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Research Project: Coordinated Precision Application Technologies for Sustainable Pest Management and Crop Protection

Location: Application Technology Research

Title: Advanced spraying systems to improve pesticide saving and reduce spray drift for apple orchards

item XUN, LU - Technical University Of Catalonia
item CAMPOS, JAVIER - Technical University Of Catalonia
item SALAS, BERNAT - Technical University Of Catalonia
item FABREGAS, FRANCESC - Technical University Of Catalonia
item Zhu, Heping
item GIL, EMILIO - Technical University Of Catalonia

Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/3/2023
Publication Date: 9/20/2023
Citation: Xun, L., Campos, J., Salas, B., Fabregas, F.X., Zhu, H., Gil, E. 2023. Advanced spraying systems to improve pesticide saving and reduce spray drift for apple orchards. Precision Agriculture. 24:1526-1546.

Interpretive Summary: Because of the environmental contamination and health risk concerns on agrochemical applications, the European Commission has established a green initiative to reduce the use of both general pesticides and more hazardous pesticides by 50% across the European Union countries by 2030. One of the strategies to achieve this ambitious objective is to develop and adapt advanced equipment and technologies to optimize spray applications. In this research, a precision spraying system was developed and compared with a conventional spraying system and an optimized spraying system following the best management practices in apple orchards. The precision system was the integration of sensors detecting trees, optimal practices following the best management practices, and pesticide dose adjustment according to the canopy structure. Field tests demonstrated that this new system was able to reduce pesticide use by 43% and reduce spray drift by 57% compared with the conventional system. Therefore, the new advanced spraying system could minimize the environmental contamination related to spray drift risk and reduce the use of pesticide in apple orchards. This research also demonstrated that using sensor-guided spray technology could achieve the sustainable pesticide applications in compliance with European Union pesticide reduction regulations.

Technical Abstract: A real time variable rate sprayer controlled by a stereo vision system was developed to increase the accuracy of spray applications and reduce the use of crop protection products. The sprayer was designed to detect tree canopies and calculate its volume using depth images from the stereo vision system and discharge corresponding spray volumes every 200 ms through the embedded software in the graphical user interface. The sprayer was evaluated in an apple orchard at different travel speeds (3.2 to 8.0 km h-1) for its performance in detecting canopy and measuring its volume. In addition, spray volume, deposition and coverage of the variable rate application of the sprayer were evaluated against a constant rate application. Test results showed that the sprayer detected visually similar tree canopies during the evaluations although its canopy volume measurements deviated from manually measured canopy volume from 0.11 to 0.83 m3 due to lateral position changes of the sprayer. The sprayer adjusted duty cycles of pulse width modulated valves to accurately spray the intended volume for detected canopies (0.073 to 0.083 L m-3) and only used the spray volumes of 19.5% to 26.7% compared to a constant rate spray application (338 L ha-1). The constant rate spray application generally had more spray deposition and coverage in tree canopies than the variable rate sprayer as expected since its spray volume was approximately 3.7 times higher. However, the mean spray depositions from the constant rate spray application were significantly varied (p=0.05) by tree sizes while the variable rate spray application achieved statistically equivalent mean spray depositions regardless of tree sizes. The stereo vision controlled sprayer offers a cost-effective real-time variable rate spray option for growers with the potential of performing other tasks by using image process algorithms while applying crop protection products.