Detection of Rice Diseases by the Fusion of Optimization based K-means Clustering Algorithm and Faster Region based Convolutional Neural Network

One of the most important food crops in the world is rice, which is highly affected by various plant diseases and pests. Even though there are so many methods to address the concern, detection accuracy is a hectic challenge, which needs to be boosted for an enjoyable farming environment. In the present study a rice disease detection technique was implemented by the fusion of Sailfish optimization – K-means (SCM-KM) and the Faster Region Based Convolutional Neural Network (Faster R-CNN) method. For the optimization of the KM clustering method, Sailfish Optimizer was coupled with the Maximum and Minimum distance algorithm, as well as Chaos theory. The 2D Filtering Mask and Weighted Multilevel Median Filter(2DFM-AMMF) were used to eliminate the sounds. With the aid of the Faster 2D-Otsu technique, the target leaf lesion was segmented from the image. The SCM-KM method is used for detection of rice disease. The Rice diseases were characterized and classified by Region Proposal Networks (RPN) and Faster R-CNN method. Comparative analysis of the SCM-KM+ Faster R-CNN method was performed using the metrics sensitivity, accuracy, and specificity. The proposed detection method produced elevated performance over similar bench marking frameworks.

Nigrospora oryzae Causing Leaf Spot on Asiatic Dayflower in Chongqing, China

Asiatic dayflower (Commelina communis L.) is an annual herbaceous weed that is distributed throughout China. A foliar disease on Asiatic dayflowers was discovered in one farm field in Dianjiang County, Chongqing, China (N30°3´22″, E107°18´5″) in summer, 2019. The disease incidence was observed on about 10% (13/127) of the plants. Symptoms appeared as round-shaped tan lesions (2-5 mm) in diameter that occurred randomly and irregularly on the whole leaves. The centers of lesions become grayish white with reddish borders as the disease progressed. The leaves with typical symptoms were detached and wiped with 70% ethanol for surface disinfestation before isolating the causal agent. Subsequently, three pieces (3-4 mm2) of tissue were taken from the margin of the leaf lesion, disinfested in 1.5% NaClO for 1 min, rinsed 3 times in sterilized distilled water, and placed onto Potato Dextrose Agar (PDA) medium containing 50 μg/ml each of kanamycin and ampicillin. A fungus was exclusively and consistently isolated from the disinfested leaf lesion sections. The colonies on PDA grew rapidly and covered the entire petri dish within 5 days at 28℃. Colonies were at first grayish white, cotton wool-like, round, with abundant aerial mycelium, and later turned black as conidia produced. The abundant conidia formed on PDA were initially yellow brown and gradually became black, oblate to ellipsoidal, smooth, single-celled, and ranged in size from 4 to 10 × 3.5 to 9 μm. They were borne on a colorless, hyaline, and inverted flask-shaped cell at the tip of each conidiophore. The morphology characteristics were consistent with those of Nigrospora spp. (Wang et al. 2017). Genomic DNA was extracted from one representative isolate NDJ0819. The amplification and sequencing of the gene fragments including the internal transcribed space (ITS) region of ribosomal DNA and beta-tubulin were performed using the primers ITS1/ITS4 (White et al. 1990) and Bt2a/Bt2b (Glass and Donaldson 1995), respectively. Fragments of 536 bp for ITS and 408 bp for beta-tubulin were obtained. A phylogram of the combined ITS and beta-tubulin sequences reconstructed using the maximum likelihood bootstrapping method implemented in the software MEGA version 7.0 (Kumar et al. 2016) indicated that isolate NDJ0819 clustered with Nigrospora oryzae. Both ITS and beta-tubulin sequences were deposited into GenBank (accession no. MT140353 and MT157509, respectively). Pathogenicity test was performed by rub-inoculating needle-wounded leaves of three 4-week-old Asiatic dayflowers with spore suspension (2.6 × 106 conidia/ml) of NDJ0819 prepared in water containing 0.05% Tween-20, and holding plants at 28℃ in the growth chamber. The pathogenicity test was repeated twice. Brown, round-shaped lesions developed on leaves inoculated with spores at 15 days post-inoculation. However, the centers of the lesion did not become grayish white, compared to those of lesions seen in naturally infected leaves. No symptoms developed on leaves inoculated with sterilized distilled water. N. oryzae was re-isolated from the lesions. All results described above indicated that N. oryzae was responsible for the leaf spot of Asiatic dayflower. To our knowledge, this is the first report of N. oryzae causing leaf spot on Asiatic dayflower in China. Research into the potential use of N. oryzae as a candidate biological agent against the weed is worth being initiated.

