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In order to achieve accurate detection of mature Zanthoxylum in their natural environment, a Zanthoxylum detection network based on the YOLOv5 object detection model was proposed. It addresses the issues of irregular shape and occlusion caused by the growth of Zanthoxylum on trees and the overlapping of Zanthoxylum branches and leaves with the fruits, which affect the accuracy of Zanthoxylum detection. To improve the model’s generalization ability, data augmentation was performed using different methods. To enhance the directionality of feature extraction and enable the convolution kernel to be adjusted according to the actual shape of each Zanthoxylum cluster, the coordinate attention module and the deformable convolution module were integrated into the YOLOv5 network. Through ablation experiments, the impacts of the attention mechanism and deformable convolution on the performance of YOLOv5 were compared. Comparisons were made using the Faster R-CNN, SSD, and CenterNet algorithms. A Zanthoxylum harvesting robot vision detection platform was built, and the visual detection system was tested. The experimental results showed that using the improved YOLOv5 model, as compared to the original YOLOv5 network, the average detection accuracy for Zanthoxylum in its natural environment was increased by 4.6% and 6.9% in terms of mAP@0.5 and mAP@0.5:0.95, respectively, showing a significant advantage over other network models. At the same time, on the test set of Zanthoxylum with occlusions, the improved model showed increased mAP@0.5 and mAP@0.5:0.95 by 5.4% and 4.7%, respectively, compared to the original model. The improved model was tested on a mobile picking platform, and the results showed that the model was able to accurately identify mature Zanthoxylum in its natural environment at a detection speed of about 89.3 frames per second. This research provides technical support for the visual detection system of intelligent Zanthoxylum-harvesting robots.

Rapid and accurate crop chlorophyll content estimation and the leaf area index (LAI) are both crucial for guiding field management and improving crop yields. This paper proposes an accurate monitoring method for LAI and soil plant analytical development (SPAD) values (which are closely related to leaf chlorophyll content; we use the SPAD instead of chlorophyll relative content) based on the fusion of ground–air multi-source data. Firstly, in 2020 and 2021, we collected unmanned aerial vehicle (UAV) multispectral data, ground hyperspectral data, UAV visible-light data, and environmental cumulative temperature data for multiple growth stages of summer maize, respectively. Secondly, the effective plant height (canopy height model (CHM)), effective accumulation temperature (growing degree days (GDD)), canopy vegetation index (mainly spectral vegetation index) and canopy hyperspectral features of maize were extracted, and sensitive features were screened by correlation analysis. Then, based on single-source and multi-source data, multiple linear regression (MLR), partial least-squares regression (PLSR) and random forest (RF) regression were used to construct LAI and SPAD inversion models. Finally, the distribution of LAI and SPAD prescription plots was generated and the trend for the two was analyzed. The results were as follows: (1) The correlations between the position of the hyperspectral red edge and the first-order differential value in the red edge with LAI and SPAD were all greater than 0.5. The correlation between the vegetation index, including a red and near-infrared band, with LAI and SPAD was above 0.75. The correlation between crop height and effective accumulated temperature with LAI and SPAD was above 0.7. (2) The inversion models based on multi-source data were more effective than the models made with single-source data. The RF model with multi-source data fusion achieved the highest accuracy of all models. In the testing set, the LAI and SPAD models’ R2 was 0.9315 and 0.7767; the RMSE was 0.4895 and 2.8387. (3) The absolute error between the extraction result of each model prescription map and the measured value was small. The error between the predicted value and the measured value of the LAI prescription map generated by the RF model was less than 0.4895. The difference between the predicted value and the measured value of the SPAD prescription map was less than 2.8387. The LAI and SPAD of summer maize first increased and then decreased with the advancement of the growth period, which was in line with the actual growth conditions. The research results indicate that the proposed method could effectively monitor maize growth parameters and provide a scientific basis for summer maize field management.

Coffee production and marketing is one of the main global commercial activities, but crop yields depend on several factors, among which plant health. The objective of this study was to evaluate the efficiency of spray droplet deposition in coffee crops grown in a mountain region, associated to the efficacy of the control of fungal diseases. The application efficiency, using an unmanned aerial vehicle (UAV), and the efficacy of the products applied were tested. Water-sensitive paper tags were used to analyze the application efficiency; agronomic efficiency, vegetative vigor, yield, and physiological parameters were used to determine the fungicide efficacy. Droplet coverage in the upper canopy layer using a pneumatic sprayer (28.70%) was 4.11-fold higher than that found in the same layer for application using a UAV (6.98%) at the rate of 15 L ha−1. The highest droplet depositions by using a UAV were found for the rate of 15 L ha−1: 1.60, 1.04, and 0.43 µL cm−2 in the upper, middle, and lower layers, respectively; the deposition in the upper layer with application using a pneumatic sprayer was 42.67 µL cm−2, and therefore, a 26.7-fold higher deposition. The results denote that the control of fungal diseases through fungicide applications using a UAV is efficient for mountain coffee crops.

Plant leaf detection and segmentation are challenging tasks for in-situ plant image analysis. Here, a novel leaf detection scheme is proposed to detect individual leaves and accurately determine leaf shapes in natural scenes. A leaf skeleton-extraction method was developed by analysing local image features of skeleton pixels. Approximate positions of individual leaves were determined according to the main leaf skeleton. Sub-images containing only single target leaves were extracted from whole plant images according to position and size of the main skeleton. Accurate leaf analysis was conducted on the sub-images of individual leaves. Leaf direction was calculated by examining the structure of the main leaf skeleton. Joint segmentation by combining region and active shape model was presented to accurately elucidate leaf shape. Leaf detection was implemented using deep learning approach, Faster R–CNN. A plant leaf image dataset containing four types of leaf images of different complexity was built to evaluate detection algorithms. Plant leaves with occlusions and complex backgrounds were effectively detected and their shapes accurately determined. Detection accuracy of the proposed method was 81.10%–100%, and 86.75%–100% for Faster R–CNN. The method demonstrated a comparable detection ability to that of Faster R–CNN. Furthermore, the rates of success to determine leaf direction by our method ranged between 89.06% and 100%, while the average measurement difference was 1.29° compared with manual measurement. The accuracy of shape measurement was 75.95%–100% for all types of plant images. Therefore, this method is accurate and stable for precise leaf measurements in agricultural applications.

Accurate estimation of chlorophyll content is important for diagnosing the physiological and phenological status of vegetation. Establishing the relationship between vegetation indices (VIs) and leaf chlorophyll content using remote sensing is crucial for large-scale earth observation. However, existing VIs for estimating chlorophyll content generally suffer from the saturation effect or depend on specific scenarios, resulting in insufficient estimation accuracy. Based on the physical mechanism of light-vegetation interaction, this study innovatively proposes the absorption triangle and reflectance triangle in the spectral space to construct the chlorophyll-sensitive Sentinel-2 Triangular Vegetation Index (STVI). The STVI uses the Sentinel-2 multispectral instrument (MSI) bands to improve the accuracy of chlorophyll content retrieval by enhancing the relationship with the chlorophyll content and mitigating the saturation effect. Simulated data, measured data, and open data sets were used to test the accuracy and stability of the STVI and 11 classical VIs for retrieving the chlorophyll content using different spectral data types, different winter wheat growth stages, and different vegetation coverages. The results showed that the STVI was more sensitive to the chlorophyll content than the classical VIs and provided the best goodness-of-fit in multiple scenarios. The STVI represents a powerful tool for large-extent chlorophyll content retrieval and a novel approach for scientific research in related fields.

