time of harvest of macaw palm fruits, and Leme et al (2019), who analyzed the degree of coffee roasting. Systems based on image processing and analysis have successfully evaluated characteristic patterns and predicted phenomena that would be difficult to perceive through visual analysis (Cruz & Silupu, 2014;Bello et al, 2020;Silva et al, 2021).…”