An automated nozzle controller for self-propelled sprayers

Pesticide application is a vital, integrated component of 21st century agriculture. Pesticides allow more produce to be generated from fewer acres, increasing the world\u27s capacity and improving quality of life. Pesticide use however, is not independent of concerns. Pesticides by nature are destroyers. When applied to target pests, their destructive nature can be advantageously utilized, however when misapplication unites pesticides and susceptible non-target organisms, resulting effects can be catastrophic. The airborne movement of pesticides, spray drift, can result in up to 36.6% of the applied pesticide volume transporting outside of the intended swath to non-target organisms under high drift potential conditions (Grover et al., 1997). Studies have shown that through the implementation of best management principles, namely spraying with large droplet sizes, drift is reduced to less than 1% of the applied volume (SDTF, 1997; Grover et al., 1997). State-of-the-art drift reduction technologies inform applicators of real-time, site-specific dangers of drift, prompting applicators to implement best management practices. These technologies rely on the applicator for the decision making and implementation processes, adding subjectivity to the system and consequently, suboptimal performance. Objective, scientific decision making avenues are required for the future development of automated nozzle selection controllers to reduce spray drift. A basis for automated nozzle control was developed, implemented, and tested in the form of a tier 1 nozzle controller. Decision making processes rely on an on-board, real-time risk assessment; the comparison of mapped predicted depositions to established acceptable levels of depositions in sensitive areas. In-field testing results indicated the critical roles of a high-resolution representation of the nozzle spectrum (specifically for droplets \u3c 150 ym), and a regression model maintaining specificity within overall predictive accuracy. The nozzle controller was found to theoretically protect sensitive areas from excessive drift however significant differences between the predicted and actual drift phenomenon led to depositions measured in sensitive areas exceeding acceptable levels. Attempting to account for real-time operating conditions was found to significantly reduce the predictive accuracy of the controller, largely due to insufficient representation of highly variable wind speeds and direction vectors acting on droplets after release. Further development of predictive capabilities in representing wind speed and direction for durations up to 30 seconds after a droplet is released are required for micro-scale nozzle control

An Interactive Spray Drift Simulator

The off target movement of pesticides, known as spray drift, results in a reduction in application rates, damage to non-target organisms, and environmental concerns. Much of this drift can be eliminated if its prevalence is understood and best management practices are implemented. Drift prediction software has been developed to serve as a management tool in determining the effects of applying pesticides under certain operating conditions. To further increase the usefulness and instructiveness of such programs, a program was developed which links spray drift prediction software (DRIFTSIM) with a GPS simulator to obtain a two dimensional representation of drift for simulated ground based spraying event. The program was evaluated using a variety of operating conditions to determine their respective effects on drift deposition levels. Results from the simulations show the importance of choosing the largest sufficient nozzle size, operating under low wind speeds, and spraying at the lowest possible boom height. Analysis of multi-swath simulations showed patterns of increased and reduced application rates due to spray drift

The Relative Accuracy of DRIFTSIM When Used as a Real-Time Spray Drift Predictor

Increasing regulation of spray drift has led to the development of real-time drift monitoring systems that present drift potential to applicators so that drift reduction spraying techniques can be implemented on an as-needed basis. The central component in each of these state-of-the-art systems is a drift prediction model. A real-time drift monitoring system was developed using look-up tables produced from simulations of a random-walk model (FLUENT via DRIFTSIM). The predictive accuracy of this system, evaluated as the difference between predicted drift and in-field measured drift, was compared to alternative prediction models to determine the suitability of random-walk models for real-time drift prediction. DRIFTSIM was found to produce a significantly more accurate representation of real-time predicted drift when compared to four of the six alternative models tested. No significant difference in predictive accuracy was found when comparing DRIFTSIM to the two other models. When compared to alternative models at incremented distances downwind from the point of spraying, DRIFTSIM’s predictions were found to be overall more accurate up to 10 m from the boom edge; however, three alternative models provided more accurate predictions for long-distance drift (20 to 50 m from the boom). These results suggest the potential of using DRIFTSIM in future real-time drift monitoring for increased accuracy and performance. However, additional development is needed to improve far-field (>10 m downwind of an application) drift prediction accuracy.This article is from Transactions of the ASABE, 55, no. 4 (2012): 1159–1165.</p