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Although drift is not a new issue, it deserves further attention for Unmanned Aerial Spraying Systems (UASS). The use of UASS as a spraying tool for Plant Protection Products is currently explored and applied worldwide. They boast different benefits such as reduced applicator exposure, high operating efficiency and are unconcerned by field-related constraints (ground slope, ground resistance). This review summarizes UASS characteristics, spray drift and the factors affecting UASS drift, and further research that still needs to be developed. The distinctive features of UASS comprise the existence of one or more rotors, relatively higher spraying altitude, faster-flying speed, and limited payload. This study highlights that due to most of these features, the drift of UASS may be inevitable. However, this drift could be effectively reduced by optimizing the structural layout of the rotor and spraying system, adjusting the operating parameters, and establishing a drift buffer zone. Further efforts are still necessary to better assess the drift characteristics of UASS, establish drift models from typical models, crops, and climate environments, and discuss standard methods for measuring UASS drift., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Douzals, Lan, Cotteux, Delpuech, Pouxviel and Zhan.)

Background: 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 ha -1 ) in both wind tunnel and field trials., Results: The 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 of <200 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., Conclusion: 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., (© 2022 Society of Chemical Industry.)

Background: 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 ha -1 ) and with or without a commercial surfactant adjuvant were evaluated based on the spot spray mode., Results: The 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 cm -2 and penetration of 3.09-66.73%. The droplet size also increased significantly, with volume median diameter (D V0.5 ) of 469 μm., Conclusion: The 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., (© 2022 Society of Chemical Industry.)

The use of plant-based indicators and other conventional means to detect the level of water stress in crops may be challenging, due to their difficulties in automation, their arduousness, and their time-consuming nature. Non-contact and non-destructive sensing methods can be used to detect the level of water stress in plants continuously and to provide automatic sensing and controls. This research aimed at determining the viability, efficiency, and swiftness in employing the commercial Workswell WIRIS Agro R infrared camera (WWARIC) in monitoring water stress and scheduling appropriate irrigation regimes in mandarin plants. The experiment used a four-by-three randomized complete block design with 80−100% FC water treatment as full field capacity and three deficit irrigation treatments at 70−75% FC, 60−65% FC, and 50−55% FC. Air temperature, canopy temperature, and vapor pressure deficits were measured and employed to deduce the empirical crop water stress index, using the Idso approach (CWSI(Idso)) as well as baseline equations to calculate non-water stress and water stressed conditions. The relative leaf water content (RLWC) of mandarin plants was also determined for the growing season. From the experiment, CWSI(Idso) and CWSI were estimated using the Workswell Wiris Agro R infrared camera (CWSIW) and showed a high correlation (R2 = 0.75 at p < 0.05) in assessing the extent of water stress in mandarin plants. The results also showed that at an altitude of 12 m above the mandarin canopy, the WWARIC was able to identify water stress using three modes (empirical, differential, and theoretical). The WWARIC’s color map feature, presented in real time, makes the camera a suitable device, as there is no need for complex computations or expert advice before determining the extent of the stress the crops are subjected to. The results prove that this novel use of the WWARIC demonstrated sufficient precision, swiftness, and intelligibility in the real-time detection of the mandarin water stress index and, accordingly, assisted in scheduling irrigation.

The effects of external factors such as temperature, humidity, pesticide formulation, and pesticide concentration on the contact angle of pesticide droplets on rice leaf surfaces were analyzed. The experiments showed that there were significant differences in the contact angles of droplets on the leaf surfaces under different temperatures and humidity. As the ambient temperature increased, the contact angle first decreased and then increased, reaching a minimum value at 25°C. With a gradual increase in humidity, the contact angle significantly increased and reached a maximum at 100% humidity. Finally, it was concluded that both the formulation and concentration of the pesticide had a significant effect on the contact angle of droplets on rice leaf surfaces. The experiments also illustrated that the effects of the pesticide formulation and concentration on the contact angle were more significant than those of temperature and humidity., Competing Interests: Conflicts of interestThe authors declare no conflicts of interest., (© Pesticide Science Society of Japan 2022. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

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.

