Healthy Plants Research Articles

The efficacy of chemical inducers and fungicides in controlling tomato root rot disease caused by Rhizoctoniasolani

Chitosan is an environmentally friendly natural substance that is used in crop disease management as an alternative to chemical pesticides. A significant issue restricting output in Egypt is root rot, which is a disease, caused by Rhizoctonia solani. Therefore, a greenhouse experiment was conducted to assess the effects of R. solani on 60-day-old tomato plants under fungal infection and to determine the antifungal activity of chitosan and Rizolax T fungicide against the pathogenic fungus. The findings demonstrated that 4 g/L of chitosan seed application completely obstructed the radial mycelial growth of R. solani and decreased the disease severity. Pathogenic infection significantly decreased morphological characteristics and total chlorophyll but significantly increased carotenoid, total thiol, non-protein thiol, protein thiol, antioxidant enzymes, oxidative stress, total phenolic, total flavonoid, and isoflavone compared to healthy plants. Tomato plants treated with chitosan exhibited lower rates of oxidative stress, but higher levels of all previously mentioned parameters compared to untreated infected plants. The number and molecular mass of protein banding patterns varied in all treated tomato plants as compared to the healthy control. There are 42 bands in the treatments, and their polymorphism rate is 69.55%. Moreover, the number and density of α- and β-esterase, and peroxidase isozymes in treated tomato plants exhibited varied responses. Moreover, in treated and control plants, chitosan treatment raised the expression levels of phenylalanine ammonia-lyase, pathogenesis-related protein-1, β-1,3-glucanases and chitinase. In conclusions, chitosan reduces R. solani infection by controlling the biochemical and molecular mechanisms in tomato plants during infection.

First Report of Alternaria alternata Causing Leaf blight on Epimedium sagittatum (Sieb. et Zucc.) Maxim. in China.

Epimedium sagittatum (Sieb.et Zucc.) Maxim. is an important material of traditional Chinese medicine because of the rich content of flavonoids that are used to treat osteoporosis, liver cancer, and sexual dysfunction (Liu et al. 2013). A leaf blight was observed on E. sagittatum in Zhumadian City, China (32°58'12" N, 114°37'48" E, continental monsoon climate) in June 2021. Survey indicated that about 18% of the plants were infected in a 266-ha commercial planting area. The initial symptoms were white patches with tan borders, irregular in outline, with small black particles visible on the center of the lesions. In a week or so, patches extended throughout the leaf, and then leaves withered. Thirty leaves with symptoms collected from five different sites were cut into 5×5 mm pieces, and then surface-sterilized with 75% ethanol for 15 s followed by rinsing with double distilled water (ddH2O) three times. The pieces were then disinfested with 0.1% HgCl2 solution for 30 s, and rinsed with ddH2O, then placed onto potato-dextrose agar medium (PDA) and incubated in the dark for 3 d at 28°C. Eight fungal isolates were purified; of these, only the isolate HY2-1 infected the host plant and was selected for further morphological characterization. The colonies of HY2-1 were olive green with loose aerial hyphae on PDA. Conidiophores were single or branched, producing brown conidia in short chains. Conidia were obclavate, obpyriform, or ellipsoidal, 15.9-47.3 µm × 7.6-16.6 µm (n=50) and pale brown or dark brown with a short cylindrical beak at the tip that contained 1-5 transverse septa and 0-4 longitudinal septa. Morphological characteristics of the isolate were identical with those of Alternaria species (Huang et al. 2022). For molecular identification, the internal transcribed spacers (ITS), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Weir et al. 2012), major allergen Alt a 1(Alt a 1) and translation elongation factor 1-α gene (TEF) (Lawrence et al. 2013) were amplified and sequenced using the primers ITS4/5, GDF/GDR, Alt-F/R, and EF1-728F/986R, respectively. The results of the sequencing were uploaded to GenBank as ITS (OR418487), GAPDH (OR419792), Alt a 1 (OR419794), and TEF (OR419796), respectively. Phylogenetic analyses were performed by concatenating all the sequenced loci using the Bayesian method in Phylosuite (Zhang et al.2020). The phylogenetic tree indicated that the isolate belongs to the A. alternata clade with a bootstrap value of 75%. The pathogen was identified as A. alternata based on the morphological and molecular results. To satisfy Koch's postulates, a conidial suspension (106 conidia/mL) of the HY2-1 was prepared with ddH2O to infect the healthy plants. Ninety healthy leaves on 30 plants in pots were punctured using a sterilized needle, and then inoculated by spraying the conidial suspension on the wounded leaves in a greenhouse at 25°C and 80% relative humidity. The control plants were sprayed with ddH2O. The plants showed similar symptoms to the original infected plant 15 d after inoculation. The controls showed no symptoms. A pure culture of A. alternata was isolated and identified again as previously described. Leaf blight caused by A. alternata has been reported on Taro (Liu et al. 2020), Toona ciliata (Wang et al. 2023), etc. To our knowledge, this is the first report of E. sagittatum leaf blight caused by A. alternata in China. The results will help to develop effective control strategies for leaf blight on E. sagittatum.