Background Agricultural pests and diseases affect grain yield and quality. In addition to the use of chemical agents, the biological control of diseases and insect pests has attracted more and more attention due to an urgent need for environmental protection. However, traditional operational methods do not integrate well with biological control technology. With the advantages of simple operation, low operating cost, high operating efficiency and wide application range, use of multirotor unmanned aerial vehicles (UAVs) in biological control is of great significance. Results An intelligent delivery system comprising an insect natural enemy (Trichogramma) capsule based on an M45 electric multirotor UAV was built to release Trichogramma ostriniae in corn fields under full autonomous mode. During operation, the M45 UAV can fly accurately in accordance with operational requirements; average deviation between the actual and planned routes was 0.24 m, the average flight speed was 9.18 m s-1 and the average altitude was 5.08 m. The effective coverage rate of natural enemies was 99.33% and the coverage redundancy was 36.75%. After delivery, an investigation into the field control effect showed an average control effect of 83.70%, which can result in effective pest control. Conclusion A natural enemy carrier intelligent delivery system based on an M45 electric multirotor UAV was found to have good flight performance. In addition, natural enemies were evenly distributed, giving even coverage of the target fields. The system had a good control effect. © 2021 Society of Chemical Industry.

The yield of coffee plants depends on several factors, and the leaf nutrition is one of the most important. The form of application of leaf fertilizers used is expensive considering the application efficacy and the efficacy of adherence and absorption of nutrients. The objective of this work was to evaluate the effect of configurations of operational parameters of centrifugal nozzle rotation and operational height on the application efficacy and efficacy of absorption of leaf fertilizers in Conilon coffee plants. The coverage, density, and deposition of spray droplets were efficacy parameters evaluated through water-sensitive paper tags and artificial targets. The efficacy of the macronutrients applied was confirmed through leaf analyses at 15 and 30 days after application. The application efficacy with a height of flight of 2.0 m and rotation of 10,000 rpm was the most adequate, with coverage of 12.4% and density of droplets of 127 droplets cm−2. The application of foliar fertilizers by UAV provided a distribution in newer leaves in which the absorption of macronutrients was greater, and the flight height of 2.0 m and rotation of the centrifugal nozzle of 10,000 rpm resulted in the best efficacy parameters.

Papaya production and export is increasingly expanding in the world market due to the nutritional importance of the fruit. Phytosanitary issues, labor shortages, and unevenness in land-based costal and motorized applications compromise crops, the environment, and humankind. The purpose of this study was to evaluate the efficiency of droplet distribution using an unmanned aerial vehicle, with different application rates (12.0, 15.0, and 18.0 L ha−1) and spray nozzles (XR110015 and MGA015) in the upper (UL), middle (ML), and lower (LL) layers, and on papaya fruit clusters (BF). Water-sensitive paper labels and artificial targets were used to assess the efficiency. Coverage, density, droplet distribution, and droplet diameter were influenced by the application rates in the following order: 18.0 > 15.0 > 12.0 L ha−1, showing concentrated droplet distribution in the respective layers: UL > ML > LL > BF. The 18.0 L ha−1 rate increased the variables examined, and the droplet coverage on the UL using the XR110015 nozzle was 6.56 times greater than that found on the LL and BF. The MGA015 nozzle presented better results in the LL and BF in all variables analyzed. The UAVs were efficient in applying to the papaya crop and further studies should be carried out in order to confirm the efficacy of plant protection products applied using this technology.

Gaillardia pulchella Foug., belonging to the family Asteraceae, is an annual herb commonly seen in tropical America and China. It is often used as ornamental flowers because of its bright color, long flowering period and simple cultivation and management. In June 2021, leaf spot on G. pulchella with ∼40% disease incidence was observed in Laoshan scenic spot of Qingdao, Shandong Province, China. Initial symptoms on leaves appeared as light yellow to brown round or oval spots with dark brown borders, and the lesion area gradually expanded and the color deepened with the development of the disease. Small tissue samples collected from the infected lesions were surface-sterilized with 70% ethanol for 30 s, then rinsed with 2% sodium hypochlorite (NaClO) for 60 s, and finally rinsed with sterilized water three times. All the samples were transferred to potato dextrose agar (PDA) medium and incubated at 25℃ in the dark for 5 days (Zhu et al. 2013). A total of 9 isolates were obtained from the 11 selected tissues of symptomatic leaves. Afterward, all the single spore isolates were transferred onto potato carrot agar (PCA) plates (Mirkova 2003). After 7 to 10 days of incubation on PCA at 25℃ in the dark, colonies had a cottony mycelium with round margins, colored in white to gray. To test pathogenicity, six healthy G. pulchella plants were inoculated with mycelial plugs of the above pure cultures from a 7-day-old culture grown on PCA, while six germfree PCA plugs were served as negative controls. All the inoculated plants were set in greenhouse incubator at 25℃ and 80% relative humidity. Following 5 days incubation, brown spots began to appear on the sites of all inoculated leaves with mycelial plugs, while all the negative controls inoculated with sterile PCA plugs remained healthy. Infected lesions were separated and cultured as the same as those isolated in the field, and the same isolate was again microscopically identified, fulfilling Koch's postulates. 5 isolates were characterized, the colony margins of single spore isolate were round with gray or black aerial mycelia. Conidia were clustered and unbranched with 1 to 4 septa, colored in light or dark brown, shaped in obclavate or ellipsoid with short conical beak at the tip, dimensions varied from 14 to 51 μm (length) × 4.5 to 11 μm (width). The described morphological characteristics were consistent with Alternaria alternata (Simmons 2007). For further identification of molecular characterization, the genes of Chitin synthase (CHSD), RNA polymerase II second largest subunit (PRB2), Tsr1 ribosome biogenesis protein (Tsr1) and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were obtained by PCR amplification with the primer pairs CHSDF1/CHSDR1, PRB2DF/PRB2DR, Tsr1F/Tsr1R and GAPDHF1/GAPDHR1 (Damn et al. 2019; Lawrence et al. 2013), respectively. The sequenced genes (GenBank accession nos. ON660874, ON660875, ON660876 and ON660877) had more than 99% nucleotide identity with the corresponding genes (GenBank accession nos. KY996470.1, MN304718.1, KY996472.1 and MN158133.1) of the reference strains of A. alternata in GenBank, and the re-inoculated and re-isolated strains have the same results which were repeated three times. The causal agent occurred on G. pulchella was identified as A. alternata based on the morphological and molecular characteristics. To our knowledge, this is the first record causing leaf spot on G. pulchella by A. alternata in China.

The use of drones in agriculture is expanding at a brisk pace in crop production due to the superiority in precision, efficiency, and safety of their applicators. However, their potential drift risk also raises concern for users and regulatory authorities. The method of wind tunnel research can effectively evaluate the weighted influence of each drift factor, especially the drift characteristics of the nozzle and spray solution. Based on the wind tunnel test results, centrifugal nozzles have a higher drift risk than hydraulic nozzles, even with a similar DV50. The cumulative drift rate of the centrifugal nozzle at 2 m downwind was 90.1% compared to the LU12001 nozzle’s 40.6% under the wind speed of 3.5 m/s. Compared with the same coding as the flat fan hydraulic nozzle, the IDK nozzle can effectively reduce the drift rate. For the tested nozzles, DV50 and wind speed had a linear relationship with drift rate, and the sampling location had an exponential or logarithmic relationship with drift rate. Spray adjuvants, especially modified vegetable oils, had a significant effect on reducing the amount of drift. The results of this experiment provide a reference for the selection of nozzles and the addition of spray adjuvants. Further clarifying the spray drift characteristics of drones until a drift prediction model is available is still the focus of research.