The precision detection of dense small targets in orchards is critical for the visual perception of agricultural picking robots. At present, the visual detection algorithms for plums still have a poor recognition effect due to the characteristics of small plum shapes and dense growth. Thus, this paper proposed a lightweight model based on the improved You Only Look Once version 4 (YOLOv4) to detect dense plums in orchards. First, we employed a data augmentation method based on category balance to alleviate the imbalance in the number of plums of different maturity levels and insufficient data quantity. Second, we abandoned Center and Scale Prediction Darknet53 (CSPDarknet53) and chose a lighter MobilenetV3 on selecting backbone feature extraction networks. In the feature fusion stage, we used depthwise separable convolution (DSC) instead of standard convolution to achieve the purpose of reducing model parameters. To solve the insufficient feature extraction problem of dense targets, this model achieved fine-grained detection by introducing a 152 × 152 feature layer. The Focal loss and complete intersection over union (CIOU) loss were joined to balance the contribution of hard-to-classify and easy-to-classify samples to the total loss. Then, the improved model was trained through transfer learning at different stages. Finally, several groups of detection experiments were designed to evaluate the performance of the improved model. The results showed that the improved YOLOv4 model had the best mean average precision (mAP) performance than YOLOv4, YOLOv4-tiny, and MobileNet-Single Shot Multibox Detector (MobileNet-SSD). Compared with some results from the YOLOv4 model, the model size of the improved model is compressed by 77.85%, the parameters are only 17.92% of the original model parameters, and the detection speed is accelerated by 112%. In addition, the influence of the automatic data balance algorithm on the accuracy of the model and the detection effect of the improved model under different illumination angles, different intensity levels, and different types of occlusions were discussed in this paper. It is indicated that the improved detection model has strong robustness and high accuracy under the real natural environment, which can provide data reference for the subsequent orchard yield estimation and engineering applications of robot picking work., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Zhao, Liu, Li, Chen and Lan.)

Adding tank-mix adjuvants into the spray mixture is a common practice to improve droplet distribution for field crops ( e.g ., rice, wheat, corn, etc .) when using Unmanned Aerial Vehicle (UAV) sprayers. However, the effectiveness of tank-mix adjuvant for UAV spraying in orchard crops is still an open problem, considering their special canopy structure and leaf features. This study aims to evaluate the effects of a typical tank-mix adjuvant concentrations ( i.e ., Nong Jian Fei (NJF)) on Contact Angle (CA) and droplet distribution in the citrus tree canopy. Three commonly used parameters, namely dynamic CA, droplet coverage, and Volume Median Diameter (VMD), are adopted for performance evaluation. The dynamic CAs on the adaxial surface of citrus leaves, for water-only and NJF-presence sprays, respectively, are measured with five concentration levels, where three replications are performed for each concentration. The sprays with 0.5‰ NJF are adopted in the field experiment for evaluating droplet distributions, where Water Sensitive Papers (WSPs) are used as collectors. Two multi-rotor UAVs (DJI T20 and T30) which consist of different sizes of pesticide tanks and rotor diameters are used as the spraying platforms. Both water-only and NJF-presence treatments are conducted for the two UAVs, respectively. The results of the CA experiment show that NJF addition can significantly reduce the CAs of the sprays. The sprays with 0.5‰ NJF obtain the lowest CA within the observing time, suggesting a better spread ability on solid surface ( e.g ., WSPs or/and leaves). With respect to the effects of NJF addition on individual UAVs, the field trial results indicate that NJF addition can remarkably increase both the droplet coverage and VMD at three canopy layers, except for T30 droplet coverage of the inside and bottom layers. Comparing the difference of droplet coverage between two UAVs, while significant difference is found in the same layer before NJF addition, there is no notable difference appearing in the outside and bottom layers after NJF addition. The difference of VMD in the same layer between two UAVs is not affected by NJF addition except for the bottom layer. These results imply that the differences of droplet coverage among different UAVs might be mitigated, thus the droplet distribution of some UAVs could be improved by adding a tank-mix adjuvant into the sprays. This hypothesis is verified by investigating the droplet penetration and the correlation coefficient (CC) of droplet coverage and VMD. After NJF addition, the total percentage of T20 droplet coverage in the bottom and inside layers is increased by 5%. For both UAVs, the CCs indicate that both droplet coverage and VMD increase at the same time in most cases after NJF addition. In conclusion, the addition of a tank-mix adjuvant with the ability to reduce CA of the sprays, can effectively improve droplet distribution using UAV spraying in the citrus canopy by increasing droplet coverage and VMD., Competing Interests: Jiyuan Su is employed by Guilin Jiqi Biochemical Company., (© 2022 Meng et al.)