An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation

Rice bacterial leaf streak (BLS) is a serious disease in rice leaves and can seriously affect the quality and quantity of rice growth. Automatic estimation of disease severity is a crucial requirement in agricultural production. To address this, a new method (termed BLSNet) was proposed for rice and BLS leaf lesion recognition and segmentation based on a UNet network in semantic segmentation. An attention mechanism and multi-scale extraction integration were used in BLSNet to improve the accuracy of lesion segmentation. We compared the performance of the proposed network with that of DeepLabv3+ and UNet as benchmark models used in semantic segmentation. It was found that the proposed BLSNet model demonstrated higher segmentation and class accuracy. A preliminary investigation of BLS disease severity estimation was carried out based on our BLS segmentation results, and it was found that the proposed BLSNet method has strong potential to be a reliable automatic estimator of BLS disease severity.

QTL Mapping for Gummy Stem Blight Resistance in Watermelon (Citrullus spp.)

Watermelon (Citrulluslanatus) is an economically important fruit crop worldwide. Gummy stem blight (GSB) is one of the most damaging diseases encountered during watermelon cultivation. In the present study, we identified quantitative trait loci (QTLs) associated with GSB resistance in an F2 population derived from a cross between maternal-susceptible line ‘920533’ (C. lanatus) and the paternal-resistant line ‘PI 189225’ (C. amarus). The resistance of 178 F2 plants was assessed by two different evaluation methods, including leaf lesion (LL) and stem blight (SB). To analyze the QTLs associated with GSB resistance, a linkage map was constructed covering a total genetic distance of 1070.2 cM. QTL analysis detected three QTLs associated with GSB resistance on chromosome 8 and 6. Among them, two QTLs, qLL8.1 and qSB8.1 on chromosome 8 identified as major QTLs, explaining 10.5 and 10.0% of the phenotypic variations localizing at same area and sharing the same top markers for both LL and SB traits, respectively. A minor QTL, qSB6.1, explains 9.7% of phenotypic variations detected on chromosome 6 only for the SB trait. High-throughput markers were developed and validated for the selection of resistant QTLs using watermelon accessions, and commercial cultivars. Four potential candidate genes were predicted associated with GSB resistance based on the physical location of flanking markers on chromosome 8. These findings will be helpful for the development of watermelon cultivars resistant to GSB.

Identification of Resistance to Bacterial Leaf Blight in the U.S. Department of Agriculture Collard Collection

Bacterial leaf blight incited by Pseudomonas cannabina pv. alisalensis (Pca) is a devastating disease with incidence reports worldwide and a wide host range capable of infecting all commercially valuable Brassica crops. With no chemical control options available, the most effective form of disease control is host plant resistance, but thus far resistant germplasm has only been identified in Brassica juncea L. (mustard greens). We report the first screening of Brassica oleracea L. var. viridis germplasm, including leafy green collard and collard-like accessions, for resistance to bacterial leaf blight by artificial inoculation of Pca in greenhouse trials. All commercial cultivars tested displayed an intermediate disease response resulting in leaf lesion development that renders the product unmarketable. Two sources of significant resistance were identified in the U.S. Department of Agriculture (USDA) viridis collection, which provides a valuable source of resistance alleles for collard cultivar development and introgression into other B. oleracea crops.

Production of Pycnidia and Conidia by Guignardia bidwellii, the Causal Agent of Grape Black Rot, as Affected by Temperature and Humidity

Black rot, caused by the fungus Guignardia bidwellii, is a polycyclic disease affecting grape leaves and berries. In environmentally controlled experiments and in a 3-year field study, the effects of temperature and relative humidity (RH) were assessed on the following growth parameters of G. bidwellii: (i) formation of pycnidia and cirri in grape leaf lesions, (ii) production and germination of conidia, and (iii) length of the period between lesion appearance and pycnidia production. Pycnidia were produced between 5 and 35°C and at 90 to 100% RH but more pycnidia were produced between 20 and 30°C. No pycnidia were produced at RH < 90%. The first pycnidia were produced in approximately 2 days after lesion appearance at ≥20°C and in 8 days at 5°C; pycnidia continued to be produced on the same lesion for 5 to 16 days after lesion appearance, depending on the temperature. Models were developed to describe the effect of temperature and RH on pycnidia production, accounting for 95 and 97% of variability, respectively. Cirri were extruded only between 15 and 35°C and mainly at 100% RH. Field experiments confirmed that pycnidia are produced for several days on a leaf lesion and that the length of the period between lesion appearance and pycnidia production depends on temperature. Overall, the findings showed that production of conidia requires high humidity; under field conditions, some hours at high humidity, which usually occur at nighttime, rather than constant high humidity may be sufficient.