Computational neural network for processing light-reflective spectra of plants and remote phytosanitary monitoring of potatoes

The article is devoted to studying the possibility of using the WaveLetNN artificial neural network to analyze the results of remote phytosanitary monitoring of early detection of plants in potato plantings affected by late blight. Various methods for analyzing the spectral characteristics of plant reflection are considered, including the classification method. To detect plants infected with late blight, the WaveLetNN neural network analyzes the light reflective characteristics of potato plants obtained as a result of research (in the range of 300–1100 nm) and calculates the cognitive significance index (CSI = 0...10), which characterizes the intensity of biochemical processes inside plants aimed at countering phytopathogenic microflora. It was found that a significant increase in the CSI index signals infection of plants by phytopathogenic microflora and activation of protective biochemical processes on the part of plants. To reliably indicate infected plants, the WaveLetNN neural network underwent test training on a large number of light reflectance spectra of uninfected plants and plants artificially infected with late blight. The spectral reflectance characteristics of infected and uninfected plants were measured during 3, 4, 7 and 8 days after infection. Processing the obtained spectra using the WaveLetNN neural network made it possible to identify significant differences between the second- and third-order spectral characteristics of uninfected and late blight infected plants on the third day after infection. Moreover, for infected plants the CSI index values were 6.1...6.7, and CSI for healthy plants – 1.9...2.5. The Wave-LetNN neural network eliminates the influence on the light reflectance spectra of the spatial arrangement of plant leaves, unevenness of the soil surface and shading of individual sections of the field, normalizing the spectra to the total intensity of light reflected from the leaves. Thus, the WaveLetNN neural network can be used as the software core of online systems for remote phytosanitary monitoring of potato plants.

Physiological and developmental disturbances caused by Botryosphaeria dieback in the annual stems of grapevine.

Botryosphaeria dieback is a grapevine trunk disease caused by fungi of the Botryosphaeriaceae family, which attacks more specifically the woody tissues. The infection leads to different symptoms including a severe form with a leaf drop as well as premature plant death. Botryosphaeria dieback causes major economic losses, since no effective treatment is yet available. A better understanding is necessary to find solutions to fight this disease. In this study, our objective was to characterize the "leaf drop" form by (1) looking for the presence of pathogens in the basal internodes of stems, (2) quantifying blocked vessels by tylosis and/or gummosis, and (3) describing the impact of the disease on vine physiology (gene expression and metabolome) and development (establishment and functioning of the cambium and phellogen) at the level of xylem and phloem of basal stem internodes. Our study has shown that Botryosphaeriaceae were present in both phloem and xylem of the basal internodes of the annual stem, with xylem vessels obturated. We have also clearly demonstrated that gene expression and metabolite profiles were strongly modified in both xylem and phloem of diseased plants. Differences in stems between healthy (control, C) and diseased (D) plants were low at flowering (vines not yet symptomatic), higher at the onset of symptom expression and still present, although less marked, at full disease expression. qRT-PCR analysis showed in both phloem and xylem an overexpression of genes involved in plant defense, and a repression of genes related to meristematic activity (i.e. vascular cambium and phellogen). Metabolomic analysis showed specific fingerprints in stems of healthy and diseased plants from the onset of symptom expression, with an increase of the level of phytoalexins and mannitol, and a decrease of 1-kestose one. At the structural level, many alterations were observed in internodes, even before the onset of symptoms: a classical obstruction of xylem vessels and, for the first time, a disorganization of the secondary phloem with an obstruction of the sieve plates by callose. The disease modifies the development of both secondary phloem (liber) and phellogen. Altogether, this study combining different approaches allowed to highlight deep vine dysfunction in the internodes at the base of stems, that may explain vine decline due to Botryosphaeria dieback.

First Reports of Cladosporium tenuissimum Causing Leaf spots on Hydrangea macrophylla in Anhui province in China.