Citrus psyllid is the only insect vector of citrus Huanglongbing (HLB), which is the most destructive disease in the citrus industry. There is no effective treatment for HLB, so detecting citrus psyllids as soon as possible is the key prevention measure for citrus HLB. It is time-consuming and laborious to search for citrus psyllids through artificial patrol, which is inconvenient for the management of citrus orchards. With the development of artificial intelligence technology, a computer vision method instead of the artificial patrol can be adopted for orchard management to reduce the cost and time. The citrus psyllid is small in shape and gray in color, similar to the stem, stump, and withered part of the leaves, leading to difficulty for the traditional target detection algorithm to achieve a good recognition effect. In this work, in order to make the model have good generalization ability under outdoor light condition, a high-definition camera to collect data set of citrus psyllids and citrus fruit flies under natural light condition was used, a method to increase the number of small target pests in citrus based on semantic segmentation algorithm was proposed, and the cascade region-based convolution neural networks (R-CNN) (convolutional neural network) algorithm was improved to enhance the recognition effect of small target pests using multiscale training, combining CBAM attention mechanism with high-resolution feature retention network high-resoultion network (HRNet) as feature extraction network, adding sawtooth atrous spatial pyramid pooling (ASPP) structure to fully extract high-resolution features from different scales, and adding feature pyramid networks (FPN) structure for feature fusion at different scales. To mine difficult samples more deeply, an online hard sample mining strategy was adopted in the process of model sampling. The results show that the improved cascade R-CNN algorithm after training has an average recognition accuracy of 88.78% for citrus psyllids. Compared with VGG16, ResNet50, and other common networks, the improved small target recognition algorithm obtains the highest recognition performance. Experimental results also show that the improved cascade R-CNN algorithm not only performs well in citrus psylla identification but also in other small targets such as citrus fruit flies, which makes it possible and feasible to detect small target pests with a field high-definition camera., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dai, Wang, Yang, Lin, Chen, Lan and Deng.)

Ganoderma lucidum is a traditional Chinese healthy food with many kinds of nutritious activities, and polysaccharide is one of its main active components. Ganoderma lucidum polysaccharide plays a vital role in improving human immunity and anti-oxidation. At present, the methods of detecting polysaccharide content of Ganoderma lucidum are destructive, and the steps are complicated and time-consuming. This study aims to explore the possibility of using hyperspectral imaging (HSI) to predict polysaccharide content in a nondestructive way during the growth of Ganoderma lucidum. The partial least square regression (PLSR) model shows good performance for Ganoderma lucidum ( R p 2  = 0.924, R P D p  = 3.622) with pretreatment method of Savitzky-Golay (SG) and standard normal variate (SNV), and feature selection method of successive projections algorithm (SPA). This study indicates that HSI can quickly and nondestructive detect the polysaccharide content of Ganoderma lucidum, provide guidance for the cultivation industry and improve the economic benefits of Ganoderma lucidum., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Citrus Huanglongbing (HLB), also named citrus greening disease, occurs worldwide and is known as a citrus cancer without an effective treatment. The symptoms of HLB are similar to those of nutritional deficiency or other disease. The methods based on single-source information, such as RGB images or hyperspectral data, are not able to achieve great detection performance. In this study, a multi-modal feature fusion network, combining a RGB image network and hyperspectral band extraction network, was proposed to recognize HLB from four categories (HLB, suspected HLB, Zn-deficient, and healthy). Three contributions including a dimension-reduction scheme for hyperspectral data based on a soft attention mechanism, a feature fusion proposal based on a bilinear fusion method, and auxiliary classifiers to extract more useful information are introduced in this manuscript. The multi-modal feature fusion network can effectively classify the above four types of citrus leaves and is better than single-modal classifiers. In experiments, the highest accuracy of multi-modal network recognition was 97.89% when the amount of data was not very abundant (1,325 images of the four aforementioned types and 1,325 pieces of hyperspectral data), while the single-modal network with RGB images only achieved 87.98% recognition and the single-modal network using hyperspectral information only 89%. Results show that the proposed multi-modal network implementing the concept of multi-source information fusion provides a better way to detect citrus HLB and citrus deficiency., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Yang, Wang, Hu, Lan and Deng.)