Bellis perennis L., commonly known as the daisy or sun chrysanthemum, belonging to the family Asteraceae, is a perennial herb and is usually used as an ornamental plant worldwide for its vibrant flowers. Simultaneously, B. perennis has been proved to have therapeutic effects used on common colds, wound healing, anti-tumor, anxiolytic and antioxidant (Karakas et al. 2017). In July 2021, typical leaf spot was observed on B. perennis with about 50% disease incidence in Ruyue lake wetland park of Zibo (36.71°N, 118.01°E), Shandong Province, China. We surveyed more than 1000 square meters of planting area, and the diseased leaves were mostly concentrated in the lower location of plants, where the humidity was higher under the forest. Symptoms on the initially diseased leaves appeared as light yellow, round or oval lesions with light or brown borders. With the development of the disease, the area of the lesion gradually expands, the color deepens, and the shape is becoming irregular. To identify the causal pathogen, small pieces of 15 tissues collected from the infected leaves were sterilized with 75% ethanol for 30 s and then 2% sodium hypochlorite (NaClO) for 60 s, finally rinsed with sterile water three times. All the tissues were placed on potato dextrose agar (PDA) and incubated at 25 ℃ in the dark for 5 days (Zhu et al. 2013). A total of 13 isolates were obtained from the above diseased leaves. The cultures were initially grayish white, then a light green halo appeared in the middle of the medium after 5 days, with numerous gray aerial hyphae. For molecular identification, the RNA polymerase II beta subunit (PRB2), Tsr ribosome biogenesis protein, partial coding sequences of chitin synthase, glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and major allergen Alt a 1 were amplified from genomic DNA extracted from four representative single isolates using the primers PRB2DF/PRB2DR, Tsr1F/Tsr1R, CHSDF1/CHSDR1, GDF1/GDR1, and AltF/AltR (Damn et al. 2019; Lawrence et al. 2013), respectively, and sequenced (GenBank accession nos. OL416000, OL416001, OL416002, OL416003, and OL416004). These genes had more than 99.9% nucleotide identity with the corresponding sequences (KY131957.1, KY131958.1, KY996470.1, MN657411.1, and KY923227.1) of the reference strains of Alternaria alternata in GenBank. For pathogenicity tests, five healthy B. perennis plants each with three living leaves were inoculated with mycelial plugs of A. alternata from a 5-day-old culture grown on PDA. After inoculation, the plants were placed in a greenhouse with 85% relative humidity at 25 ℃ and monitored daily for symptom development. After 3 days, all inoculated leaves with mycelial plugs of A. alternata appeared symptoms similar to those observed in the field previously, while no symptoms appeared on negative controls which were inoculated with sterile PDA plugs. Cultures re-isolated from diseased leaves had the same morphological and molecular results as those isolated in the field, confirming Koch's postulates. The causal agent on B. perennis was confirmed as A. alternata on the basis of morphological and molecular results (Simmons 2007). To our knowledge, this is the first report on the presence of A. alternata affecting B. perennis plants in China. The discovery of this new disease is beneficial to the application and protection of B. perennis, which is a popular landscape and medicinal plant.

Aerial application, a spraying method with aircraft as the application platform, is one of the most important methods of pest control in pine forests. This work aimed to investigate the movement and distribution patterns of droplets and the pest control effectiveness of helicopter spraying on Masson pine (MP) and Korean pine (KP) trees. In particular, three nozzle types (CP02, CP03 and CP04) were evaluated and compared at the same spray volume rate (15 L haThe CP04 nozzle with a larger orifice size showed significantly better deposition and penetration, but the precision of flight operation parameters must be strictly controlled when using this type of nozzle. The majority (≥48%) of the droplets produced by the three nozzle types were concentrated in the range of200 μm, and the spray performance of each nozzle type was more stable in the range 201-300 μm. The final pest correction control rate (CR) of MP was in the range 59.24-95.74% and that of KP was 45.41-54.39% by helicopter spraying.This study confirmed that it is necessary to select the appropriate nozzle type and ensure accurate flight parameters for aerial application in mountain pine forests. Meanwhile, it also confirms the importance of the timing of operation which can lead to incorrect implementation, resulting in poor control effectiveness. © 2022 Society of Chemical Industry.

Aerial spray is one of the most important applications of unmanned aerial vehicles (UAVs) in agriculture. This work aimed to promote the use of UAVs as an alternative to knapsack electric sprayers in pesticide application in Nanguo pear orchards planted in mountain terraced orchard scenarios. The spray deposition of four types of nozzles (SX110015, XR80015, IDK90015 and TR80015), two spray volume rates (45 and 90 L haThe air- assisted IDK90015 nozzle showed significantly higher deposition and penetration, and its large droplet size also reduced the risk of drift. Increasing the spray volume rate can increase the amount of droplets deposition. The adjuvant showed excellent potential to improve spray technology in Nanguo pear trees, with a mean deposition of 0.175-0.574 μL cmThe nozzle type, spray volume rate and adjuvant should be well considered when using the spot spray in orchard. Compared with increasing spray volume rate, the use of air-induction nozzles and surfactant-based adjuvants can improve the spray deposition better. © 2022 Society of Chemical Industry.

The efficient use of water and fertilizer is vital for optimizing plant growth and yield in rice production. To achieve sustainable rice production and resource management, the ways in which applied water and nitrogen affect the root and shoot morpho-physiology, as well as yield, must be understood. In this study, a pot experiment was conducted to investigate the effects of slow-release nitrogen fertilizer (sulfur-coated urea) application at three levels (light nitrogen (NL), medium nitrogen (NM), and heavy nitrogen (NH)) on the growth, yield, and nitrogen use efficiency (NUE) of rice grown under three water regimes (wetting and soil saturation (WSS), wetting and moderate drying (WMD) and wetting and severe drying (WSD)). The results revealed that differences in water regimes and fertilizer rates led to significant differences in the roots, shoots, yield, and NUE of rice. Increasing the N dosage by 5% enhanced the root and biomass production by 16% in comparison with that of the other groups. The NH×WSS treatment produced the greatest root length, weight, density, active absorption, and oxidation. However, the integration of WSS × NL generated the maximum value of nitrogen apparent recovery efficiency (63.1% to 67.6%) and the greatest value of nitrogen partial factor productivity (39.9 g g−1 to 41.13 g g−1). Transmission electron microscopy (TEM) images showed that plants grown under high and medium nitrogen fertilizer rates with WSS had improved leaf mesophyll structure with normal starch grains, clear cell walls, and well-developed chloroplasts with tidy and well-arranged thylakoids. These results show that TEM images are useful for characterizing the nitrogen and water status of leaves in the sub-micrometer range and providing specific information regarding the leaf microstructure. The findings of this study suggest that the application of NH×WSS can produce improvements in growth traits and increase rice yield; however, the NL×WSS treatment led to greater NUE, and the authors recommend its usage in rice agriculture.

Soil moisture is an important factor determining yield. With the increasing demand for agricultural irrigation water resources, evaluating soil moisture in advance to create a reasonable irrigation schedule would help improve water resource utilization. This paper established a continuous system for collecting meteorological information and soil moisture data from a litchi orchard. With the acquired data, a time series model called Deep Long Short-Term Memory (Deep-LSTM) is proposed in this paper. The Deep-LSTM model has five layers with the fused time series data to predict the soil moisture of a litchi orchard in four different growth seasons. To optimize the data quality of the soil moisture sensor, the Symlet wavelet denoising algorithm was applied in the data preprocessing section. The threshold of the wavelets was determined based on the unbiased risk estimation method to obtain better sensor data that would help with the model learning. The results showed that the root mean square error (RMSE) values of the Deep-LSTM model were 0.36, 0.52, 0.32, and 0.48%, and the mean absolute percentage error (MAPE) values were 2.12, 2.35, 1.35, and 3.13%, respectively, in flowering, fruiting, autumn shoots, and flower bud differentiation stages. The determination coefficients (R2) were 0.94, 0.95, 0.93, and 0.94, respectively, in the four different stages. The results indicate that the proposed model was effective at predicting time series soil moisture data from a litchi orchard. This research was meaningful with regards to acquiring the soil moisture characteristics in advance and thereby providing a valuable reference for the litchi orchard’s irrigation schedule.

In recent times, Wireless Sensor Network (WSN) technology is widely applied in various agricultural applications. However, when a WSN becomes large and complex, it is difficult to maintain and adjust the whole WSN or multiple WSNs manually. Therefore, we propose an UAV-based scheme for automatic delivery and recovery of WSN nodes in the field. This scheme includes a combination of specially designed UAV, node platforms together with a GNSS-RTK (Global Navigation Satellite System, Real – Time Kinematic) system. A prototype system was built to test the UAV-based scheme. The results and analyses of indoor and outdoor experiments demonstrated the feasibility and efficacy of the scheme.

