Leaf Spot Research Articles

Apple Bitter Rot and Glomerella Leaf Spot: A Comprehensive Review of Causal Species and Their Biology, Fungicide Sensitivities, and Management Strategies.

Bitter rot and Glomerella leaf spot (GLS) are two distinct diseases of apple fruit and foliage caused by members of the ascomycete fungal genus Colletotrichum. While GLS is restricted to subtropical and in some areas to temperate climates, bitter rot is responsible for significant yield loss worldwide, particularly during the post-harvest period. Initially thought to be caused by just two species of Colletotrichum, C. acutatum and C. gloeosporioides, advances in molecular biology and sequencing techniques enabled the identification of 25 different species capable of causing bitter rot and/or GLS of apple belongs to the C. gloeosporioides species complex (CGSC), C. acutatum species complex (CASC) and C. boninense species complex (CBSC). Three species (C. gloeosporioides, C. fructicola, and C. chrysophilum) of CGSC cause both bitter rot and GLS, 18 species (6 of CGSC and 12 of CASC) only cause bitter rot, and four species (C. aenigma and C. asianum of CGSC, C. limetticola of CASC and C. karstii of CBSC) only cause GLS. These species were found to differ in their geographical distribution, environmental and host tissue preference, pathogenicity, and fungicide sensitivities. In this review, we summarize the distribution, life cycle, and pathogenicity mechanisms of all currently known Colletotrichum species responsible for bitter rot and GLS of apple. Furthermore, we describe known apple defense mechanisms and management strategies for the control of these economically significant pathogens and identify gaps in our present understanding for future research.

First report of leaf spot disease caused by Pseudopithomyces chartarum on Lonicera caerulea L. in Heilongjiang Province, China.

Blue honeysuckle (Lonicera caerulea L.) cultivation has gradually expanded in China but continues to be limited by challenges such as leaf spot disease. Between September 2022 and September 2023, a leaf spot disease was observed on approximately 30% of 'Lanjingling' blue honeysuckles grown in a 2.66 ha field (a total of about 11,000 plants) in Jiamusi city (130.47°E, 46.16°N), Heilongjiang Province, China. Affected plants displayed brown necrotic lesions on their leaves that gradually expanded in area until the leaves fell off the plant entirely. Small, 3 to 4 mm segments of infected tissue from 50 randomly selected leaves were surface sterilized with 75% ethanol for 30 s and 5% sodium hypochlorite (NaOCl) for 3 min, rinsed three times with sterile distilled water, dried on paper towels, and plated in 9 cm Petri dishes containing potato dextrose agar (PDA) (Yan et al. 2022). Five pathogens (LD-232, LD-233, LD-234, LD-235, and LD-236) were isolated on PDA and displayed a conidia morphology consistent with Pseudopithomyces spp. (Perelló et al. 2017). The fungal colonies on PDA were villiform, white, and whorled and had sparse aerial mycelium on the surface with black conidiomata. The conidia were obpyriform and dark brown, had 0 to 3 transverse and 0 to 1 longitudinal septa, and measured 9.00 to 15.30 μm × 5.70 to 9.30 μm in size (n = 50). Genomic DNA was extracted from a representative isolate, LD-232, for molecular verification and PCR amplification was performed with ITS1/ITS4 (White et al. 1990), LROR/LR7 (Carbone and Kohn 1999), and RPB2-5F2/RPB2-7CR (Liu et al. 1999) primers. Sequences of LD-232 ITS (OR835654), LSU (OR835652), and RPB2 (OR859769) revealed 99.8% (530/531 nt), 98.8% (639/647 nt), and 99.8% (1015/1017 nt) shared identity with Pseudopithomyces chartarum sequences (OP269600, OP237014, and MK434892), respectively (Wu et al. 2023). Bayesian inference (BI) was used to construct the phylogenies using Mr. Bayes v. 3.2.7 to confirm the identity of the isolates (Ariyawansa et al. 2015). Phylogenetic trees cannot be constructed based on the genes' concatenated sequences because selective strains do not have complete rDNA-ITS, LSU, and RPB2 sequences. Therefore, based on the morphological characteristics and molecular phylogeny, LD-232 was identified as P. chartarum (Perelló et al. 2017; Wu et al. 2023). A pathogenicity test was performed with six healthy, two-year-old 'Lanjingling' blue honeysuckle plants. Three plants were inoculated by spraying the LD-232 conidial suspension (1 × 106 spores/ml) or clean water as an experimental control condition (Wu et al. 2023; Yan et al. 2023). All plants were cultured in a greenhouse at 28℃ under a 12-h light/dark cycle, and each experiment was replicated three times. Typical leaf spot symptoms were observed on inoculated leaves after 10 days. The same pathogens were reisolated from infected leaves, displayed the same morphological and molecular traits, and were again identified as P. chartarum, confirming Koch's postulate. P. chartarum previously caused leaf spot disease on Tetrapanax papyrifer in China (Wu et al. 2023). To our knowledge, this is the first report of blue honeysuckle leaf spot caused by P. chartarum in China. Identification of P. chartarum as a disease agent on blue honeysuckle will help guide future management of leaf diseases for this economically important small fruit tree.

