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Abstract. A solid set canopy delivery system (SSCDS) has the potential to be an efficient spraying technique because it minimizes chemical drift and allows timely spray applications even under adverse field conditions. A conventional SSCDS, working as a hydraulic spraying system, is controlled manually. Such operation makes it labor and time intensive for spray application in larger orchards. Therefore, a new hardware and software design was implemented in this project for field-level automation of a conventional SSCDS. An electronic control system (ECS) and a spray control unit (SCU) were developed and integrated with an improvised conventional SSCDS. A graphical user interface (GUI) was developed to provide user inputs to the ECS for valve actuation-based decision making. The SCU consisted of a microcontroller, a relay board, and a radio frequency (RF) module assisting in actuation of the solenoid valves in the desired sequence with commands from the ECS. Integrated RF trans-receiver modules facilitated closed-loop wireless communication between the ECS and SCU. This technical note elaborates on the successful integration of the hardware and software to achieve SSCDS automation and functionality, as well as the performance evaluation in a vineyard. The results confirmed the envisioned triggering sequence and solenoid valve actuation for different operating stages of the SSCDS over 120 m loop length. Moreover, the automated SSCDS had no significant differences in spray volume or pressure along the loop up to 82 m. Overall, the proposed integration will aid in automating SSCDS-based spray operation for larger orchards. To validate such a setup, further studies are planned for a centrally located control station and spraying several adjacent plots at a time. Keywords: Agricultural automation, Electronic control system, Precision spray application, Solid set canopy delivery, Spray control unit, Wireless communication.

The timing of spray application plays an essential role in daily pesticides management. Proper wind speed, air temperature, and relative humidity are the main external factors to improve the efficacy of pesticides, reduce the amount of spray drift and environmental pollution. Very few previous studies have focused on the need for short-term weather prediction in rapid spraying decisions. In this paper, a Convolutional-LSTM encoder-decoder (ConvLSTM-AE) hybrid model for multivariate output and multistep prediction with short time intervals is proposed to predict these three agrometeorological variables in advance. This model can predict daily weather conditions at 15-minute intervals and track the changes of time-varying systems effectively. This method was also compared with other methods such as CNN, multihead CNN, LSTM encoder-decoder, and CNN-LSTM encoder-decoder models. The results show that the proposed model outperforms other models and is suitable for daily weather forecasts in a short time interval. The obtained rapid and accurate prediction results provide a reliable basis for precise spray timing in actual farming.