Rotor unmanned aerial vehicle (UAV) for plant protection has been widely applied in China in recent years. As a unique and crucial parameter of rotor UAV, the distribution shape and law of rotor wind field determine the spatial distribution of spraying droplets and influence the outcome of low-altitude pesticide spraying. However, the exploration of the interaction between rice canopy and wind field as well as the vertical decay mechanism is still an uncharted field in the study of three-dimensional wind field. In this paper, the author acquired vertical wind speed through S pitot sensor array, wind speed sensors were installed to acquire vertical wind speed data in the canopy, 30 cm beneath the canopy and 60 cm beneath the canopy. Contour maps at three height levels with wind speed of equal difference were drawn to calculate the equivalent area (the area surrounded by the contour line and the upper boundary) and maximum wind field width, all of which are direct parameters represent the distribution pattern of different wind speeds at equal time span. Under the condition that rice plants are well-distributed with consistent density, as the height decays arithmetically, coefficient variance (CV) of equivalent area is no more than 5% with basically equal reduction in equivalent area, the vertical wind field formed by the decayed airflow as it travels through rice plants distributes hierarchically. The decay rate of equivalent area and wind speed follow linear relation. When the height reduces from Ptop to Pmedium, the decay rate monotonically changes from 47.9% to 8.4%. When the height reduces from Pmedium to Pbottom, the decay rate monotonically changes from 53.7% to 22.4%. The lower the height is, the greater the decay rate is. Having analyzed the abnormal data ranges, the author found that the vortex formed under the interaction of wind field and rice canopy is the primary cause of abnormal data. Moreover, an ideal cone model was built on the basis of the rotor area and decay rate of equivalent area at equal height. The cone model not only justifies the vortex structure of rotor wind field but also provides theoretical foundation for the permeability study of droplets in rice canopy and the study of pollination parameters.

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 (

Chemical application using unmanned aerial vehicles (UAVs) has received significant attention from researchers and the market in recent years. The concept of using drones for collaborative spraying was proposed by manufacturers for improving intelligence and work efficiency. However, chemical spraying is a professional technology in which spraying quality is the main concern. Using drones to achieve multi-unmanned aerial vehicle formation spraying and evaluating the spraying effect has not yet been reported. In this study, an indoor test platform and two UAVs for field experiments were built. Indoor and outdoor trials of close formation spraying were carried out in Guangzhou and Changji, China from the end of 2018 to 2019, respectively. The droplet density and distribution uniformity of droplets were evaluated from multiple spray overlap areas. It can be seen that simultaneous spraying was better than sequential spraying with the indoor spraying results in the outer fuselage overlap area (S1), and spraying in a short-interval mode can improve the droplet deposition distribution in the overlapping spraying area. Additionally, the droplet distribution result of sequential spraying was better than that of simultaneous spraying in the route center overlap area (S2). Also, the droplet distribution result of the long-interval mode was better than that of the short-interval mode. The uniformity of the droplets’ distribution in two spray width areas (S3) did not change significantly among the treatments.

Identifying and extracting male and female parent of hybrid rice and then accurately judging the spikelet flowering of male parents is the basis of hybrid rice pollination. Currently, male parent flowering information extraction for hybrid rice is basically obtained by manual recognition. In this study, remote sensing images of parental rice fields were obtained with a multispectral camera carried by a UAV (Umanned Aerial Vehicle). Six kinds of visible light vegetation indices and four kinds of multispectral vegetation indices, together with two classification methods, pixel-based supervised classification and sample-based object-oriented classification, were applied to identify the male and female parents of hybrid rice, after which the accuracies of the methods were compared. The results showed that the visible vegetation index had a better effect in pixel-based supervised classification. The kappa coefficient of ExGR (Excess Green minus Excess Red index) classification was 0.9256 and the total accuracy was 0.9552. The extraction accuracy was higher than that of the other vegetation indices and object-oriented classification. In pixel-based supervised classification, the maximum likelihood method achieved the highest identification accuracy and shortest calculation time. Taking the remote sensing images obtained with a UAV as a data source, maximum likelihood supervised classification based on ExGR index can more effectively and quickly identify the field information of male and female parents of hybrid rice so as to provide a reference for determining optimal pollination timing for hybrid rice in large-scale seed production farms.

Effective detection of rice spikelet flowering is crucial to the determination of optimal pollination timing for hybrid rice seed production. Currently, the detection of rice spikelet flowering status relies on manual observation of farmers, which has low efficiency and large errors. This study attempts to acquire rice spikelet flowering information using a hyperspectral technique and machine learning in order to meet the needs of hybrid rice seed pollination rapidly and automatically. Hyperspectral data of rice male parents with flowering and non-flowering in two experimental sites were collected with an ASD FieldSpec® HandHeld™2 spectrometer. Three traditional classifiers, Random Forest (RF), Support Vector Machine (SVM) and Back Propagation (BP) neural network, and Convolutional Neural Network (CNN), were used to build classification models for rice spikelets flowering detection. Three data processing methods, PCA feature extraction, GA feature selection, and the PCA and GA combination algorithm, were used for data dimensionality reduction. By comparing the precision and recall rate of different algorithms and data processing methods, the algorithms applicable to identify rice spikelet flowering were investigated. Results show that by evaluating different feature reduction methods and classifiers, the optimal model for rice spikelets flowering detection is the BP model with PCA feature extraction. The accuracy of the model reaches up to 96–100%. Hyperspectral technology and machine learning algorithm are capable of effective detection of rice spikelet flowering. This study provides technical reference for accurate judgment of rice flowering and helps to determine the optimal operation time for supplementary pollination of hybrid rice.

With the acceleration of the Belt and Road Initiative, Poland–China agricultural trade has increasingly strengthened, but there is little exchange or cooperation in agricultural machinery. China’s agricultural UAV industry has flourished over the past 7 years. In China, by using typical food crops and economic crops to control diseases and pests, agricultural drones can reduce the use of fertilizer, pesticides, and water, improve operational efficiency, open up new markets through the ‘sale + services’ mode, and reduce production costs and labor shortages. The spraying of agricultural UAVs and related pest-disease-defense services applied in China are also suitable for Poland’s decentralized, small-scale production modes. By learning from China’s development progress of precision-agriculture aviation, Poland can develop 5th-generation (5G) unmanned intelligent organic farms from traditional organic agriculture, use agricultural UAVs in the spraying of Plant Protection Products (PPPs), and carry out special protection or loss management on typical fruits. Furthermore, by building its own spraying system, aviation industry, and service team, Poland can realize resource optimization, technological empowerment, application expansion, and industrial innovation. Therefore, this paper focuses on the development experience of Chinese agricultural UAVs and discusses its enlightenment to the precision-agriculture aviation application of Poland.

