Development of real-time laser-scanning system to detect tree canopy characteristics for variable-rate pesticide application

Air-assisted spraying is the primary method of plant protection in orchards, and precision spraying according to the canopy characteristics of fruit trees can reduce waste and pollution due to pesticide drift. To facilitate targeted pesticide application in the canopy of fruit trees, this study employed a newly developed wind-speed-adjustable orchard sprayer and established a prediction model for deposition based on data from orthogonal trials using a central composite design accounting for the coupling effect of three-dimensional spatial parameters. The experimental design systematically quantified the interaction effects of spray distance (1.5–2.5 m), fan wind speed (10–20 m/s), and deposition height (0.5–3 m) on the spatial distribution of droplets. Model significance was p < 0.0001 and the misfit term was significant (p = 0.2193), supporting its validity. The research found that wind speed and distance significantly interact in influencing deposition. By adjusting fan speed and spray distance, variable applications can be achieved in different canopy zones during plant protection operations. The response surface model developed in this study can be applied to variable-rate spraying control systems, thus providing a quantitative basis for dynamic droplet control guided by canopy characteristics. Validation tests revealed that the model’s accuracy was lower in high canopy regions and upwind spraying scenarios, indicating areas for further research.

Using an intelligent plant protection machine for spraying herbicides at a real-time variable rate plays a key role in improving the utilization efficiency of herbicides and reducing environmental pollution. Spraying volume (SV) and nozzle size (NS) are key factors influencing droplet deposition and herbicide efficacy and safety. A three-way split-split plot design experiment was conducted in the winter wheat field, with SV 180 L·ha⁻¹ and 150 L·ha⁻¹ in the main plot, a turbo air induction nozzle TTI11004 and TTI11003 in the subplot, herbicide flucarbazone-Na 70% WG mixed with florasulam 50 g·L⁻¹ SC as the recommended dose, and a 20% reduced dose in the sub-subplot. Droplet deposition and weed control efficacy treated by these three factors and their combination were evaluated. Results indicated that there was a significant influence of SV on droplet coverage and density, but no significant influence of NS and its interaction with SV. A droplet coverage and density of treatment at 180 L·ha⁻¹ were both significantly higher than at 150 L·ha⁻¹. The influence of SV and its interaction with NS on weed control efficacy were significant. The efficacy of treatment TTI11004 at SV 180 L·ha⁻¹ was the highest but decreased when NS was switched to TTI11003 and the SV was decreased to 150 L·ha⁻¹. There was no significant effect of all the treatments on winter wheat yield and its components, but the yield loss could be reduced by 2.36% when the herbicide input was reduced by 20%. We can conclude that herbicide input can be reduced by at least 20% using the intelligent machine while equipped with the right NS at the right SV, which would increase the safety of winter wheat production.

This study addresses the challenge of scheduling coordination between harvesters and transfer vehicles during the mechanized harvesting of processing peppers. An online detection method for assessing the loading capacity during harvesting was proposed, employing non-contact distance sensors to measure the stacking height of peppers in the hopper in real time. This measurement was used to calculate the loading capacity of the transfer vehicle for peppers. This study compared and analyzed the detection accuracy of ultrasonic, infrared distance, and light detection and ranging sensors to identify the most suitable sensor for detecting the stacking height of peppers, and establish an optimal detection model for the loading capacity of peppers in transfer vehicles. The results indicated that ultrasonic sensors achieved the highest accuracy, with a maximum absolute error of 11.0 mm and a standard deviation of 8.5 mm in detecting the stacking height of peppers. When three symmetrically mounted ultrasonic sensors were used, the maximum error and standard deviation for the mean stacking height under varying lifting speeds were 37.0 mm and 15.5 mm, respectively. The developed model’s relative error in detecting the pepper loading capacity decreased to less than 2% when the fed volume exceeded 0.4 m³. These findings provide a basis for scheduling decisions in the coordination of pepper harvesters and transfer vehicles.

Issues of poor real-time performance and low accuracy in the detection of load volume in the hopper during the mechanized harvesting of wine grapes are addressed in this study through the development of a proposed volume detection method based on ultrasonic sensors. First, the ultrasonic sensor beamwidth and detection height were determined through calibration tests. Next, a test bench was used to explore the influence of the number of ultrasonic sensors and conveying speed on the detected grape pile height. Data-based regression and hopper configuration-based geometric models correlating grape load volume with detected pile height were subsequently constructed; their accuracies were compared using test bench experiments to identify the optimal detection scheme. The regression model was more accurate than the geometric model under the considered conveying speeds with a maximum relative error of 8.0% for the former. Finally, field tests determined that the average grape load volume detection error during actual harvesting was 14.4%. Therefore, this study provides an effective solution for the detection of grape load volume in the hopper during mechanized harvesting and establishes a theoretical basis for the development of intelligent grape harvesting methods.

Orchard wind-assisted spraying technology relies on auxiliary airflow to disturb the canopy and improve droplet deposition uniformity. However, there are few effective means of quantitatively assessing the dynamic response of fruit tree leaves to airflow or the changes in airflow patterns within the canopy in real time. To address this, this study proposed an online monitoring method for the aerodynamic characteristics of fruit tree leaves using strain gauge sensors. The flexible strain gauge was affixed to the midribs of leaves from peach, pear and apple trees. Leaf deformations were captured with high-speed video recording (100 fps) alongside electrical signals in controlled wind fields. Bartlett low-pass filtering and Fourier transform were used to extract frequency-domain features spanning between 0 and 50 Hz. The AdaBoost decision tree model was used to evaluate classification performance across frequency bands. The results demonstrated high accuracy in identifying wind exposure (98%) for pear leaf and classifying the three leaf types (κ = 0.98) within the 4–6 Hz band. A comparison with the frame analysis of high-speed video recordings revealed a time error of 2 s in model predictions. This study confirms that strain gauge sensors combined with machine learning could efficiently monitor fruit tree leaf responses to external airflow in real time. It provides novel insights for optimizing wind-assisted spray parameters, reconstructing internal canopy wind field distributions and achieving precise pesticide application.