Forever Summer Hydrangea (Hydrangea macrophylla) is a common flowering plant in the Yangtze River Valley area of China, and it is widely cultivated globally (Chen et al. 2015). In July 2023, H. macrophylla leaves exhibiting visible diseased lesions were reported in a nursery in Wuhu, Anhui Province, China. The incidence reached 40% in a 0.2 ha area. The primary disease symptom was multiple irregular necrotic spots (0.5 to 1 mm in diameter) appearing on the leaves. These spots on the leaves were faded yellow around the perimeter and grayish brown in the center.). 15 leaf samples were sterilized with 75% alcohol and rinsed three times in sterile distilled water, then transferred to antibiotic-added potato dextrose agar (PDA) for incubation at 27°C. The colonies were fluffy, flocculent, or hairy, dark green, gray-green to gray-brown in color, and spreading or protruding punctate with a colorless halo on PDA. The conidiophores were brown to dark brown, smooth or rough surface, mostly unbranched, clearly differentiated, erect or curved. The conidia displayed a light brown to brown hue, lemon shape, fusiform, elongated ellipsoid or others with obvious spore markings and spore umbilicus. Genomic DNA was extracted from fungal colonies on infected leaves of three collections separately (Braun et al. 2003) and the internal transcribed spacer regions (ITS), actin (ACT) genes and partial translation elongation factor-l-alpha (EF) were amplified and sequenced using the primers ITS1/4 (Yin et al. 2012), ACT-512F/ACT-783R and EF 1-728F/986R (Carbone and Kohn 1999), respectively. DNA sequences of isolates were identical and deposited in GenBank (accession no. OR362754 for ITS, OR611929 for ACT and PP209106 for EF). The consensus sequences from ITS, EF and ACT showed 100%, 98.98% and 100% identical to Cladosporium strains (accession no. OQ186140.1, MT154169.1 and OL322092.1), respectively. To confirm the pathogenicity of the isolates, hydrangeas were planted in 15-cm pots containing commercial potting mix (one plant/pot). Three healthy plants were inoculated at the five to eight leaf stage by spraying 50 μL of the isolate conidial suspension (4 × 106 spores/mL) on healthy leaves. Three plants treated with sterile distilled water were used as controls. After inoculation, all plants were placed in a humidity chamber (>95% relative humidity, 26°C) for 48 h and then transferred to a greenhouse at 22/27°C. All inoculated leaves exhibited symptoms similar to those observed in the nursery 10 days after inoculation, while no symptoms were observed for control leaves. The fungus was re-isolated and confirmed to be C. tenuissimum. Based on the above morphological characterization and molecular identification, the causal agent for this leaf spot disease was identified as C. tenuissimum. Although C. tenuissimum has been reported to cause disease on H. paniculata in northern China (Li et al.2021), this is the first time that C. tenuissimum has been found on H. macrophylla in southern China. This new disease of H. macrophylla caused by C. tenuissimum is a threat to urban greening and is worth further investigation.

First Report of Root and Collar Rot caused by Phytophthora nicotianae on ⨯Graptoveria 'Silver Star' in Korea.

⨯Graptoveria 'Silver Star' (a cross between Graptopetalum filiferum and Echeveria agavoides) from the Crassulaceae family, are an evergreen succulent with lotus constellation-shaped flowers, making it consumer favorite ornamental plant in Korea. In 2019, Korea's ornamental production was estimated at KRW 517.4 billion (EUR 382 million), from 4,244 ha of farming area according to the Ministry of Agriculture, Food and Rural Affairs of Korea. In July 2023, ⨯Graptoveria 'Silver Star' plants with chlorotic leaves, root and collar rot were observed in a greenhouse in Yongin (37°14'27.9"N, 127°10'39.19"E), Korea. To isolate the causal agent, small pieces (1 mm2) of symptomatic tissues were surface-sterilized using 1% NaOCl for 1 min, then put onto a water agar (WA) plate and incubated in the dark at 25℃ for five days. Two isolates (FD00202, FD00203) were obtained from diseased leaves, stem and roots by isolating single sporangium. To investigate the morphological characteristics of the isolates, the mycelium from potato dextrose agar (PDA) were transferred to V8 agar (V8A) followed by incubation at 25°C in the dark for 7 days. The isolates produced dense cottony mycelium, with slightly petaloid and light rossette pattern, with coralloid edges measuring 70 to 83 mm diameter. Sporangium were spheroid (30.0-48.0 µm long, 25.0-35.0 µm wide) with globose chlamydospores (17.0-50.0 µm long, 18.0-38.0 µm wide). Oogonia were not observed. Morphological and cultural characteristics of these isolates were phenotypically similar to that of Phytophthora nicotianae (Faedda et al. 2013; Abad et al. 2023). For molecular identification, genomic DNA was extracted from 5 days old cultures using the Maxwell® RSC PureFood GMO and Authentication Kit (Promega). Two gene regions, the rDNA-ITS, COX I were amplified and sequenced using primers ITS1/ITS4 and FM83/FM84, respectively (White et al. 1990; Martin and Tooley 2003). The resulting sequences were deposited in GenBank with accession no. LC783858 to LC783861. A BLASTn search of the DNA sequences from ITS, COX I showed 99.81 and 98.94% identity to P. nicotianae isolate IMI 398853, respectively. Maximum likelihood phylogenetic analyses were performed for the combined data set with ITS, COX I using MEGA7 under the Tamura-Nei model (Kumar et al. 2016). The isolates formed a monophyletic group with P. nicotianae isolate IMI 398853, CPHST BL162, and CPHST BL 44. Based on morphological characteristics and molecular analysis, the isolates were identified as P. nicotianae. T confirm their pathogenicity, inoculum was prepared in accordance with Ann (2000). Artificially wounded healthy plant roots were dipped in zoospore suspension (3.0 × 106 zoospore/ml) for 24 hours, with mock-treated plants (control) dipped in sterile distilled water (Ann. 2000). Thereafter, the plants were transplanted into new medium and kept under high humidity. Symptoms were observed after 10 days of incubation. The plants inoculated with P. nicotianae showed similar symptoms of chlorotic leaves with root and collar rot, while control remained symptomless. The pathogen was re-isolated from all inoculated plants and confirmed as P. nicotianae by morphological and molecular analysis. but not from controls, fulfilling Koch's postulates. Phytophthora nicotianae was previously report on Echeveria derenbergii and Kalanchoe blossfeldiana causing brown spot on stems and roots in California and Korea, respectively (French 1989; Oh and Son 2008). To best of our knowledge, this is the first report of P. nicotianae causing root and collar rot on ⨯Graptoveria 'Silver Star' plants in the Korea.

