To solve the problem of lacking scientific guidance in aerial pesticide application, this study introduced an adaptive spraying decision system (ASDS) for Unmanned Aerial Vehicle (UAV) spraying to guide the operators of plant protection UAVs to set reasonable spraying parameters under complicated environment. The minimum applied volume rate, proper spraying velocity, spraying height, and initial droplet size were recommended by the ASDS. The key factor of the decision system is the decision model of reinforcement learning based on the actor-critic neural network. In specific, the field experimental data were used to train the critic and actor networks, which made the model adaptive to optimize the output of spraying parameters. Compared with the conventional spraying parameters, the spraying parameters recommended by the ASDS had a positive impact on wheat parcels. The decision results of the ASDS showed that the spraying volume rate was lower in the blocks with a small leaf area index. In addition, the spraying volume rate for the whole parcel was reduced by 14%. After UAV spraying, the uniformity of the droplet deposition in the ASDS parcel was better than that in the conventional parcel. Moreover, the penetrability of the droplets and the control efficacy for the brown wheat mite Petrobia latens (Muller) were similar in the two parcels. The ASDS can recommend the optimal spraying parameters to minimize pesticide application.
A soil electrical conductivity (EC) measurement system based on direct digital synthesizer (DDS) and digital oscilloscope was developed. The system took the "current-voltage four-electrode method" as the design principal and adopted a six-pin structure of the probe, two center pins to measure the soil EC in shallow layer, two outside pins to measure the soil EC in deep layer, and two middle pins for inputting the driving current. A signal generating circuit using DDS technology was adopted to generate sine signals, which was connected with the two middle pins. A digital oscilloscope was used to record and store the two soil output signals with noises in microseconds, which were from the two center pins and two outside pins, respectively. Then a digital bandpass filter was used to filter the soil output signals recorded by the digital oscilloscope. Compared with the traditional analog filter circuit, the digital filter could filter out the noises of all frequency except for the frequency of the excitation source. It could improve the effect of filtering and the accuracy of the soil EC measurement system. The DDS circuit could provide more stable sine signals with larger amplitudes. The use of digital oscilloscope enables us to analyze the soil output signals in microseconds and measure the soil EC more accurately. The new soil EC measurement system based on DDS and digital oscilloscope can provide a new effective tool for soil sensing in precision agriculture.
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