Drought and water scarcity due to global warming, climate change, and social development have been the most death-defying threat to global agriculture production for the optimization of water and food security. Reflectance indices obtained by an Analytical Spectral Device (ASD) Spec 4 hyperspectral spectrometer from tomato growth in two soil texture types exposed to four water stress levels (70-100% FC, 60-70% FC, 50-60% FC, and 40-50% FC) was deployed to schedule irrigation and management of crops' water stress. The treatments were replicated four times in a randomized complete block design (RCBD) in a 2 × 4 factorial experiment. Water stress treatments were monitored with Time Domain Reflectometer (TDR) every 12 h before and after irrigation to maintain soil water content at the desired (FC%). Soil electrical conductivity (Ec) was measured daily throughout the growth cycle of tomatoes in both soil types. Ec was revealing a strong correlation with water stress at R 2 above 0.95 p < 0.001. Yield was measured at the end of the end of the growing season. The results revealed that yield had a high correlation with water stress at R 2 = 0.9758 and 0.9816 p < 0.01 for sandy loam and silty loam soils, respectively. Leaf temperature (LT °C), relative leaf water content (RLWC), leaf chlorophyll content (LCC), Leaf area index (LAI), were measured at each growth stage at the same time spectral reflectance data were measured throughout the growth period. Spectral reflectance indices used were grouped into three: (1) greenness vegetative indices; (2) water overtone vegetation indices; (3) Photochemical Reflectance Index centered at 570 nm (PRI 570 ), and normalized PRI (PRInorm). These reflectance indices were strongly correlated with all four water stress indicators and yield. The results revealed that NDVI, RDVI, WI, NDWI, NDWI 1640 , PRI 570 , and PRInorm were the most sensitive indices for estimating crop water stress at each growth stage in both sandy loam and silty loam soils at R 2 above 0.35. This study recounts the depth of 858 to 1640 nm band absorption to water stress estimation, comparing it to other band depths to give an insight into the usefulness of ground-based hyperspectral reflectance indices for assessing crop water stress at different growth stages in different soil types.

