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BACKGROUND Unmanned aerial vehicles (UAVs) are a recently developed aerial spraying technology. However, the effect of spray volume variation on deposition and pesticide control efficacy is unknown. The effect of three UAV spray volumes (9.0, 16.8 and 28.1 L ha−1) using three different nozzle sizes on droplet deposition and wheat aphid and powdery mildew control efficacy was assessed. An electric air‐pressure knapsack (EAP) sprayer was used as a comparison. RESULTS Different spray volumes significantly influenced the deposition and control efficacy of the UAV and EAP. For the UAV, a low spray volume of 9.0 L ha−1 with a fine nozzle (nozzle LU120‐01) resulted in lower deposition and control efficacy. Optimal control efficacy was achieved with coarser nozzles (nozzles LU120‐02, ‐03) at > 16.8 L ha−1 volume with systemic insecticide, and at 28.1 L ha−1 with contact insecticide and fungicide. For EAP, a high spray volume led to run‐off, and a spray volume of 225 L ha−1 achieved better deposition and control efficacy. CONCLUSION The UAV had comparable deposition and efficacy control to the EAP at a higher spray volume (> 16.8 L ha−1) with coarse nozzles, but exhibited inferior deposition and efficacy control at a lower spray volume (<9.0 L ha−1) with fine nozzles. © 2019 Society of Chemical Industry

Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) , p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data.

Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.

As one of the important components of agricultural aviation industry in China, plant protection unmanned aerial vehicles (UAVs) have been developed rapidly in recent years. In order to understand the current development status and limitations of plant protection UAV and its spraying technologies in China, the Department of Agricultural Mechanization Management of the Ministry of Agriculture commissioned South China Agricultural University to perform a survey and generate a report on Analysis of the Development Situation and Policy Suggestion for Agricultural Plant Protection UAV in China in 2016. Based on this report, this paper performed statistical analyses on the development and application of plant protection UAV in China. First, the geographical distribution of operating plant protection UAVs in China was discussed. Second, the current status of spraying technologies for plant protection UAVs were reviewed. Key components in aerial spraying, including the effects of operating parameter of aerial spraying, aerial applied pesticide effect detection, and the promotion and application of aerial spraying technology. Last, future perspectives of spraying technology for plant protection UAV was discussed. This paper may inspire the innovation of precision agricultural aviation technology, the basic theory development of pesticide spraying technology, multi-aircraft cooperative technology and other supporting technologies for UAV-based aerial spraying for scientific research and application by research institutions and enterprises in China.

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Detection and monitoring are the first essential step for effective management of sheath blight (ShB), a major disease in rice worldwide. Unmanned aerial systems have a high potential of being utilized to improve this detection process since they can reduce the time needed for scouting for the disease at a field scale, and are affordable and user-friendly in operation. In this study, a commercialized quadrotor unmanned aerial vehicle (UAV), equipped with digital and multispectral cameras, was used to capture imagery data of research plots with 67 rice cultivars and elite lines. Collected imagery data were then processed and analyzed to characterize the development of ShB and quantify different levels of the disease in the field. Through color features extraction and color space transformation of images, it was found that the color transformation could qualitatively detect the infected areas of ShB in the field plots. However, it was less effective to detect different levels of the disease. Five vegetation indices were then calculated from the multispectral images, and ground truths of disease severity and GreenSeeker measured NDVI (Normalized Difference Vegetation Index) were collected. The results of relationship analyses indicate that there was a strong correlation between ground-measured NDVIs and image-extracted NDVIs with the R2 of 0.907 and the root mean square error (RMSE) of 0.0854, and a good correlation between image-extracted NDVIs and disease severity with the R2 of 0.627 and the RMSE of 0.0852. Use of image-based NDVIs extracted from multispectral images could quantify different levels of ShB in the field plots with an accuracy of 63%. These results demonstrate that a customer-grade UAV integrated with digital and multispectral cameras can be an effective tool to detect the ShB disease at a field scale.

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