Physostegia virginiana Benth. (false dragon head flower), belonging to the family Lamiaceae, is a perennial plant and is usually used as landscape plant in parks and wetlands in China. It is also widely used as an ornamental plant for cut-flower all over the world (Cardin et al. 2007). In June 2019, leaf spot symptoms were observed on P. virginiana in Zibo botanical garden of Shandong Province, China (36.79°N, 118.02°E). We surveyed about 200 square meters of planting area, and most of the infected plants were close to the water or wet places, with ~20% disease incidence which were concentrated in the lower leaves of plants. The symptoms appeared on leaves were mostly round or oval spots, light to dark brown in color, and 3 to 8 mm in diameter. Severe leaf lesions were linked together, causing early fall of leaves. Small pieces of 15 infected leaves were collected to confirm the causal pathogen. The samples were sterilized by 70% ethanol for 30 s, 5% NaClO for 1 min, then rinsed in sterile water three times, plated on potato dextrose agar (PDA) and incubated at 25℃ in the dark for 7 days (Zhu et al. 2013), and 7 isolates were obtained from 10 diseased samples. The stroma of fungal mycelium was initially white, gradually turning dark green to black, while the margin of colony was regular, with concentric rings which were black sporodochia aggregates. Conidiophore hyaline, produce 2-3 order complex branches, arising as 3-4 conidiogenous cells from the tip of the branches, conidiogenous cells ampulliform to cylindrical. Conidia were aseptate, unicellular, hyaline, cylindrical, and their dimensions varied from 4.8 to 8.2 × 1.7 to 2.4 μm with rounded tips. The morphological characteristics of the isolates matched features described for Paramyrothecium roridum (Tode) L. Lombard & Crous, comb. nov. (Lombard et al. 2016). For molecular identification, genomic DNA was extracted from five representative single spore isolates. The partial coding genes of internal transcribed spacer (ITS) and calmodulin (cmdA) from the original isolates were amplified with primers ITS1/ITS4 and CALDF1/CALDR1 (White et al. 1990; Lawrence et al. 2013), respectively. The sequenced genes (GenBank accession no. MT318535, and MT454826) exhibited 98.71%, and 100.00% homology with type specimen of P. roridum strain CBS372.50 (GenBank accession no. MH856665.1, and KU846271.1), respectively, confirming the morphological identification. Pathogenicity of the fungus was tested indoor by inoculating 5 living, healthy P. virginiana plants with 3 leaves, which were inoculated with 10 µl of conidial suspension (2 × 105 conidia/ml) from a 10-day-old cultures on PDA, while 5 other inoculated plants with 10 µl of sterile water were served as controls. Treated plants with the inoculated leaves were covered by plastic bags in the greenhouse of 14 h light/10 h dark with ~80% relative humidity at 25℃. As time went by (about 3-7 days), the leaves inoculated with conidial suspension appeared similar symptoms as described above, whereas negative controls were still healthy. The same pathogens were isolated from the diseased leaves and repeated three times with same results as those that were obtained previously from the outdoor plants, including morphological and molecular results which confirm Koch's postulates. To our knowledge, this is the first record of P. roridum causing leaf spot on P. virginiana in China. The finding is beneficial to the better application of P. virginiana, a very common ornamental plant.

In this paper, a novel method for modeling and optimization of the contact angle of rice leaf surfaces by comparing the analytical result of the artificial neural network (ANN) and response surface methodology (RSM), to minimize the contact angle of the rice leaf surfaces. Three factors and three levels Box-Behnken design was used to analyze the external factors influencing the contact angle of the rice leaf surfaces, which consisted of temperature, humidity and the chemical pesticide concentration. By optimizing the designing parameters, the optimum input parameters were obtained. The best input conditions were temperature (25.96 °C), humidity (60.00%), chemical pesticide concentration (131.89 mg/L), and a minimum contact angle of 103.565° was obtained. Moreover, the ANN sensitivity analysis was used to study the weight of three external factors affecting the contact angle. Experiments showed the concentration of chemical pesticides to be the chief factor, as it has a weight is 55.61%, followed by humidity with a weight of 31.15% and temperature with 13.24%. Finally, the reliability of the RSM and ANN methods in modeling and predicting the contact angle was compared by analyzing the value of mean squared error (MSE) and determination coefficient (R2).The result showed that both models have better performance, and both RSM and ANN have better determination coefficients. The experiments also illustrated that the ANN method get better results than RSM in fitting experimental data, prediction and modeling.

Processing pepper planting and processing have become an important red pillar industry in Xinjiang. With the continuous growth of processing pepper planting areas in Xinjiang, diseases and pests are increasing year by year. The aim of this study was to compare the droplet deposition and control efficiency of unmanned aerial vehicle (UAV) and electric air-pressure knapsack (EAP) sprayers on a processing pepper field. The UAV sprayer had a poor droplet coverage rate, droplet density, and deposition uniformity, but displayed the best deposition (1.01 &mu, g/cm2, which was 98% more than the EAP sprayer). The control efficacy of the UAV sprayer on processing pepper fields with Phytophthora capsici and aphids was slightly lower than that of the EAP sprayer. When the UAV sprayer was used to control processing pepper diseases and pests, it could reduce the pesticide dosage on the premise of ensuring the control effect. Further study of the residue of high concentration pesticides in pepper fruit and environment sprayed by UAVs are needed.

In the field of pesticide spraying, droplet size is one of the most important factors affecting droplet deposition and drift. In order to study the effect of different droplet size parameters on droplet deposition distribution and drift of aerial spraying by using plant protection UAV, an aerial spraying test with the same spraying rate and different size droplets in rice canopy was carried out by using multi-rotor unmanned aerial vehicles (UAV) and four TEEJET nozzles with different orifice sizes (these droplets with a volume median diameter (VMD) of 95.21, 121.43, 147.28, and 185.09 &mu, m, respectively), and the deposition distribution and penetration of droplets in the target area and the drift distribution of droplets in the non-target area were compared and analyzed. The results showed that the deposition distribution and penetration of droplets in the target area and the drift distribution of droplets in the non-target area were influenced by the droplet size. The droplet deposition rate in the upper and lower rice canopies were increased in the target area with the increase of droplet size. The penetration results of droplets also increased with the increase of droplet size, and that of droplets with a VMD of 185.09 &mu, m was the best, reaching 38.13%. The average values of the cumulative drift rate of droplets in the rice canopy in the four tests were 73.87%, 50.26%, 35.91%, and 23.06%, respectively, and the cumulative drift rate and the drift distance of droplets decreased with the increase of droplet size, which indicated that the increase of droplet size can effectively reduce droplet drift. It demonstrated that the droplet size is one of the most important factors affecting droplet deposition and drift for pesticide spraying by plant protection UAV, and for the application of plant protection UAV with extra-low volume spraying, the use of droplets with VMD less than 160 &mu, m should be avoided and a more than 10 m buffer zone should be considered downwind of the spraying field to avoid drug damage caused by pesticide drift. The results have fully revealed the effect of droplet size parameters on droplet deposition and drift of aerial spraying. Moreover, the influence of the wind field below the rotors on the distribution of droplet deposition was surmised and analyzed from the perspective of plant protection UAV. It is important for optimizing the droplet parameters of aerial spraying, increasing the spraying efficiency, and realizing precision agricultural aviation spray.

The volatiles of Brown rice plant hopper (BRPH) itself is an important evidence for BRPH electronic nose detection. However, during infestation, BRPH always sucks the juice from the rice stem, therefore, a study on the similarity between BRPH’s volatiles and undamaged rice stem volatiles might help determine whether the volatile contents of BRPH would be influenced by the sucking of the rice stem juice. If so, recognizing BRPH from rice stem should be a crucial step to reduce the misjudgment of BRPH occurrence prediction by using electronic nose, which has not been reported until now. This paper used an electronic nose (PEN3) sample of the volatile of U3IN (under the 3th-instar nymphs), O3IN (over the 3th-instar nymphs) and healthy rice stem. Hierarchical clustering analysis (HCA), Loading analysis (Loadings), principal component analysis (PCA), k-nearest neighbor (KNN), probabilistic neural network (PNN), and support vector machine (SVM) were used for data analysis. HCA, Loadings, and PCA results proved that certain similarities exist between volatiles of rice stem and BRPH, Loadings and PCA results also indicated the volatile similarity between O3IN and rice stem is stronger than the volatile similarity between U3IN and rice stem. To reduce the redundant information and improve computation efficiency, according to Loadings and PCA results, sensor R5 of electronic nose has been be removed, then, the fist four principle components has been kept as the feature values. KNN, PNN and SVM all can recognize rice stem, O3IN, and U3IN effectively, however, KNN and PNN are more fit to solve the problem of rice stem and BRHP recognition than SVM. This experiment results proved that certain similarities exist between volatiles of rice stem and BRPH, also figured out the feasible way to recognize rice stem and BRPH, which could provide a reference for further research of BRPH prediction.