First Report of Rust Disease on Alcea rosea caused by Puccinia modiolae in China.

Alcea rosea, belonging to the Alcea genus in the Malvaceae family, originated from China, but it is now grown worldwide. A. rosea has been widely used in traditional Chinese medicine to alleviate constipation, pain, swelling, and sores. In February 2023, typical symptoms of fungal infection were observed on A. rosea at Guizhou Normal University in Guiyang, Guizhou Province, China. The disease incidence was over 90% (n = 100) for the surveyed A. rosea plants, and the disease severity range from 30% to 90%. The initial symptoms of A. rosea rust were the appearance of chlorotic spots on the leaves. Subsequently, numerous reddish to dark-brown erumpent pustules (telia) were observed. Gradually, the entire plant was covered by rust and the center of each lesion turned brown, necrotic, and ruptured over times, eventually causing defoliation. Voucher specimens of infected A. rosea leaves as representative samples have been deposited at Guizhou Normal University (GNU2023LS008). Telia are round in shape, mostly aggregated in mass, with a diameter of 0.28-0.78 mm (0.46 mm, n = 20). They range in color from reddish-brown to dark brown, and are mainly hypophyllous but occasionally formed on the adaxial leaf surface. The teliospores are fusoid with dimensions of 31.3-93.8 × 10.9-21.3 μm (57.5 × 15.1 µm average, n = 50), hyaline or yellowish to light-brown in color, mostly two-celled, with a smooth wall (1.5-3.0 µm) and a thickened apex (3.0-9.0 µm). However, teliospores which are one-, three-, or four-celled with a notch at the apex, are rarely observed. The morphological characteristics of host symptoms and teliospores were similar to those of Puccinia modiolae (Aime and Abbasi 2018; Albu et al. 2019). For phylogenetic analysis, genomic DNA was extracted from the teliospores of infected leaves. To confirm the species-level identification, PCR was performed on the extracted DNA to amplify the ribosomal DNA internal transcribed spacer (ITS) and large subunit (LSU) regions using primer pairs ITS1/ITS4 (Schoch et al. 2012) and NL1/NL4 (Ziemiecki et al. 1990), respectively. The resulting ITS DNA sequence (GenBank accession no. OR607960) showed 100% identity with P. modiolae sequences (OP369291.1), when the query coverage was 100%. The LSU DNA sequence obtained (OR607961.2) shared 99.85% similarity with P. modiolae (MK458702.1). A phylogenetic tree was constructed using MEGA7.0 and the maximum likelihood method based on the ITS and LSU sequences. The fungal isolates collected in this study and several reference sequences of P. modiolae were grouped within a clade that included the isolates reported on A. rosea in Korea (Ryu et al. 2023), with 100% bootstrap support. Pathogenicity testing was conducted by gently pressing spore powder of naturally diseased leaves onto young leaves of three healthy A. rosea plants, with three noninoculated healthy plants serving as controls. The inoculated and noninoculated plants were kept in a growth chamber at the 26°C with a 12 hour light/dark cycle and 80% humidity. After 2 weeks, all inoculated A. rosea plants showed characteristic disease symptoms of rust infection and telia of P. modiolae, while control plants remained symptomless. The pathogen was identical to that observed on the original diseased leaves. The study results indicate that the causal fungus responsible for the disease is P. modiolae, which has been previously reported on Malvaceae plants (Farr and Rossman 2022). To the best of our knowledge, this is the first report of P. modiolae on A. rosea in China. This study will contribute to an increased understanding of the host range of Puccinia modiolae.

Detection and identification of plant leaf diseases using YOLOv4.