The goal of this research is to use a WORKSWELL WIRIS AGRO R INFRARED CAMERA (WWARIC) to assess the crop water stress index (CWSI W ) on tomato growth in two soil types. This normalized index (CWSI) can map water stress to prevent drought, mapping yield, and irrigation scheduling. The canopy temperature, air temperature, and vapor pressure deficit were measured and used to calculate the empirical value of the CWSI based on the Idso approach (CWSI Idso ). The vegetation water content (VWC) was also measured at each growth stage of tomato growth. The research was conducted as a 2 × 4 factorial experiment arranged in a Completely Randomized Block Design. The treatments imposed were two soil types: sandy loam and silt loam, with four water stress treatment levels at 70-100% FC, 60-70% FC, 50-60% FC, and 40-50% FC on the growth of tomatoes to assess the water stress. The results revealed that CWSI Idso and CWSI W proved a strong correlation in estimating the crop water status at R 2 above 0.60 at each growth stage in both soil types. The fruit expansion stage showed the highest correlation at R 2 = 0.8363 in sandy loam and R 2 = 0.7611 in silt loam. VWC and CWSI W showed a negative relationship with a strong correlation at all the growth stages with R 2 values above 0.8 at p < 0.05 in both soil types. Similarly, the CWSI W and yield also showed a negative relationship and a strong correlation with R 2 values above 0.95, which indicated that increasing the CWSI W had a negative effect on the yield. However, the total marketable yield ranged from 2.02 to 6.8 kg plant -1 in sandy loam soil and 1.75 to 5.4 kg plant -1 in silty loam soil from a low to high CWSI W . The highest mean marketable yield was obtained in sandy loam soil at 70-100% FC (0.0 < CWSI W ≤ 0.25), while the least-marketable yield was obtained in silty loam soil 40-50% FC (0.75 < CWSI W ≤ 1.0); hence, it is ideal for maintaining the crop water status between 0.0 < CWSI W ≤ 0.25 for the optimum yield. These experimental results proved that the WWARIC effectively assesses the crop water stress index (CWSI W ) in tomatoes for mapping the yield and irrigation scheduling.

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., (© 2021 Society of Chemical Industry.)

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.

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.

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., (© 2019 Society of Chemical Industry.)

With the steady progress of China's agricultural modernization, the demand for agricultural machinery for production is widely growing. Agricultural unmanned aerial vehicles (UAVs) are becoming a new force in the field of precision agricultural aviation in China. In those agricultural areas where ground-based machinery have difficulties in executing farming operations, agricultural UAVs have shown obvious advantages. With the development of precision agricultural aviation technology, one of the inevitable trends is to realize autonomous identification of obstacles and real-time obstacle avoidance (OA) for agricultural UAVs. However, the complex farmland environment and changing obstacles both increase the complexity of OA research. The objective of this paper is to introduce the development of agricultural UAV OA technology in China. It classifies the farmland obstacles in two ways and puts forward the OA zones and related avoidance tactics, which helps to improve the safety of aviation operations. This paper presents a comparative analysis of domestic applications of agricultural UAV OA technology, features, hotspot and future research directions. The agricultural UAV OA technology of China is still at an early development stage and many barriers still need to be overcome., Competing Interests: The authors declare no conflict of interest.

Chemical control is necessary in order to control weed infestation and to ensure a rice yield. However, excessive use of herbicides has caused serious agronomic and environmental problems. Site specific weed management (SSWM) recommends an appropriate dose of herbicides according to the weed coverage, which may reduce the use of herbicides while enhancing their chemical effects. In the context of SSWM, the weed cover map and prescription map must be generated in order to carry out the accurate spraying. In this paper, high resolution unmanned aerial vehicle (UAV) imagery were captured over a rice field. Different workflows were evaluated to generate the weed cover map for the whole field. Fully convolutional networks (FCN) was applied for a pixel-level classification. Theoretical analysis and practical evaluation were carried out to seek for an architecture improvement and performance boost. A chessboard segmentation process was used to build the grid framework of the prescription map. The experimental results showed that the overall accuracy and mean intersection over union (mean IU) for weed mapping using FCN-4s were 0.9196 and 0.8473, and the total time (including the data collection and data processing) required to generate the weed cover map for the entire field (50 × 60 m) was less than half an hour. Different weed thresholds (0.00⁻0.25, with an interval of 0.05) were used for the prescription map generation. High accuracies (above 0.94) were observed for all of the threshold values, and the relevant herbicide saving ranged from 58.3% to 70.8%. All of the experimental results demonstrated that the method used in this work has the potential to produce an accurate weed cover map and prescription map in SSWM applications.

