Visible and NIR microscopic hyperspectrum reconstruction from RGB images with deep convolutional neural networks.

We investigate the microscopic hyperspectral reconstruction from RGB images with a deep convolutional neural network (DCNN) in this paper. Based on the microscopic hyperspectral imaging system, a homemade dataset consisted of microscopic hyperspectral and RGB image pairs is constructed. For considering the importance of spectral correlation between neighbor spectral bands in microscopic hyperspectrum reconstruction, the 2D convolution is replaced by 3D convolution in the DCNN framework, and a metric (weight factor) used to evaluate the performance reconstructed hyperspectrum is also introduced into the loss function used in training. The effects of the dimension of convolution kernel and the weight factor in the loss function on the performance of the reconstruction model are studied. The overall results indicate that our model can show better performance than the traditional models applied to reconstruct the hyperspectral images based on DCNN for the public and the homemade microscopic datasets. In addition, we furthermore explore the microscopic hyperspectrum reconstruction from RGB images in infrared region, and the results show that the model proposed in this paper has great potential to expand the reconstructed hyperspectrum wavelength range from the visible to near infrared bands.

Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus.

BACKGROUND: Astragalus is a widely used traditional Chinese medicine material that is easily confused due to its quality, price and other factors derived from different origins. This article describes a novel method for the rapid tracing and detection of Astragalus via the joint application of an electronic tongue (ET) and an electronic eye (EE) combined with a lightweight convoluted neural network (CNN)-transformer model. First, ET and EE systems were employed to measure the taste fingerprints and appearance images, respectively, of different Astragalus samples. Three spectral transform methods - the Markov transition field, short-time Fourier transform and recurrence plot - were utilized to convert the ET signals into 2D spectrograms. Then, the obtained ET spectrograms were fused with the EE image to obtain multimodal information. A lightweight hybrid model, termed GETNet, was designed to achieve pattern recognition for the Astragalus fusion information. The proposed model employed an improved transformer module and an improved Ghost bottleneck as its backbone network, complementarily utilizing the benefits of CNN and transformer architectures for local and global feature representation. Furthermore, the Ghost bottleneck was further optimized using a channel attention technique, which boosted the model's feature extraction effectiveness. RESULTS: The experiments indicate that the proposed data fusion strategy based on ET and EE devices has better recognition accuracy than that attained with independent sensing devices. CONCLUSION: The proposed method achieved high precision (99.1%) and recall (99.1%) values, providing a novel approach for rapidly identifying the origin of Astragalus, and it holds great promise for applications involving other types of Chinese herbal medicines. © 2024 Society of Chemical Industry.

A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI.

LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this can easily lead to loss of repositioning or low repositioning accuracy. In this paper, a method of LiDAR layout and field of view selection based on high-precision map normal distribution transformation (NDT) relocation is proposed to solve the problem of large NDT relocation error and position loss when the occlusion field of view is too large. To simulate the real placement environment and the LiDAR obstructed by obstacles, the ROI algorithm is used to cut LiDAR point clouds and to obtain LiDAR point cloud data of different sizes. The cut point cloud data is first downsampled and then relocated. The downsampling points for NDT relocation are recorded as valid matching points. The direction and angle settings of the LiDAR point cloud data are optimized using RMSE values and valid matching points. The results show that in the urban scene with complex road conditions, there are more front and rear matching points than left and right matching points within the unit angle. The more matching points of the NDT relocation algorithm there are, the higher the relocation accuracy. Increasing the front and rear LiDAR field of view prevents the loss of repositioning. The relocation accuracy can be improved by increasing the left and right LiDAR field of view.

Global Analysis of Lysine Lactylation of Germinated Seeds in Wheat.

Protein lactylation is a newly discovered posttranslational modification (PTM) and is involved in multiple biological processes, both in mammalian cells and rice grains. However, the function of lysine lactylation remains unexplored in wheat. In this study, we performed the first comparative proteomes and lysine lactylomes during seed germination of wheat. In total, 8000 proteins and 927 lactylated sites in 394 proteins were identified at 0 and 12 h after imbibition (HAI). Functional enrichment analysis showed that glycolysis- and TCA-cycle-related proteins were significantly enriched, and more differentially lactylated proteins were enriched in up-regulated lactylated proteins at 12 HAI vs. 0 HAI through the KEGG pathway and protein domain enrichment analysis compared to down-regulated lactylated proteins. Meanwhile, ten particularly preferred amino acids near lactylation sites were found in the embryos of germinated seeds: AA*KlaT, A***KlaD********A, KlaA**T****K, K******A*Kla, K*Kla********K, KlaA******A, Kla*A, KD****Kla, K********Kla and KlaG. These results supplied a comprehensive profile of lysine lactylation of wheat and indicated that protein lysine lactylation played important functions in several biological processes.