The aerial spraying of pesticides by plant protection unmanned aerial vehicles (UAV) is a process in which the spray droplet deposition on target sites occurs under the influence of the downwash wind field. The downwash wind field is the most important factor affecting droplet deposition distribution characteristics in an aerial spray. To understand the mechanism of the downwash wind field, spray tests were conducted at different flight heights by using a DJI UAV, and the downwash wind field in the three-dimensional direction (X-directional wind, Y-crosswind, and Z-vertical wind) was measured by using a wind speed measurement system for UAV. Combined with the droplet deposition of aerial spray, the distribution characteristics of the downwash wind field and the influence of the downwash wind field on droplet deposition were studied. The results showed that it had obvious differences in the distribution of the downwash wind field for UAV at different flight heights. As the flight height increases, the downwash wind field in X-direction and Z-direction showed a strong to weak trend, while the downwash wind field in Y-direction showed an opposite trend. In addition, it was found that the downwash wind field in Y-direction and Z-direction both have a significant influence on droplet deposition. With the increase of flight height, the change of the downwash wind field led to a gradual decrease in droplet deposition in the effective spray area, and droplets deposited more uniformly. For the DJI T16 plant protection UAV in this test, the optimal flight height was 2.0 m, and the downwash wind field had a better improvement effect on droplet deposition. Therefore, in order to make full use of the downwash wind field of UAV, the appropriate flight height should be selected to improve droplet deposition of liquid pesticide and achieve a better control effect for crop disease and pests when UAV is used for aerial spray operations in the field. This study revealed the influence mechanism of the downwash wind field on droplet deposition of aerial spray, and proposed appropriate operation parameters from the perspective of practical operation. It was expected to provide data support for improving the operation quality of aerial spraying and the formulation of field operation specifications.

The airflow field produced by UAV is an important factor to determine the movement of spray droplets, which can enhance deposition and reduce drift. Therefore, in this study, the downwash airflow field of the six-rotor plant protection UAV in the flight state was simulated based on the Lattice Boltzmann method (LBM), and the distribution characteristics at multi-feature parameters including different flight speed, flight height, crosswind speed and work load were studied. In addition, take the coverage area and the penetrable area as evaluating index, the influence of multi-feature parameters and prediction model to the operation effect were investigated. The results showed that, with the increase of flight speed and height, the angle of backward tilt of the downwash airflow field gradually increased, and the length and width of longitudinal and horizontal spread on the ground decreased. The optimal flight speed and height for UAV operations respectively were 2 m or 2.5 m, and 4 m/s based on the evaluating index. As the flight height increased from 2 m to 3 m, the eddies became larger, and they would entrain and affect the spatial movement and distribution of droplets. Furthermore, the distribution of the airflow structure was obviously changed with the increase of crosswind speed, and the coverage area and the penetrable area had a huge decline when the crosswind speed was greater than 3 m/s, which was not fit for operations. By the designed orthogonal test, the prediction models of the coverage area and the penetrable area were established, the R square was 0.952 and 0.759, respectively; the influence of multi-parameters to the operation effect was analyzed, and the prediction of the models for the dependent variables were relatively accurate. The results of the study could provide reference for exploring the downwash airflow field and droplet deposition pattern in the complex airflow field when UAV sprayings.

This study proposed an efficient approach that an electronic tongue (ET) and an electronic eye (EE) combined with a deep learning algorithm were jointly leveraged to recognition Pu-erh tea. A one-dimensional convolutional neural network (1-D CNN) and a two-dimensional convolutional neural network (2-D CNN) were designed and optimized for the feature extraction of ET and EE signals, respectively. Then, a feature-level fusion strategy was introduced to address the feature vectors extracted from the different types of CNN models. To highlight the effect of data fusion, a backpropagation neural network (BPNN), a classifier similar to the fully connected layers of CNN models, was employed. Meanwhile, the Bayesian optimization algorithm (BOA) was employed for hyperparameter optimization of the identification models. The experimental results showed that the feature fusion strategy assimilated the merits of the ET and EE and gained better Pu-erh tea identification performance than an independent intelligent sensory system combined with CNN model. The results demonstrate that the feature-level fusion based on deep learning algorithm gained the best accuracy on the test set, with a precision, a recall, an F1-score and an AUC score of 99.07%, 99.2%, 0.992 and 0.994, respectively. This study shows that the simultaneous utilization of an ET and an EE combined with deep learning algorithm could function as a rapid detection method for discriminating the storage time of Pu-erh tea.

Defoliant spraying is an important aspect of mechanized cotton harvesting. Fully and uniformly spraying defoliant could improve the quality of defoliation and reduce the impurity content in cotton. Improving the coverage of defoliant droplets in the middle and lower layers of cotton and ensuring the full and even dispersion of droplets in the cotton canopy are essentially in increasing the defoliation effect. In this study, we assessed the effect of aviation spray adjuvants on droplet deposition, defoliation, boll opening and defoliant retention in cotton leaves sprayed by an unmanned aerial vehicle (UAV). The results showed that adding aviation spray adjuvants could significantly improve the defoliant droplet deposition. Fifteen days after spraying, the defoliation rate was 80.31% and the boll opening was 90.61%. The defoliation rate increased by 3.12&ndash, 34.62% and the boll opening rate increased by 6.67&ndash, 29.56% after the addition of aviation spray adjuvants. Using a vegetable oil adjuvant could significantly increase the droplet coverage rate and the retention of defoliants in cotton leaves.

A common problem of unmanned aerial vehicles (UAVs) spraying is that fewer droplets could reach the lower layer, and the depositions among the spray width show a significant difference. However, ensuring the penetration and uniformity of the spray is particularly important in the cotton harvest aid applications. As a new spraying method, poor droplet penetration and uniformity of droplet distribution may lead to inconsistent defoliation among canopy rows and poor defoliation in the lower part of the cotton canopy. The operational parameters of UAVs have remarkable effects on the droplet distribution, which will improve the defoliation rate of cotton. Therefore, full factorial tests were conducted in two experimental locations in the Xinjiang cotton-producing region in 2018 and 2019. The selected operational parameters in 2018 included the spray volume (three levels: 8.7, 12, and 15 L/ha), droplet size Dv50 (three levels: 100, 150, and 200 μm), and flight height (three levels: 1, 2, and 3 m). Meanwhile, the operational parameters in 2019 included the spray volume (three levels: 18, 22.5, and 30 L/ha) and droplet size Dv50 (three levels: 100, 150, and 200 μm). The main effects and interaction effects among the operational parameters were analyzed and discussed. The spray penetration and uniformity of droplet distribution were compared separately. The results show that flight height, spray volume, and droplet size have significant effects on spray penetration. Lowering the flight height of the drone, increasing the spray volume and Dv50 of droplets can improve the distribution of droplets at the bottom of the cotton canopy. As for droplet distribution uniformity, with the increase of the application volume and decrease of the flight height, the uniformity of the droplet distribution becomes worse. The influence of flight parameters on the uniformity of droplet distribution is very limited.

Traditional artificial statistical methods of hailstorm damage assessment are often inefficient, subjective, time-consuming, and inaccurate. The use of drones for high-resolution remote sensing imaging enables large-scale, rapid assessment of a target area. The purpose of this study was to use multispectral imaging techniques to promptly and accurately assess the damage degree of hail in crops. In this experiment, cotton was used as the research target, and the experiment was carried out after a hail disaster that occurred during the flowering and boll stage of cotton development. A hail vegetation index (HGVI) was constructed and compared with the classification results of other vegetation indices on the hail disaster degree. It was concluded that: (i) both the hail vegetation index HGVI and the previous vegetation index, ratio vegetation index, are suitable tools of classification for grading cotton hail disasters, with both indices having a Kappa coefficient over 0.85 and a median Kappa coefficient greater than 0.9; (ii) the method used in this study is suitable for simple classification based on multispectral images; (iii) this study explains the possibility of classifying hail damage degrees based on multispectral images. Subsequent research can further diagnose and classify hail disasters during different periods and improve the accuracy and completeness of classification.