The Origin of The Problem: Characterization of Paraguayan Septoria steviae, Causal Agent of Septoria Leaf Spot of Stevia Based on Multi-Locus Sequence Analysis.

Septoria leaf spot is a significant disease affecting cultivated stevia, potentially reducing yields by > 50%. The disease is caused by Septoria steviae, first identified in 1978 in Japan as a new pathogen of stevia. Understanding the origin of S. steviae could clarify how it spread to new production areas. To investigate this, twelve isolates of Septoria sp. were obtained from stevia's native range in the Amambay forests and field plantings in Paraguay from 2018 to 2020. These isolates underwent colony morphology and molecular characterization of Actin, β-Tubulin, Calmodulin, ITS, LSU, RPB2, and TEF1α loci. GenBank sequences from S. steviae isolates collected in France, Japan, and the United States (USA) were included. Multi-locus sequence phylogenetic analysis generated a maximum likelihood (ML) tree. The morphological characteristics of Paraguayan isolates were similar to previously reported S. steviae type cultures from Japan. The ML analysis showed that Paraguayan isolates formed a monophyletic group with S. steviae isolates from France, Japan, and the USA. During blotter tests, pycnidia and cirri of S. steviae were observed on multiple stevia seed surfaces from different sources. Further characterization confirmed viable pathogenic conidia of S. steviae. This observation suggests that S. steviae could be associated with stevia seed, possibly spreading from the center of origin to other countries. This research is the first to genetically characterize S. steviae from Paraguay and propose its potential spread mechanism from the center of origin to the rest of the world.

Cardamom Plant Disease Detection Approach Using EfficientNet

Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamil Nadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U2 -Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%. Index Terms — Cardamom plant, deep learning, machine learning, neural networks, plant pathology

Various tomato infection discrimination using spectroscopy

AbstractDiagnosing plant diseases is a difficult task, but it could be made easier with the use of advanced instrumentation and the latest machine learning techniques. This paper is a further development of a previous authors study by the authors, which has been extended to provide the classification method for tomato diseases and to indicate the spectral ranges of greatest importance for this process. As tomatoes are one of the most popular and consumed vegetables, and diseases of this crop even reduce yields by up to 80% every year, their detection is a vague topic. This manuscript describes research in which spectroscopy was used to develop methods for discriminating between selected tomato diseases. The following, frequently occurring diseases were investigated for this research: anthracnose, bacterial speck, early blight, late blight, and Septoria Leaf Spot. The study used a dataset consisting of 3877 measurements taken with the ASD FieldSpec 4 Hi-Res spectroradiometer in the 350–2500 nm range from 2019/09/10 to 2019/12/20. The highest classification efficiency ($$F_1$$ F 1 score) of 0.896 was obtained for the logistic regression based model which was evaluated on Septoria Leaf Spot disease records.

