Effects of spray adjuvants and operation modes on droplet deposition and elm aphid control using an unmanned aerial vehicle

A conventional spraying mode and a fully autonomous fruit tree operation mode using a model DJ T30 unmanned aerial vehicle(UAV)were used to control aphids control on elm trees and to clarify the distribution of droplets in elm trees sprayed by a UAV.The effects of six aviation spray adjuvants on elm canopy droplet deposition and aphid control were evaluated.ImageJ software was used to analyze and measure the droplet density and deposition of water sensitive paper in two modes;this was done to calculate the droplet uniformity,depositional penetration,and droplet penetration,and to verify the aphid control effect.The results showed that the droplet density increased by 79.7%-100.7% in the upper canopy and 0-394.1%in the lower canopy without adjuvants in the fully autonomous fruit tree operation mode.The upper canopy deposits increased by 65.7%-179.3%,and the lower canopy increased by 0-152.8%.When adjuvants were added,the droplet density in the upper canopy increased by 49.7-56.1%using Jiexiaofeng(JXF),and the lower canopy increased by 138.2%-177.8% using JXF,45.8%-141.3%using Beidatong(BDT),45.5%-92.9% using Gongbei(GB),0-93.5% using Maisi(MS),and 0-95.2%using Manniu(MN).The deposits of the upper canopy increased by 888.1-1154.2% using JXF,0-1298.3% using MN,0-343.9%using BDT,0-422.5% using GB,0-580.3% using MS.The lower canopy increased by 746.4%-1426.0%using JXF,226.2%-231.0% using BDT,435.8%-644.0% using GB,255.0%-322.4%using MS,and 249.3%-360.0%using MN.When JXF was added,the droplet uniformity,droplet penetration and depositional penetration were better than when using other adjuvants.The effects of JXF,BDT and GB in controlling aphids was significantly better than other adjuvants(p<0.05).The following control effects were observed;94.1% with JXF,93.1% with BDT,and 93.3% with GB after 3 d of application,and 97.9% with JXF,95.6% with BDT,and 97.1% with GB after 7 d of application.At the same time,the application of the fully autonomous fruit tree operation mode and JXF can effectively improve the density and deposits,which will produce a superposition optimization effect.Our study focuses on the prevention and control of elm aphid infestations based on the operation mode of a UAV and aviation spray adjuvants,which can provide a baseline for the control of diseases and insect pests using UAVs in agriculture and forestry.

Point Cloud Completion of Plant Leaves under Occlusion Conditions Based on Deep Learning

The utilization of 3-dimensional point cloud technology for non-invasive measurement of plant phenotypic parameters can furnish important data for plant breeding,agricultural production,and diverse research applications.Nevertheless,the utilization of depth sensors and other tools for capturing plant point clouds often results in missing and incomplete data due to the limitations of 2.5D imaging features and leaf occlusion.This drawback obstructed the accurate extraction of phenotypic parameters.Hence,this study presented a solution for incomplete flowering Chinese Cabbage point clouds using Point Fractal Network-based techniques.The study performed experiments on flowering Chinese Cabbage by constructing a point cloud dataset of their leaves and training the network.The findings demonstrated that our network is stable and robust,as it can effectively complete diverse leaf point cloud morphologies,missing ratios,and multi-missing scenarios.A novel framework is presented for 3D plant reconstruction using a single-view RGB-D(Red,Green,Blue and Depth)image.This method leveraged deep learning to complete localized incomplete leaf point clouds acquired by RGB-D cameras under occlusion conditions.Additionally,the extracted leaf area parameters,based on triangular mesh,were compared with the measured values.The outcomes revealed that prior to the point cloud completion,the R^(2)value of the flowering Chinese Cabbage's estimated leaf area(in comparison to the standard reference value)was 0.9162.The root mean square error(RMSE)was 15.88 cm^(2),and the average relative error was 22.11%.However,post-completion,the estimated value of leaf area witnessed a significant improvement,with an R^(2)of 0.9637,an RMSE of 6.79 cm^(2),and average relative error of 8.82%.The accuracy of estimating the phenotypic parameters has been enhanced significantly,enabling efficient retrieval of such parameters.This development offers a fresh perspective for non-destructive identification of plant phenotypes.