Journal of Phytopathology最新文献

Drought stress is one of the major abiotic stresses, which seriously affects crop growth and yield. Therefore, the main attention of plant geneticists, physiologists and agronomists is always focused on obtaining drought-resistant varieties. The application of modern technologies, including nanotechnology, in this area, can be an achievement. In the presented work, the effect of some metal-based nanoparticles on the biomorphological parameters of chickpea (Cicer arietinum L.) plants grown in the field under normal irrigation and artificial drought conditions was studied. Outcomes of our experiments indicated that drought causes a remarkable reduction in plant height, leaf area, number of branches, number of pods, pod length and width, the number and mass of grains taken from five chickpea plants, the mass of 100 grains (g), the mass of bundle (g), yield index and the protein and fat contents. Biomorphological parameters were observed to be both decreased and stimulated depending on the type of nanoparticles under irrigated conditions in plants obtained from nanoparticle-coated chickpea seeds. In drought conditions, nanoparticles slightly improved the biomorphological characteristics of plants. Different nanoparticles had different effects on the biochemical indices of grains. Nanoparticle application increased the leaf area, plant height, the number of pods, the length and width of the pods, the mass of seeds of five plants at full maturity stage, the dry mass of 100 seeds, the number of productive nodes, the mass of bundle and the fat content of grain in the drought condition.

Date palm (Phoenix dactylifera) considered a main income and food source for local individuals in producing countries. However, palm offshoot decline and bud rot disease was observed in some palm groves in Khuzestan province as a major date producer in Iran. Thirty-five samples with rot and decline were collected from diseased offshoot from palm groves of six cities of Khuzestan during 2019–2021. Yellowing in outer leaves, black, brown or reddish brown spots were observed on petioles at the early stage, which gradually continues to rot. Seventy-nine were isolated from symptomatic samples among which 38 strains caused rotting in the inoculated site and emission of a foul odour 3 weeks after inoculation on petioles and rachises of young offshoots. Based on the phenotypic characters, pathogenic strains categorised in three groups including Klebsiella oxytoca with 15, Kosakonia radicincitans with 12 and Citrobacter koseri with 11 strains. The phylogenic tree based on analysis of the concatenated partial sequences of two housekeeping genes, gyrB and infB, revealed that six representative strains clustered with the type strains of K. oxytoca and K. radicincitans. Furthermore, two strains were clustered with the type strain of C. koseri based on concatenated partial sequences of gyrB, fusA and pyrG genes. This is the first report of K. oxytoca, K. radicincitans and C. koseri, as agents of date palm offshoot decline and bud rot. The inundation of palm groves due to massive flood during 2019 and 2020 in Khuzestan province may intensify the outbreak of decline and offshoot rot disease.

Garlic plays a crucial role in Vietnam's agricultural economy. However, the presence of post-harvest diseases presents considerable obstacles as it reduces the quality and market value of the crop. The objective of this study was to identify the microbial strains responsible for these diseases through isolation and identification. A comprehensive analysis was conducted to identify six pathogenic species that have a significant impact on the quality of post-harvest garlic in Phan Rang, Ninh Thuan province, Vietnam. This analysis involved morphological observations, microscopic assessments, pathogenicity testing and molecular sequencing. Fusarium solani, F. oxysporum and Lasiodiplodia theobromae were identified as the main causes of dry rot, while Macrophomina phaseolina was associated with charcoal rot. Moreover, Aspergillus niger has been associated with black mould, while Pantoea sp. has been identified as the causative agent of soft rot. Re-infection tests following Koch's postulates conducted on garlic bulbs provided confirmation of the isolates' pathogenicity. These findings have significant implications for enhancing our understanding of garlic diseases after harvest, which can contribute to the development of effective control measures in the future.

Tomato (biological name: Solanum lycopersicum) is an important food crop worldwide. However, due to climatic changes and various diseases, the yield of tomatoes decreased significantly, being detrimental from an economic point of view. Various diseases infect the tomato leaves, such as bacterial and septorial leaf spots, early blight and mosaic virus, to name a few. If uncared, these tomato leaf diseases (TLDs) can spread to other leaves and the fruit. Hence it is vital to detect these diseases as early as possible. Leaf examination is one of the standard techniques to identify and control the spread of diseases. Big Data has made substantial progress, and with the help of computer vision and deep learning techniques to analyse data, we can identify the diseased leaves and help control the disease's spread further. This study used three lightweight midgeneration convolutional neural networks (CNNs) classification network architectures which has the scope to be deployed in IoT devices to help the agricultural community tackle TLDs. It also shows the efficacy of the models with and without geometric data augmentation. The model was trained on a Kaggle data set containing a more significant number of samples to make a robust model aware of broader data distribution and validated on the Plant Village dataset to test its efficacy. The results show that applying transfer learning using ImageNet weights to the MobileNet Architecture using geometrically augmented sample images yields a train and test accuracy of 99.71% and 99.49%, respectively.

The complexity of phytosanitary control for bacterial spot disease is a major challenge in tomato production for industrial processing. The climatic conditions in Goiás and the number of species causing the disease make this management difficult. Thus, this study aimed to evaluate the severity and reaction to bacterial leafspot of sixteen tomato hybrids for industrial processing under the Cerrado conditions in Goiás. The experiment was carried out in randomised blocks, with sixteen tomato hybrids for industrial processing with five replicates, in Hidrolândia-GO. Once the bacterial spot presence in the area was verified, a severity assessment was performed weekly with a diagrammatic scale, totalling five assessments. The area under the disease progress curve (AUDPC) was calculated. At 115 days after transplanting (DAT), harvesting was performed manually, and vegetative development, yield, number of fruits with Xanthomonas spp. symptoms and scalding by the sun. Bacterial spot symptoms were observed in all hybrids evaluated. Above all, the symptoms observed in the fruits and leaves do not coincide with the hybrid showing the greatest disease severity. The hybrid CVR 6116 had the highest yield, lowest AUDPC and the lowest number of fruits with bacterial-spot and sunscald damage symptoms.