: Remote sensing can be a rapid, accurate, and simple method for assessing pest damage on plants. The objectives of this study were to identify spectral wavelengths sensitive to cotton aphid infestation. Then, the normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the leaf spectrum were obtained within 350⁻2500 nm, and their correlation with infestation were qualified. The results showed that leaf spectral reflectance decreased in the visible range (350⁻700 nm) and the near-infrared range (NIR, 700⁻1300 nm) as aphid damage severity increased, and significant differences were found in blue, green, red, NIR and short-wave infrared (SWIR) band regions between different grades of aphid damage severity. Decrease in Chlorophyll a (Chl a) pigment was more significant than that in Chlorophyll (Chl b) in the infested plants and the Chl a/b ratio showed a decreasing trend with increase in aphid damage severity. The sensitive spectral bands were mainly within NIR and SWIR ranges. The best spectral indices NDSI (R 678 , R 1471 ) and RSI (R 1975 , R 1904 ) were formulated with these sensitive spectral regions through reducing precise sampling method. These new indices along with 16 other stress related indices compiled from literature were further tested for their ability to detect aphid damage severity. The two indices in this study showed significantly higher coefficients of determination (R² of 0.81 and 0.81, p < 0.01) and the least RMSE values (RMSE of 0.50 and 0.49), and hence have potential application in assessing aphid infestation severity in cotton., Competing Interests: The authors declare no conflict of interest.

Weed control is necessary in rice cultivation, but the excessive use of herbicide treatments has led to serious agronomic and environmental problems. Suitable site-specific weed management (SSWM) is a solution to address this problem while maintaining the rice production quality and quantity. In the context of SSWM, an accurate weed distribution map is needed to provide decision support information for herbicide treatment. UAV remote sensing offers an efficient and effective platform to monitor weeds thanks to its high spatial resolution. In this work, UAV imagery was captured in a rice field located in South China. A semantic labeling approach was adopted to generate the weed distribution maps of the UAV imagery. An ImageNet pre-trained CNN with residual framework was adapted in a fully convolutional form, and transferred to our dataset by fine-tuning. Atrous convolution was applied to extend the field of view of convolutional filters; the performance of multi-scale processing was evaluated; and a fully connected conditional random field (CRF) was applied after the CNN to further refine the spatial details. Finally, our approach was compared with the pixel-based-SVM and the classical FCN-8s. Experimental results demonstrated that our approach achieved the best performance in terms of accuracy. Especially for the detection of small weed patches in the imagery, our approach significantly outperformed other methods. The mean intersection over union (mean IU), overall accuracy, and Kappa coefficient of our method were 0.7751, 0.9445, and 0.9128, respectively. The experiments showed that our approach has high potential in accurate weed mapping of UAV imagery.

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.

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&#95;based CNN algorithm and Pixel&#95;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.

Objective: To investigate the effectiveness of reconstructing medial patellofemoral ligament with hamstring tendon autografts for the treatment of recurrent patellar dislocation under arthroscopy., Methods: Between January 2005 and January 2010, 22 cases of recurrent patellar dislocation were treated by lateral retinacular release and reconstruction of the medial patellofemoral ligament with hamstring tendon autografts under arthroscopy. There were 5 males and 17 females, aged 15-19 years (mean, 17.3 years). The average number of dislocation was 4 (range, 3-8). The main clinical symptoms were pain and swelling of knee joint, weakness in the leg, and limited range of motion (ROM). The patellar tilt test, pressing pain of patellofemoral ligament insertion, and apprehension sign showed positive results. According to International Knee Documentation Committee (IKDC) scoring criteria, the subjective IKDC score was 36.7 +/- 4.7, and the Lysholm score was 69.3 +/- 3.8. X-ray films showed that the patella inclined outwards., Results: All incisions healed by first intention. Twenty-two cases were followed up 18-49 months (mean, 34 months). Pain and swelling of knee joint and weakness were improved obviously. No recurrence was found during follow-up. The ROM of knee in flexion and extension was improved when compared with preoperative ROM. The subjective IKDC score was 92.4 +/- 5.3 and the Lysholm knee score was 91.7 +/- 5.2, showing significant differences when compared with preoperative scores (P < 0.05)., Conclusion: Reconstruction of the medial patellofemoral ligament with hamstring tendon autografts under arthroscopy is an effective method to treat recurrent patellar dislocation.