Detecting plant leaf diseases accurately and promptly is essential for reducing economic consequences and maximizing crop yield. However, farmers' dependence on conventional manual techniques presents a difficulty in accurately pinpointing particular diseases. This research investigates the utilization of the YOLOv4 algorithm for detecting and identifying plant leaf diseases. This study uses the comprehensive Plant Village Dataset, which includes over fifty thousand photos of healthy and diseased plant leaves from fourteen different species, to develop advanced disease prediction systems in agriculture. Data augmentation techniques including histogram equalization and horizontal flip were used to improve the dataset and strengthen the model's resilience. A comprehensive assessment of the YOLOv4 algorithm was conducted, which involved comparing its performance with established target identification methods including Densenet, Alexanet, and neural networks. When YOLOv4 was used on the Plant Village dataset, it achieved an impressive accuracy of 99.99%. The evaluation criteria, including accuracy, precision, recall, and f1-score, consistently showed high performance with a value of 0.99, confirming the effectiveness of the proposed methodology. This study's results demonstrate substantial advancements in plant disease detection and underscore the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction. These developments have significant significance for everyone involved in agriculture, researchers, and farmers, providing improved capacities for disease control and crop protection.

Impact of Urdbean Leaf Crinkle Disease on Growth and Yield of Urdbean (Vigna mungo L. Hepper) Genotypes

Blackgram (Vigna mungo (L.) Hepper), also known as urdbean, stands as a significant pulse crop cultivated globally. Among the viral diseases documented in blackgram, Urdbean Leaf Crinkle Disease (ULCD) emerges as particularly devastating, leading to substantial economic losses contingent upon factors such as seasonal variations and cultivated varieties. In this study, six cultivars of urdbean (M114, M218, M338, T9, N7, and M1008) were subjected to scrutiny regarding their growth and yield-related attributes under ULCD infection. Employing a randomized block design (RBD), data pertaining to each yield-contributing factor were meticulously recorded from both healthy and infected urdbean plants and analysed using two way ANOVA. A pronounced reduction across all yield parameters was noted in virus-infected plants, manifesting in diminished plant height by 2.02-37.01%, pod size by 1.10-51.30%, seed/pod ratio by 4.41-61.46%, 100-seed weight by 2.73%-51.37%, pod number per plant by 3.72-63.86%, and number of branches per plant ranging from 0.84 to 48.43%. Upon comparative analysis of all urdbean genotypes, it can be deduced that genotypes M114, M218, and M338 exhibit lesser susceptibility to urdbean leaf crinkle disease, thereby positioning them as promising candidates for breeding programs and cultivation endeavors aimed at achieving higher yields.

The association between score of plant-based diet and non-alcoholic fatty liver disease in adults

Plant-based diet (PDI) as resource of antioxidants and anti-inflammatory phytochemicals, that was considered to protect against onset and development of Non-alcoholic fatty liver disease (NAFLD). To investigate the association between plant-based diet and NAFLD in adults. The present case control study was conducted on 240 individuals (120 with NAFLD and 120 control) aged 20-69 years. Provided recommendations by the American College of Gastroenterology and the American Gastroenterological Association were used for NAFLD diagnosis. Dietary intake was assessed using 178-food item food frequency questionnaire (FFQ). Also, plant-based diet score was evaluated based on 18 food groups classified into animal foods, healthy and unhealthy plant foods. A multiple logistic regression model was used to examine the relationship between fatty liver disease and tertiles of PDI. The results of this study showed that we did not observe any association between tertiles of PDI and NAFLD in crude model (OR: 1.29, 95%CI:0.66-2.52, P:0.44) and after adjustment for confounders including age, energy intake, physical activity, body mass index (OR:0.76, 95%CI: 0.31-1.86, P:0.52). Also, there were not any association of tertiles of healthy PDI (hPDI) (OR:1.14, 95%CI: 0.50-2.60, P:0.74) and unhealthy PDI (uhPDI) (OR:0.89, 95%CI:0.36-2.18, P: 0.79) with NAFLD after full adjustment for potential confounders. There was not any association of PDI, hPDI, and uPDI with NAFLD in adults. More research needs to examine whether this specific diet may impact and improve NAFLD.

Identification of volatile organic compound of oil palm plants infected with Ganoderma sp.