First Report of Alternaria alternata Causing Leaf Spot on Bellis perennis in China.

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 identification and first report of Alternaria alternata causing leaf spot on Gaillardia pulchella Foug. in Shandong province of China.

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.

Rapid Estimation of Water Stress in Choy Sum (Brassica chinensis var. parachinensis) Using Integrative Approach.

To optimize crop water consumption and adopt water-saving measures such as precision irrigation, early identification of plant water status is critical. This study explores the effectiveness of estimating water stress in choy sum (Brassica chinensis var. parachinensis) grown in pots in greenhouse conditions using Crop Water Stress Index (CWSI) and crop vegetation indicators to improve irrigation water management. Data on CWSI and Spectral reflectance were collected from choy sum plants growing in sandy loam soil with four different soil field capacities (FC): 90-100% FC as no water stress (NWS); 80-90% FC for light water stress (LWS); 70-80% FC for moderate water stress (MWS); and 60-70% FC for severe water stress (SWS). With four treatments and three replications, the experiment was set up as a completely randomized design (CRD). Throughout the growing season, plant water stress tracers such as leaf area index (LAI), canopy temperature (Tc), leaf relative water content (LRWC), leaf chlorophyll content, and yield were measured. Furthermore, CWSI estimated from the Workswell Wiris Agro R Infrared Camera (CWSIW) and spectral data acquisition from the Analytical Spectral Device on choy sum plants were studied at each growth stage. NDVI, Photochemical Reflectance Index positioned at 570 nm (PRI570), normalized PRI (PRInorm), Water Index (WI), and NDWI were the Vegetation indices (VIs) used in this study. At each growth stage, the connections between these CWSIW, VIs, and water stress indicators were statistically analyzed with R2 greater than 0.5. The results revealed that all VIs were valuable guides for diagnosing water stress in choy sum. CWSIW obtained from this study showed that Workswell Wiris Agro R Infrared Camera mounted on proximal remote sensing platform for assessing water stress in choy sum plant was rapid, non-destructive, and user friendly. Therefore, integrating CWSIW and VIs approach gives a more rapid and accurate approach for detecting water stress in choy sum grown under greenhouse conditions to optimize yield by reducing water loss and enhancing food security and sustainability.

Real-Time Assessment of Mandarin Crop Water Stress Index.

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.

Rapid Estimation of Crop Water Stress Index on Tomato Growth.

The goal of this research is to use a WORKSWELL WIRIS AGRO R INFRARED CAMERA (WWARIC) to assess the crop water stress index (CWSIW) 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 (CWSIIdso). 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 CWSIIdso and CWSIW proved a strong correlation in estimating the crop water status at R2 above 0.60 at each growth stage in both soil types. The fruit expansion stage showed the highest correlation at R2 = 0.8363 in sandy loam and R2 = 0.7611 in silt loam. VWC and CWSIW showed a negative relationship with a strong correlation at all the growth stages with R2 values above 0.8 at p < 0.05 in both soil types. Similarly, the CWSIW and yield also showed a negative relationship and a strong correlation with R2 values above 0.95, which indicated that increasing the CWSIW 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 CWSIW. The highest mean marketable yield was obtained in sandy loam soil at 70-100% FC (0.0 < CWSIW ≤ 0.25), while the least-marketable yield was obtained in silty loam soil 40-50% FC (0.75 < CWSIW ≤ 1.0); hence, it is ideal for maintaining the crop water status between 0.0 < CWSIW ≤ 0.25 for the optimum yield. These experimental results proved that the WWARIC effectively assesses the crop water stress index (CWSIW) in tomatoes for mapping the yield and irrigation scheduling.

Research on Lightweight Citrus Flowering Rate Statistical Model Combined with Anchor Frame Clustering Optimization.

At present, learning-based citrus blossom recognition models based on deep learning are highly complicated and have a large number of parameters. In order to estimate citrus flower quantities in natural orchards, this study proposes a lightweight citrus flower recognition model based on improved YOLOv4. In order to compress the backbone network, we utilize MobileNetv3 as a feature extractor, combined with deep separable convolution for further acceleration. The Cutout data enhancement method is also introduced to simulate citrus in nature for data enhancement. The test results show that the improved model has an mAP of 84.84%, 22% smaller than that of YOLOv4, and approximately two times faster. Compared with the Faster R-CNN, the improved citrus flower rate statistical model proposed in this study has the advantages of less memory usage and fast detection speed under the premise of ensuring a certain accuracy. Therefore, our solution can be used as a reference for the edge detection of citrus flowering.

Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils.

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 R2 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 R2 = 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 (PRI570), 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, NDWI1640, PRI570, 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 R2 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.

First Report of Botryosphaeria dothidea Causing Leaf Spot and Wilt on Celtis sinensis in China.

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.

First Record of Paramyrothecium roridum Causing Leaf Spot on Physostegia virginiana in China.

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.

WSN-Assisted UAV Trajectory Adjustment for Pesticide Drift Control.

Unmanned Aerial Vehicles (UAVs) have been widely applied for pesticide spraying as they have high efficiency and operational flexibility. However, the pesticide droplet drift caused by wind may decrease the pesticide spraying efficiency and pollute the environment. A precision spraying system based on an airborne meteorological monitoring platform on manned agricultural aircrafts is not adaptable for. So far, there is no better solution for controlling droplet drift outside the target area caused by wind, especially by wind gusts. In this regard, a UAV trajectory adjustment system based on Wireless Sensor Network (WSN) for pesticide drift control was proposed in this research. By collecting data from ground WSN, the UAV utilizes the wind speed and wind direction as inputs to autonomously adjust its trajectory for keeping droplet deposition in the target spraying area. Two optimized algorithms, namely deep reinforcement learning and particle swarm optimization, were applied to generate the newly modified flight route. At the same time, a simplified pesticide droplet drift model that includes wind speed and wind direction as parameters was developed and adopted to simulate and compute the drift distance of pesticide droplets. Moreover, an LSTM-based wind speed prediction model and a RNN-based wind direction prediction model were established, so as to address the problem of missing the latest wind data caused by communication latency or a lack of connection with the ground nodes. Finally, experiments were carried out to test the communication latency between UAV and ground WSN, and to evaluate the proposed scheme with embedded Raspberry Pi boards in UAV for feasibility verification. Results show that the WSN-assisted UAV trajectory adjustment system is capable of providing a better performance of on-target droplet deposition for real time pesticide spraying with UAV.

Estimation of Peanut Leaf Area Index from Unmanned Aerial Vehicle Multispectral Images.

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

A Novel Illumination Compensation Technique for Multi-Spectral Imaging in NDVI Detection.

To overcome the dependence on sunlight of multi-spectral cameras, an active light source multi-spectral imaging system was designed and a preliminary experimental study was conducted at night without solar interference. The system includes an active light source and a multi-spectral camera. The active light source consists of four integrated LED (Light Emitting Diode) arrays and adjustable constant current power supplies. The red LED arrays and the near-infrared LED arrays are each driven by an independently adjustable constant current power supply. The center wavelengths of the light source are 668 nm and 840 nm, which are consistent with that of filter lens of the Rededge-M multi-spectral camera. This paper shows that the radiation intensity measured is proportional to the drive current and is inversely proportional to the radiation distance, which is in accordance with the inverse square law of light. Taking the inverse square law of light into account, a radiation attenuation model was established based on the principle of image system and spatial geometry theory. After a verification test of the radiation attenuation model, it can be concluded that the average error between the radiation intensity obtained using this model and the actual measured value using a spectrometer is less than 0.0003 w/m2. In addition, the fitting curve of the multi-spectral image grayscale digital number (DN) and reflected radiation intensity at the 668 nm (Red light) is y = -3484230x2 + 721083x + 5558, with a determination coefficient of R2 = 0.998. The fitting curve with the 840 nm (near-infrared light) is y = 491469.88x + 3204, with a determination coefficient of R2 = 0.995, so the reflected radiation intensity on the plant canopy can be calculated according to the grayscale DN. Finally, the reflectance of red light and near-infrared light can be calculated, as well as the Normalized Difference Vegetation Index (NDVI) index. Based on the above model, four plants were placed at 2.85 m away from the active light source multi-spectral imaging system for testing. Meanwhile, NDVI index of each plant was measured by a Greenseeker hand-held crop sensor. The results show that the data from the two systems were linearly related and correlated with a coefficient of 0.995, indicating that the system in this article can effectively detect the vegetation NDVI index. If we want to use this technology for remote sensing in UAV, the radiation intensity attenuation and working distance of the light source are issues that need to be considered carefully.

Design of Plant Protection UAV Variable Spray System Based on Neural Networks.

Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized.

Applications and Prospects of Agricultural Unmanned Aerial Vehicle Obstacle Avoidance Technology in China.

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.

Detection of Stress in Cotton (Gossypium hirsutum L.) Caused by Aphids Using Leaf Level Hyperspectral Measurements.

: 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 (R678, R1471) and RSI (R1975, R1904) 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.

A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery.

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.