Biophysical characteristics of soybean estimated by remote sensing associated with artificial intelligence

The biophysical characteristics of vegetative canopies, such as biomass, height, and canopy diameter, are of paramount importance for the study of the development and productive behavior of crops. Faced with a scarcity of studies aimed at estimating these parameters, the objective of this study was to evaluate the performance of artificial neural networks (ANNs) applied to Proximal Remote Sensing (PRS) to estimate biophysical characteristics of soybean culture. The data used to train and validate the ANNs came from an experiment composed of 65 plots with 30 x 30 m mesh, its development was carried out in the 2016/2017 crop in the Brazilian agricultural area. The evaluations were carried out at 30, 45, 60, and 75 days after sowing (DAS), monitoring the spatial and temporal variability of the biophysical characteristics of the soybean crop. Vegetation indexes were collected using canopy sensors. The accuracy and precision were determined by the coefficient of determination (R2) and the error of the forecasts by MAPE (Mean Absolute Percentage Error). PRS and ANNs showed high potential for application in agriculture, since they obtained good performance in the estimation of height (R2 = 0.89) and canopy diameter (R2 = 0.96), being fresh biomass (R2 =0.98) and dry biomass (R2 = 0.97) were the best-estimated variables.

Statistical process control applied to mechanized peanut sowing as a function of soil texture.

The successful establishment of agricultural crops depends on sowing quality, machinery performance, soil type and conditions, among other factors. This study evaluates the operational quality of mechanized peanut sowing in three soil types (sand, silt, and clay) with variable moisture contents. The experiment was conducted in three locations in the state of São Paulo, Brazil. The track-sampling scheme was used for 80 sampling locations of each soil type. Descriptive statistics and statistical process control (SPC) were used to evaluate the quality indicators of mechanized peanut sowing. The variables had normal distributions and were stable from the viewpoint of SPC. The best performance for peanut sowing density, normal spacing, and the initial seedling growing stand was found for clayey soil followed by sandy soil and then silty soil. Sandy or clayey soils displayed similar results regarding sowing depth, which was deeper than in the silty soil. Overall, the texture and the moisture of clayey soil provided the best operational performance for mechanized peanut sowing.