The success of controlling basal stem rot (BSR) disease caused by Ganoderma sp. is mostly determined by the early detection of the fungi. Hence, when the signs of infection begin to appear, plants are generally difficult to rescue since the pathogen infection has spread widely to all parts of the plant. Therefore, early detection through the application of biosensors for Ganoderma sp. infection is absolutely necessary. This study aim was to identify biomarker compounds of Ganoderma sp. infection in oil palm plants with GC-MS for volatile compounds, and LC-MS for non-volatile compounds. The results showed that Ganoderma sp. mycelium produced pyrimidinamine compounds. Meanwhile in early infected nursery plants, were found benzo[h]quinoline, hexaoxa-7,9,11-trisilaheptad, tris-(trimethyl-silyl ester and methyl-tris(trimethyl-siloxy)-silane). Whereas in Bekri, Rejosari, and Adolina plantation, the compound methyl-tris(trimethyl-siloxy)-silane was also found in healthy mature plants. GC-MS test results showed that for early, moderate and severe plants produced several benzene derivative compounds such as ethylbenzene, xylene, and benzaldehyde. These compounds were assumed to be resulted from the breakdown of the lignin structure which build plant cell walls, and have potency to be used as marker compounds for early infected Ganoderma sp. detection. The result of the produced gas quantification concluded that in the produced oil palm plants released less CO2 compared to healthy plants. On the other hand, the NH3 produced was higher than the healthy plants. Meanwhile, two non-volatile compounds were found that they were only produced by infected trunk and root tissue, namely pseudobrucine and picrasidine.

Introduction and establishment of Euwallacea fornicatus (Coleoptera: Curculionidae: Scolytinae) in Brazil.

Euwallacea fornicatus is an invasive tree pest able to infest healthy plants and cause damage to many host plants. This beetle has become established in several countries where it was introduced. It has now become established in Brazil, and while the original introduction site remains uncertain, there is a possibility of multiple introductions. We report the first evidence for the establishment of E. fornicatus with molecular confirmation, as well as its distribution, and host plants in Brazil. Euwallacea fornicatus has spread to main commercial avocado groves, other monocultures, and native vegetation in the country, and its pest status puts it as a threat, mainly to Brazilian avocado producers.

Utilizing Machine Learning Techniques forthe Detection of Plant Leaf Diseases

Identification of plant diseases is crucial for preserving crops and ensuring food security. Analysis of detectable chemicals in plants is essential to understand transmission mechanisms and develop effective strategies for disease control measures to conserve agricultural products and prevent losses. However, manual monitoring of plant health is labor-intensive and time-consuming, requiring specialized skills and knowledge. To overcome these challenges, random forest systems are emerging as a powerful tool for disease detection and classification in plants. The process involves several steps, including image acquisition, preprocessing, and segmentation, followed by feature extraction, model training, and testing. Leveraging machine learning techniques, the random forest algorithm enables accurate classification of healthy and diseased leaves based on selected features. Image classification techniques are utilized to extract color information, while global features such as size and texture are captured through annotation. The dataset used for model training and testing comprises diverse samples, encompassing healthy and diseased plants. The random forest model is trained on 70% of the data to ensure robust learning, while the remaining 30% is reserved for testing, facilitating the exploration of model performance and overall feasibility

Transmission of Xylella fastidiosa subspecies multiplex from naturally infected to healthy Rhamnus alaternus by Philaenus spumarius and Neophilaenus campestris

In Europe, the recently reported plant pathogen Xylella fastidiosa subsp. multiplex affects several wild, ornamental, and cultivated trees causing scorch diseases. In 2018, the sequence type 87 was reported in Tuscany on Mediterranean shrubs and trees. Although spittlebugs (Hemiptera: Aphrophoridae) were already identified as main vectors of this bacterium in Europe, their role in the transmission of this subspecies has not been ascertained yet. In this study the ability of Philaenus spumarius and Neophilaenus campestris to acquire and transmit Xylella fastidiosa subsp. multiplex sequence type 87 from and to Rhamnus alaternus was evaluated in two-year semi-field experiments. To acquire the bacterium, insects were confined on wild, naturally infected R. alaternus shrubs for 120 h. Then, they were transferred to healthy plants and maintained in cages for 96 h. To follow the infection, plant samples were collected every two months for three times. Tested plants were destroyed at the end of experiments and roots, twigs and leaves were analysed. Philaenus spumarius showed a significantly higher survival rate than N. campestris. The infection status of both insects and plants was assessed through molecular analysis. P. spumarius and N. campestris were able to infect healthy plants although the acquisition rate and the estimated probability of transmission appeared to be low. These findings provide new accounts on the role of two polyphagous insect vectors in spreading a quarantine organism, which is lethal to a huge number of plant species. However, further studies are needed to disclose more specific interactions within this complex pathosystem.

Complete Genome Resource of a Fungal Antagonistic Strain: Pseudomonas tohonis

KRS022 was isolated from the rhizosphere soil of a healthy plant in a pathogen-infested cotton field in Xinjiang, China. Previous studies have demonstrated that KRS022 possesses the ability to trigger plant immune responses, inhibit the growth of various pathogenic fungi, including Verticillium dahliae, and promote plant growth. The antagonistic properties of KRS022 primarily stem from its bioactive metabolites. In this study, the high-quality genome of KRS022 was obtained. The KRS022 genome consists of a chromosome of 6,369,026 bp with 67.09% GC content and possesses 5,830 open reading frames, of which 5,186 genes were annotated, and 119 were ncRNA genes. Additionally, 11 gene clusters of secondary metabolites biosynthesis were predicted, involved in polyketide synthase, arylpolyene, NAGGN, beta-lactone, ranthipeptide, PiPP-like, redox cofactor, and non-ribosomal peptide-synthetase clusters. It could be useful to explore the biocontrol potential of P. tohonis, implying promising application of KRS022 to biological control. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