Celtis sinensis Pers. (Chinese hackberry), belonging to the family Ulmaceae, is widely used as a street tree or landscape plant because of its longevity and aesthetic growth habit. Additionally, C. sinensis is of economic importance due to its medicinal properties. Roots and bark of the plant can be used in natural medicine for the treatment of lumbago, measles, tumor, etc (Zhang et al. 2016). In July 2019, symptoms of leaf spot were observed on C. sinensis in Yuanshan national forest park of Zibo, Shandong Province, China (36.48°N, 117.84°E). We surveyed more than 500 square meters of forest area, and more than 80% of the acreage was affected with the leafspot disease. Symptoms on infected leaves appeared as regular round or oval spots, colored in yellow with brown borders, which coalesced into larger spots as the disease progressed. To investigate the cause, 20 leaves of infected tissues were cut into ~2 mm pieces and surface disinfected with 75% ethanol for 30 s, rinsed three times with sterile deionized water. These were air dried and placed on potato dextrose agar (PDA) and incubated at 25℃ for 5 to 7 days. A minimum of 15 isolates were obtained and cultures were initially white, gradually becoming gray green to dark after 1 week, producing copious amounts of gray aerial mycelium. Three representative single isolates were used for molecular identification, which were verified based on the amplification of DNA sequences of internal transcribed spacer region, translation elongation factor 1 alpha and beta-tubulin genes, using the primers ITS1/ITS4 (White et al. 1990), EF1-728F/EF1-986R (Carbone and Kohn 1999), and BT-2a/BT-2b (Glass and Donaldson 1995), respectively. The sequenced genes (GenBank accession no. MT367874, MT385087, MT374083) exhibited 99.63% (Identity=545/547), 99.00% (Identity=297/300), and 100.00% (Identity=451/451) homology with the corresponding genes of type specimen of Botryosphaeria dothidea strain CBS110302 (GenBank accession no. AY259092, AY573218, EU673106), respectively. Morphological and molecular results showed that the isolates were B. dothidea (Slippers et al. 2014; Zhai et al. 2014). Pathogenicity was confirmed using five living, healthy C. sinensis plants with three leaves were wound inoculated with mycelial plugs (about 4 mm in diameter) of B. dothidea from a 7-day-old culture grown on PDA, while inoculated with sterile PDA plugs on the same leaves were served as negative controls. All the plants were covered by plastic sheeting and keep high relative humidity by adding water in time. Seven days later, all inoculated leaves appeared as round dark brown spots, which were larger than observed in the field. The pathogenicity test was repeated three times. No symptoms were observed on negative controls. Fungi re-isolated from inoculated leaves were confirmed as B. dothidea on the basis morphology and molecular characterization as described above. To our knowledge, this is the first report on the presence of B. dothidea affecting C. sinensis plants in China. This discovery is important to ensure the sustainable production of C. sinensis, an important landscaping and medicinal tree.

Single boll weight is the main factor of cotton yield and a key index used to evaluate the quality of cotton. Predicting the single boll weight in a large area is important for variety selection and yield improvement. A model was established to predict the single boll weight by using the multitemporal high-resolution visible light remote sensing data obtained from UAV. Specifically, remote sensing data were collected for 29 fields in the Changji, Shihezi and Shawan areas in northern Xinjiang during the blooming period and boll opening stage. Five circular areas with a radius of 1 m were selected from each field as the ground investigation area for collection of the cotton boll samples. Fully convolutional networks (FCN) was used to recognize and extract bolls at the boll opening stage in the remote sensing images as a dependent variable of the model. Correlation analysis was carried out by combining VDVI (Visible-band difference vegetation index) at the flowering and boll setting stages, VDVI at boll opening stages, VDVI at boll opening areas (Extracted by FCN) and RGB mean values, then use the least squares linear regression and BP neural networks to model the upper, middle, lower cotton layers and average single boll weight in investigation area. Subsequently, K-fold cross-validation was performed to evaluate the results. The results showed that the results of the least squares linear regression (R2 = 0.8162) and BP neural networks (R2 = 0.8170) were nearly equivalent. The percentage of boll opening in the area and VDVI at the flowering and boll setting stages were highly correlated with upper single boll weight. This study proposes a method to realize the large scale prediction of single boll weight, which provided a new idea for cotton yield prediction and breeding screening.

Citrus greening, also named citrus huanglongbing (HLB) is a very destructive disease in citrus production that has caused massive economy damage all over the world. Early and accurate detection of HLB infected tree is a critical management step to control the spread of this disease. However, the leaves of HLB infected citrus trees are alike the one of nutrient deficiency and other diseases. In view of this, a detection method of HLB based on visible spectrum image processing and C-SVC (cost-support vector classification) was investigated in the study. Different classes of visible images of citrus leaves (nutrition-deficient, healthy, HLB, etiolated) under natural light were collected and preprocessed to extract the texture and histograms of image color space (gray and HSI), followed by feature modeling and recognition of the existence of HLB based on C-SVC. The experimental results show that the proposed HLB recognition method achieved about 91.93% with low-cost and low-computation complexity.

An unmanned aerial vehicle based automatic control spraying system was designed.A highly integrated micro-computer with an independent functional module was used.Plenty of experiments was performed to show the effectiveness of this system.A minimum coefficient of variation was 25%, when flying at common work environment. To perform plant-protection operations, an unmanned aerial vehicle (UAV) based automatic control spraying system was designed in China. The system used a highly integrated and ultra-low power MSP430 single-chip micro-computer with an independent functional module. This allowed route planning software to direct the UAV to the desired spray area. The test results of route precision showed that in a 3-4m/s crosswind, route deviations were around 0.2m. The result of multiple-spraying swath uniformity tests showed a minimum coefficient of variation of 25% when flying at a height of 5m with a spraying swath of 7m and a wind speed of 0-2m/s. When the spraying swath was 9m or 5m, the coefficients were 34% and 41%, respectively. Spray uniformity for these UAV tests were superior to the Standard Requirement for ultra-low volume spraying variation coefficient, 60%.

Bacterial wilt (BW) caused by Ralstonia solanacearum is the most serious peanut diseases in South China. Its timely and accurate detection is important to opportunely implement disease management practices. This study aimed to establish and select the most appropriate leaf-level reflectance-based vegetation indices for BW detection and to determine whether these new indices can be used in UAV multispectral imaging for peanut BW detection. ANOVA, multilayer perception, and the reduced sampling method were used to analyze the spectral data. The most effective detection wavelengths, 730 nm and 790 nm, were used for developing new peanut BW detection indices. The 15 hyperspectral indices with highest correlation coefficients (R > 0.80) were obtained based on 46 hyperspectral indices and the BW severity results from Experiment 1. By testing the above vegetation indices at the leaf level and in UAV images using different methods and the results from Experiment 2, it was found that four of the developed indices (BWI1, BWI3, BWI4, and BWI6) performed appropriately (P 1.0), as they could distinguish between healthy and BW infected peanut plants, even if the plant presented minimal external symptoms. Our findings confirmed the potential of hyperspectral remote sensing including leaf-level and UAV images for peanut BW detection at early disease stages and discrimination of different BW severity levels based on vegetation indices derived from leaf-level reflectance. Timely BW severity determination based on our results could provide farmers with useful information to control peanut BW disease.

