Within the context of precision agriculture, the use of automatic guidance is without a doubt one of the most popular tools among farmers, however, are few producers of peanuts using this technology, the benefits from this technology can bring significant gains for culture even more when thinking about reducing the indices of losses in the digging. Thus, it objective was to evaluate the variability of quantitative losses of peanut mechanized digging with use the autopilot, using the Statistical Process Control. The treatments consisted of absence of autopilot use in sowing and digging, pilot's absence at sowing and presence in the digging, pilot use at sowing and absence in the digging and the pilot use in sowing and digging. In each treatment, 15 points of each variable was collected from distance of 50 m apart. Visible, invisible and total losses in the digging and parallelism were evaluated. The reduction of the plant material on the vibratory mat affected the levels of visible losses. Total losses are strongly correlated with the invisible losses. The use of the autopilot allows the operator to pay more attention to the digging operation improving the quality of the operation. The average error found between passes of the mechanized set using autopilot was 0.35 m. The variability of the losses as well as of parallelism was reduced when using the autopilot in two operations, providing a higher quality process.

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Using UAV and multispectral images has contributed to identifying field variability and improving crop management through different data modeling methods. However, knowledge on application of these tools to manage peanut maturity variability is still lacking. Therefore, the objective of this study was to compare and validate linear and multiple linear regression with models using artificial neural networks (ANN) for estimating peanut maturity under irrigated and rainfed conditions. The models were trained (80% dataset) and tested (20% dataset) using results from the 2018 and 2019 growing seasons from irrigated and rainfed fields. In each field, plant reflectance was collected weekly from 90 days after planting using a UAV-mounted multispectral camera. Images were used to develop vegetation indices (VIs). Peanut pods were collected on the same dates as the UAV flights for maturity assessment using the peanut maturity index (PMI). The precision and accuracy of the linear models to estimate PMI using VIs were, in general, greater in irrigated fields with R2 > 0.40 than in rainfed areas, which had a maximum R2 value of 0.21. Multiple linear regressions combining adjusted growing degree days (aGDD) and VIs resulted in decreased RMSE for both irrigated and rainfed conditions and increased R2 in irrigated areas. However, these models did not perform successfully in the test process. On the other hand, ANN models that included VIs and aGDD showed accuracy of R2 = 0.91 in irrigated areas, regardless of using Multilayer Perceptron (MLP; RMSE = 0.062) or Radial Basis Function (RBF; RMSE = 0.065), as well as low tendency (1:1 line). These results indicated that, regardless of the ANN architecture used to predict complex and non-linear variables, peanut maturity can be estimated accurately through models with multiple inputs using VIs and aGDD. Although the accuracy of the MLP or RBF models for irrigated and rainfed areas separately was high, the overall ANN models using both irrigated and rainfed areas can be used to predict peanut maturity with the same precision.

The beneficial effect of corn seed treatment with zinc (Zn) is directly related to the source used. The excess of this micronutrient causes seedling stress and reduces growth. Thus, assuming that the use of exogenous phytohormones can minimize such effects, we evaluated different doses and sources of Zn for the treatment of maize seeds with or without salicylic acid. The experiment took place in the laboratory, and two factorial experiments, 2 × 4 + 1, were performed in a randomized design. The seeds were treated with either ZnO or ZnSO 4 at doses of 0.5, 1, 2, and 3 g.kg −1 seed with four replications, differing only by the addition of 4.14 mg L −1 salicylic acid. Treating seeds with Zn and salicylic acid did not affect germination. ZnO led to a greater increase in dry mass in corn seedlings as compared with zinc sulfate, especially at higher doses (2 and 3 g kg −1 seed). Seed treatment with sulfate reduces root and shoot length, and salicylic acid did not attenuate this toxic effect. Dry mass is not affected when oxide is used. Salicylic acid reduces the accumulation of zinc in the treatment of corn seeds, regardless of the source used. ARTICLE HISTORY

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Hydraulic systems are equipment widely used in stationary industrial equipment and in moving agricultural equipment and construction machines. Currently, the rotation of the vibrating conveyor belt of the peanut digger-inverter is triggered by the tractor power take-off. Considering that for the equipment to operate at its optimal, the tractor engine needs to work at low rotation, responsible for the rotation of the power take-off, this work aimed at developing an electro-hydraulic system able to transmit varying work rotations to the vibrating conveyor belt of the digger-inverter, regardless the rotation of the power take-off and the speed used in the tractor. The electro-hydraulic system uses the oil of the auxiliary hydraulic system (remote control) and electric power of the tractor battery. The rotation of the hydraulic motor that drives the vibrating conveyor axis is controlled by a proportional flow control valve while the direction of rotation is determined by an electro-hydraulic directional valve, controlled by a Vcontrol Personal Device Assistant controller installed in the tractor cab. The electro-hydraulic system developed can be used to control the rotation of the vibrating conveyor belt of the peanut digger-inverter since it meets the torque and power requirements necessary to move the vibrating conveyor belt, with the respective rotation control.