MACHINE LEARNING INTEGRATION, REMOTE SENSING DATA PREPROCESSING TECHNIQUES TO MAP PESTS COTTON CROPS

Cotton has a considerable economic impact on agribusiness. Strategies to reduce production loss due, for example, to pest attacks are increasingly required. Spodoptera frugiperda, known as fall armyworm, causes irreversible damage to cotton. In this context, a current approach is the use of hyperspectral measurements obtained by remote sensors and processed by machine learning algorithms. However, such measures generate data redundancy, making it difficult to extract information. An alternative is to apply pre-processing techniques, but little is known about the impact these generate on the learning ability of algorithms. This study evaluates the performance of machine learning algorithms in identifying cotton plants attacked by pests using pre-processed and raw hyperspectral measurements. Data are collected by EMBRAPA, and consist of hyperspectral measurements, in the range of 350-2500 nm, referring to eight days of collections in healthy cotton plants and attacked by S. frugiperda. Pre-processing techniques to try are baseline removal, smoothing, first and second order derivatives. A group of machine learning algorithms, such as Random Forest, Support Vector Machine, Extra Tree, was used to model pre-processed and non-pre-processed hyperspectral measurements. According to the proposed metric, the F-Score and the Extra Trees (ExT) algorithm performed better (0.77). So it overlapped the other results with the preprocessed dataset. In addition to obtaining the most important lengths for the algorithm to have its best performance. Concluding that machine learning with spectroscopy can help the field in a promising way. Studies in other crops and with other factors applied to the plant are recommended.

Plant disease recognition using residual convolutional enlightened Swin transformer networks

Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer’s life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands. But, precise prediction of plant diseases is a complex job due to the presence of noise, changes in the intensities, similar resemblance between healthy and diseased plants and finally dimension of plant leaves. To tackle this problem, high-accurate and intelligently tuned deep learning algorithms are mandatorily needed. In this research article, novel ensemble of Swin transformers and residual convolutional networks are proposed. Swin transformers (ST) are hierarchical structures with linearly scalable computing complexity that offer performance and flexibility at various scales. In order to extract the best deep key-point features, the Swin transformers and residual networks has been combined, followed by Feed forward networks for better prediction. Extended experimentation is conducted using Plant Village Kaggle datasets, and performance metrics, including accuracy, precision, recall, specificity, and F1-rating, are evaluated and analysed. Existing structure along with FCN-8s, CED-Net, SegNet, DeepLabv3, Dense nets, and Central nets are used to demonstrate the superiority of the suggested version. The experimental results show that in terms of accuracy, precision, recall, and F1-rating, the introduced version shown better performances than the other state-of-art hybrid learning models.

High-throughput sequencing-based analysis of the composition and diversity of the endophyte community in roots of Stellera chamaejasme

Stellera chamaejasme (S. chamaejasme) is an important medicinal plant with heat-clearing, detoxifying, swelling and anti-inflammatory effects. At the same time, it is also one of the iconic plants of natural grassland degradation in northwest China, playing a key role in the invasion process. Plant endophytes live in healthy plant tissues and can synthesize substances needed for plant growth, induce disease resistance in host plants, and enhance plant resistance to environmental stress. Therefore, studying the root endophytes of S. chamaejasme is of great significance for mining beneficial microbial resources and biological prevention and control of S. chamaejasme. This study used Illumina MiSeq high-throughput sequencing technology to analyze the composition and diversity of endophytes in the roots of S. chamaejasme in different alpine grasslands (BGC, NMC and XGYZ) in Tibet. Research results show that the main phylum of endophytic fungi in the roots of S. chamaejasme in different regions is Ascomycota, and the main phyla of endophytic bacteria are Actinobacteria, Proteobacteria and Firmicutes (Bacteroidota). Overall, the endophyte diversity of the NMC samples was significantly higher than that of the other two sample sites. Principal coordinate analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA) results showed significant differences in the composition of endophytic bacterial and fungal communities among BGC, NMC and XGYZ samples. Co-occurrence network analysis of endophytes showed that there were positive correlations between fungi and some negative correlations between bacteria, and the co-occurrence network of bacteria was more complex than that of fungi. In short, this study provides a vital reference for further exploring and utilizing the endophyte resources of S. chamaejasme and an in-depth understanding of the ecological functions of S. chamaejasme endophytes.

First Report of Powdery Mildew Caused by Podosphaera xanthii on Acalypha indica in China.