Hedera helix L. (common ivy) grows worldwide and is an evergreen root-climbing liana commonly used in parks, green belts (Metcalfe 2005), indoors as an ornamental, and as a traditional medicinal plant in China. In July 2019, a leaf wilt was observed on H. helix in Xiaofuhe wetland of Zibo, Shandong Province, China (36.79°N, 117.98°E). More than 100 m² of planting area was investigated, and the disease incidence reached about 30%. The place where the disease was found was a wetland park, mainly in a watery and humid environment. Initial symptoms on leaves appeared as small water-soaked lesions, yellow or brown in color and round or oval in shape. As the disease progressed, the area of the lesions gradually increased, forming round or irregular brown necrotic spots surrounded by a yellow halo, which eventually resulted in a wilt and death. To determine the causal pathogen, small pieces of the diseased tissue were collected and surface sterilized by 75% ethanol for 30 s followed by 5% NaClO for 1 min and then rinsed three times with sterile water. The treated samples were plated on potato dextrose agar (PDA) and incubated at 25°C for 2 days, and nine fungal isolates were obtained from 10 diseased leaves. The colonies were initially white, gradually turning gray-green to dark gray after 7 days, and the margin of the colony was regular, with abundant gray aerial mycelium. To verify identity, the partial coding genes of the internal transcribed spacer region, beta-tubulin, and translation elongation factor 1 alpha genes from the original isolate were amplified with primers ITS1/ITS4 (White et al. 1990), BT-2a/BT-2b (Glass et al. 1995), and EF1-728F/EF1-986R (Carbone et al. 1999), respectively. The sequenced genes (GenBank accession nos. MN633360, MN642587, and MN642588) exhibited 99.46, 100.00, and 99.00% homology with type specimen of Botryosphaeria dothidea CBS115476 (GenBank accession nos. KF293866, EU673106, and AY573218), respectively. The isolates were confirmed as B. dothidea on the basis of morphological and molecular results (Zhai et al. 2014). To assess pathogenicity, five healthy leaves from each of three 6-month-old healthy H. helix plants were wound inoculated with a sterilized needle. Mycelial plugs (about 5 mm in diameter) of B. dothidea from a 7-day-old culture on PDA were placed on the wounds, and five other sterile PDA plugs served as negative controls on the same leaves. All treated leaves and the whole plants were covered by sterilized plastic film and sprayed with water as needed. Ten days later, all inoculated plants developed similar symptoms to those previously observed outdoors, and the reisolated pathogen was identified as B. dothidea based on morphological and molecular results, whereas no symptoms appeared and no pathogen was isolated on negative controls. The pathogenicity test was repeated three times and showed similar results, confirming Koch’s postulates. To our knowledge, this is the first report on the presence of B. dothidea affecting H. helix plants in China. The discovery of this new disease is beneficial to the application and protection of H. helix, a common landscape and medicinal plant.

Small-scale plant protection Unmanned Aerial Vehicles (UAVs) are finding a wide range of appli cations in modern agriculture management (including aerial spraying) due to their high efficiency and flexibility, low labour/water requirement and no damage to crops and soils, which substantially increase agricultural productivity and sustainability. UAV operational parameters, however, have remarkable effects on droplet distribution in UAV spraying, which significantly affect pesticide utilization rate and treatment effectiveness. Therefore, this work aimed to evaluate the effects of UAV operational parameters on droplet distribution for orchard trees. In particular, peach, an important orchard tree worldwide, is investigated in this study, and two typical tree shapes were considered including Y-shape and Central Leader (CL)-shape. Specifically, UAV spraying experiments were performed in Shandong Institute of Pomology, Shandong Province, China, and gas powered helicopter 3WQF120-12 was chosen as the spraying platform. The UAV operational parameters under consideration include flight route (intra-row, inter-row), flight velocity (four levels: 2, 3, 4, 5 m/s), number of spray times (1 vs 2) and nozzle flow rate. Droplet coverage rate at different positions and layers, obtained by water sensitive papers, was chosen as the metric to evaluate spraying performance. Experimental results show that: (1) the spraying uniformity is different between Y-shape and CL-shape peach tree, where Y-shape exhibits uniformity for positions at inner or outer layers. CL-shape results in a higher droplet coverage at top layer while with uniformity at lower three layers; (2) for Y-shape peach, intra-row route obtained a higher droplet coverage rate; while for CL-shape peach inter-row not only saved spraying volume but also results in a higher droplet coverage rate; (3) for both tree shapes, the increase in flight velocity (2–5 m/s) significantly decreased the droplet coverage rate; (4)for Y-shape peach with doubling the number of spraying times decreased the spraying performance for unit area.(5) for CL-shape peach with intra-row route, increasing the nozzle flow rate from 1.8 to 2.2 L · min - 1 can significantly improve the droplet coverage rate at top and bottom two layers. It is envisioned that this study can provide some fundamental guidance of the operation of small UAVs for the aerial spraying of peach trees and similar orchards.

Previous studies have confirmed that choosing nozzles that produce coarser droplets could reduce the risk of pesticide spray drift, but this conclusion is based on a large volume of application, and it is easy to ignore how this impacts the control effect. The difference from the conventional spray is that the carrier volume of Unmanned Aerial Vehicle (UAV) is very limited. Little was known about how to choose suitable nozzles with UAV’s limited volume to ensure appropriate pest control. Droplet deposition with the addition of adjuvant and the LU110-010, LU110-015, and LU110-020 nozzles and control of planthoppers within nozzles treatments were studied by a quadrotor UAV in rice (Tillering and Flowering stages). Allura Red (10 g/L) was used as a tracer and Kromekote cards were used to collect droplet deposits. The results indicate that the density of the droplets covered by the LU110-01 nozzle is well above other treatments, while the differences in droplet deposition and coverage are not significant. The deposition and coverage were improved with the addition of adjuvant, especially in LU110-01 nozzles’ treatment. The control effects of rice planthoppers treated by LU110-01 nozzle were 89.4% and 90.8% respectively, which were much higher than 67.6% and 58.5% of LU110-020 nozzle at 7 days in the Tillering and Flowering stage. The results suggest that selecting a nozzle with a small atomizing particle size for UAV could improve the control effect of planthoppers.

In recent years, with the large-scale application of unmanned aerial vehicles (UAV) in agricultural plant protection, various shortcomings of have been identified; for example, the rotor flow field of the UAV will cause drift of the droplets, resulting in waste and secondary disaster. Therefore, digital simulation has become a necessity. However, due to the complexity of the rotor flow field of the rotor UAV and the operating environment of the UAV, digital simulation is associated with a large workload and great computational costs. It is urgent to explore new models using deep learning to identify the law of the rotor flow field. To address this problem, deep learning, in combination with flow field methods, is used to explore new models in this paper. A generative adversarial network (GAN) prediction model is proposed in this paper. The GAN includes a generation network and a discrimination network. In this paper, the features of the flow field are learned by the generative network to identify deep features of the flow field. The discrimination network distinguishes between true and false pictures by extracting features during training to realize adversarial training. This model can predict the flow field by identifying features of the flow-field distribution in training samples to build a predictive model. The compression effects of the computational fluid dynamics (CFD) model and the GAN model are compared in this paper. The GAN model outperforms the CFD model in predicting the flow field and compressing the data.

Plant protection unmanned aerial vehicles (UAVs) consist of light and small UAVs with pesticide spraying equipment. The advantage of UAVs is using low-volume spray technology to replace the traditional large-volume mass locomotive spray technology. Defoliant spraying is a key link in the mechanized cotton harvest, as sufficient and uniform spraying can improve the defoliation quality and decrease the cotton trash content. However, cotton is planted at high density in Xinjiang, with leaves in two adjacent rows seriously overlapped, making the lower leaves poorly sprayed. Thus, the defoliation effect is poor, and the cotton quality is degraded. To improve the effect of defoliation and reduce the losses caused by boom sprayer rolling, the effect of defoliant dosage on defoliation, boll opening, absorption and decontamination in cotton leaves and the effect of spraying volume on absorption and decontamination in cotton leaves sprayed by UAVs are studied. The pooled results indicate that plant protection UAVs could be used for cotton defoliants spraying with a twice defoliant spraying strategy, and the defoliant dosage has no significant effect on seed cotton yield and fiber quality in Xinjiang. The residue of thidiazuron in cotton leaves reaches the maximum at four days after spraying, the residue of diuron in cotton leaves reaches the maximum at one day after second spraying. The thidiazuron and diuron residues are increased with spraying volume at rang of 17.6&ndash, 29.0 L/ha. When the spraying volume is less than 17.6 L/ha, the residue of thidiazuron and diuron is reduced. The research results could provide a reference for further optimization of the spraying parameters of cotton defoliant by plant protection UAVs.