Lasiodiplodia theobromae causes several severe diseases in cocoa, including cocoa dieback disease. The dieback caused by L. theobromae on cocoa is considered an emerging disease in Sulawesi. The fungus colonises stem tissues, producing leaf chlorosis, wilting and sudden dieback. To cope with the dieback, selecting and planting existing clones with partial resistance to the pathogen would be a good strategy. The main objective of this study was to evaluate the resistance of locally selected cocoa clones in Sulawesi to L. theobromae infection. Ten cocoa clones were assessed in the laboratory and the greenhouse for disease severity caused by an isolate of L. theobromae. The responses of the clones visible in laboratory and greenhouse tests through stem-wounded inoculation ranged from susceptible to tolerant. The majority of the clones (nine clones) showed a significantly severe dieback and necrotic lesions. In contrast, another clone, designated MCC 02, performed consistently better in suppressing dieback and reducing necrotic lesions. Symptoms on the leaves varied, including chlorotic, yellowing on leaf veins and sudden browning, while in the stem, necrotic lesions and vascular streaking from dark brown to black were consistently visible. The clone MCC 02, with its superior resistance to L. theobromae, holds great potential for use in breeding programs to produce promising progeny clones with good resistance, yield and quality.

White mould is a disease caused by the fungus Sclerotinia sclerotiorum, and it is considered one of the most devastating diseases in soybean crops, causing huge losses in productivity. In this study, we aimed to contribute to the genetic control of this disease by characterising the phenotypic reaction of 67 Brazilian soybean genotypes to S. sclerotiorum under controlled conditions. Plants were artificially inoculated with mycelium discs when they reached the four-node phenological stage. Symptoms of white mould developed in all soybean genotypes. The resistance reaction was characterised by measuring the length of lesions on the main stem at 7 days after inoculation. Of the 67 genotypes evaluated, nine showed greater resistance to S. sclerotiorum, including 17S-01443-L8, 16S-00630-L4, GER_00003, 13S-00001-L2/FPS 2457 RR, BMX Torque I2X, P95Y02 IPRO, 17S-00842-L5, HO Pirapó IPRO and TMG 2359 IPRO. Twenty-three genotypes showed an intermediate response, while 35 genotypes showed greater susceptibility to S. sclerotiorum. Further studies should be conducted under field conditions with the soybean genotypes that showed greater resistance response to S. sclerotiorum under controlled conditions.

Precise prediction of crop yield is crucial for addressing the economic resilience and food security of agricultural countries. Current models for crop yield prediction struggle to fully understand the long-term trends and seasonal variations. Here, the Fractional Rider-Based Water Cycle Algorithm-Based Deep Long Short-Term Memory (FRWCA-DLSTM) is devised for crop production forecasting and addresses these issues. Primarily, the simulation of the IoT is performed. Then, the selection of Cluster Head (CH) and routing are done with the Rider-Based Water Cycle Optimisation (RWCO). Then, the crop production data are accumulated at the Base Station (BS), where Spark architecture is used for crop prediction. Here, the data partitioning is done using Deep Fuzzy Clustering (DFC). Next, the technical indicators are extracted. Then, the ensemble-based Feature selection is accomplished. Here, the ranking techniques are combined by a fusion function. The weight parameters are tuned by Hunter-Sparrow Search Optimisation (HSSO). Finally, the crop yield prediction is performed by DLSTM, which is trained using FRWCA. The FRWCA is developed by merging Fractional Calculus (FC) with RWCO. The performance of FRWCA-DLSTM shows the minimum mean absolute percentage error (MAPE), mean square error (MSE) and root mean square error (RMSE) of 0.103, 0.081 and 0.284, respectively.

Bean anthracnose, caused by Colletothricum lindemuthianum, poses a significant threat to common bean production in Ethiopia. The objective of this study was to determine the pathogenic variability and race structure and distribution of bean anthracnose in four selected zones (Metekel, Sidama, Wolaita and Halaba) of Ethiopia. Field surveys were conducted at 5–7 km intervals, focusing on diseased plant parts of common bean. The severity, incidence and prevalence of bean anthracnose were assessed and determined, and a total of 74 bean anthracnose isolates were obtained for further analysis. The isolates were tested on the 12 standard differential cultivars. The results revealed a wide range of pathogenic variability, with severity ranging from 0% to 24.1%, incidence from 0% to 87% and prevalence from 0% to 100%. The 74 isolates were classified into 32 distinct pathogenic races with 20 of them being newly identified races specific to Ethiopia. Dibate district had the highest number of races (12), whereas Sodo_Zuria and Borecha district had only one. Race 128 was widely distributed, and race 3770 was the most virulent, infecting 8 of the 12 differential cultivars. Around 14% of the isolates were virulent to one differential cultivar, whereas 4.1% to eight cultivars. These findings have important implications for developing resistant cultivars by deploying resistance genes into an improved cultivar and ensuring sustainable common bean production in Ethiopia and other related countries.