Acalypha indica L. is an annual erect herb of the Euphorbiaceae family. This plant is found widely in the tropics and parts of Africa and Asia (Chakraborty et al. 2023). In China, A. indica is a vegetable and also used as a folk medicine due to its antipyretic and hemostatic, antibacterial and anti-inflammatory properties. In February 2022 and 2023, powdery mildew symptoms were observed on 70% of A. indica plants on the Hainan Medical University campus (19° 58' 53″ N; 110° 19' 47″ E) in Haikou, Hainan Province, China. Powdery mildew colonies covered the leaf surfaces and stems of affected plants, causing discoloration and defoliation. Mycelia were superficial and hyphal appressoria were nipple-shaped. Conidiophores (n =30) were unbranched, cylindrical, 66 to 150 × 10 to 15 µm, and produced three to five immature conidia in chains with a crenate outline. Foot cells (n =30) were cylindrical, straight or sometimes curved at the base, and 31 to 59 µm long. Conidia (n =100) were ellipsoid-ovoid to doliiform, 20 to 33 ×12 to 20 µm (length/width ratio = 1.3 to 2.4), with well-developed fibrosin bodies, and produced germ tubes from the lateral position. Based on these morphological characteristics, the pathogen was provisionally identified as Podosphaera xanthii (Braun and Cook 2012). The teleomorph was not observed. A specimen was deposited in the Hainan Medical University Plant Pathology Herbarium as HMAI-23. To confirm the genus identification and ascertain a putative species, genomic DNA was extracted from mycelium, conidiophores, and conidia using a fungal DNA kit (Omega Bio-Tek, USA). The rDNA internal transcribed spacer (ITS) region was amplified with primers ITS1/ITS4 (White et al. 1990) and sequenced directly. The resulting 575-bp sequence was deposited in GenBank (accession no. OR775733). A BLASTn search in GenBank of this sequence showed 99% similarity with the ITS sequences of P. xanthii on plants of Fabaceae, Malvaceae and Cucurbitaceae family from China (MH143485, MT242593, MK439611 and MH143483), Thailand (LC270779 and LC270778), Korea (MG754404), Vietnam (KM260704), and Puerto Rico (OP882310). Additionally, the 28S rDNA region was amplified using the primer pairs NL1 and NL4 (O´Donnell 1993; accession no. OR784547). This region shared 99% similarity with P. xanthii isolates (LC371333, LC270780, AB936277, and OP765401) as well. To confirm pathogenicity, five healthy potted plants of A. indica were inoculated by gently pressing a powdery mildew-infected leaf onto 15 young leaves. Five non-inoculated plants served as controls. All plants were maintained in a greenhouse at 24 to 30°C, 70% relative humidity, with a 16-h photoperiod. After 7 days, inoculated leaves showed powdery mildew symptoms whereas no symptoms were observed on control plants. The fungal colonies observed on inoculated plants were morphologically identical to those found on the originally infected leaves collected from Hainan Province. Based on the morphological characteristics and molecular identification, the fungus was identified as P. xanthii. In different countries and regions, P. xanthii has been previously reported on A. indica from Sudan and India (Amano 1986). To our knowledge, this is the first record of P. xanthii infecting A. indica in China. We are concerned that the pathogen could become a threat to the widespread planting of A. indica in the future.

Unveiling stress-adapted endophytic bacteria: Characterizing plant growth-promoting traits and assessing cross-inoculation effects on Populus deltoides under abiotic stress

In the face of the formidable environmental challenges precipitated by the ongoing climate change, Plant Growth-Promoting Bacteria (PGPB) are gaining widespread acknowledgement for their potential as biofertilizers, biocontrol agents, and microbial inoculants. However, a knowledge gap pertains to the ability of PGPB to improve stress tolerance in forestry species via cross-inoculation. To address this gap, the current investigation centres on PGPBs, namely, Acinetobacter johnsonii, Cronobacter muytjensii, and Priestia endophytica, selected from the phyllosphere of robust and healthy plants thriving in the face of stress-inducing conditions. These strains were selected based on their demonstrated adaptability to saline, arid, and nitrogen-deficient environments. The utilization of PGPB treatment resulted in an improvement of stomatal conductance (gs) and transpiration rate (E) in poplar plants exposed to both salt and drought stress. It also induced an increase in essential biochemical components such as proline (PRO), catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD). These reactions were accompanied by a decrease in leaf malonaldehyde (MDA) content and electrolyte leakage (EL). Furthermore, the PGPB treatment demonstrated a notable enhancement in nutrient absorption, particularly nitrogen and carbon, achieved through the solubilization of nutrients. The estimation of canopy temperature via thermal imaging proved to be an efficient method for distinguishing stress reactions in poplar than conventional temperature recording techniques. In summation, the utilization of PGPB especially Cronobacter muytjensii in this study, yielded profound improvements in the stress tolerance of poplar plants, manifesting in reduced membrane lipid peroxidation, enhanced photosynthesis, and bolstered antioxidant capacity within the leaves.