Identification of Curvularia eragrostidis (Henn.) J.A.Mey. the Leaf Spot Pathogen of Oil Palm (Elaeis guineensis Jacq.) and It’s Control by False Elder (Peronema canescens Jack) Leaf Extract

Oil palm is the main vegetable oil-producing crop in Indonesia. Leaf spot disease is one of the major diseases that attacks oil palm seedlings at all seedling stages. In Indonesia, leaf spot disease in oil palm nurseries is most commonly caused by the genus Curvularia with an infection intensity of up to 60–70%. The control of leaf spot disease usually uses chemical fungicides but its continuous use can cause the development of resistant pathogen fungi and have a long-term negative impact on the environment. False elder (Peronema canescens Jack) leaves have bioactivity as an antimicrobial control disease caused by fungal infections. In this study, Curvularia was isolated from oil palm seedlings infected with leaf spot disease from oil palm nurseries in South Sumatra. Effectiveness testing of P. canescens leaves conducted by a Completely Randomized Design method with five treatments and three replications. The fungi that caused leaf spot disease in oil palm were identified as Curvularia eragrostidis. P. canescens leaf water extract at 25% concentration was very ineffective to quite effective in controlling disease severity based on the average number of spots and diameter of spots in oil palm with the values 36.25–59.50% and 12.50–27.78%. P. canescens leaf water extract could reduce the average number of spots and diameter of spots started on day 30 after being sprayed three times with P. canescens leaf water extract so that it could be used as an alternative to control C. eragrostidis leaf spot disease in oil palm that is more friendly to the environment.

Leaf Spot Disease of Red Clover Caused by Leptosphaeria weimeri (=Longiseptatispora meliloti) in China

Leaf Spot Disease of Red Clover Caused by Leptosphaeria weimeri (=Longiseptatispora meliloti) in China

Winter Durum Wheat Disease Severity Detection with Field Spectroscopy in Phenotyping Experiment at Leaf and Canopy Level

Accurate disease severity assessment is critical for plant breeders, as it directly impacts crop yield. While hyperspectral remote sensing has shown promise for disease severity assessment in breeding experiments, most studies have focused on either leaf or canopy levels, neglecting the valuable insights gained from a combined approach. Moreover, many studies have centered on experiments involving a single disease and a few genotypes. However, this approach needs to accurately represent the challenges encountered in field conditions, where multiple diseases could occur simultaneously. To address these gaps, our current study analyses a combination of diseases, yellow rust, brown rust, and yellow leaf spots, collectively evaluated as the percentage of the diseased leaf area relative to the total leaf area (DA) at both leaf and canopy levels, using hyperspectral data from an ASD field spectrometer. We quantitatively estimate overall disease severity across fifty-two winter durum wheat genotypes categorized into early (medium milk) and late (late milk) groups based on the phenophase. Chlorophyll content (CC) within each group is studied concerning infection response, and a correlation analysis is conducted for each group with nine vegetation indices (VI) known for their sensitivity to rust and leaf spot infection in wheat. Subsequent parametric (linear and polynomial) and nonparametric (partial least squares and kernel ridge) regression analyses were performed using all available spectral bands. We found a significant reduction in Leaf CC (>30%) in the late group and Canopy CC (<10%) for both groups. YROI and LRDSI_1 are the VIs that exhibited notable and strong negative correlations with Leaf CC in the late group, with a Pearson coefficient of −0.73 and −0.72, respectively. Interestingly, spectral signatures between the early and late disease groups at both leaf and canopy levels exhibit opposite trends. The regression analysis showed we could retrieve leaf CC only for the late group, with R2 of 0.63 and 0.42 for the cross-validation and test datasets, respectively. Canopy CC retrieval required separate models for each group: the late group achieved R2 of 0.61 and 0.37 (cross-validation and test), while the early group achieved R2 of 0.48 and 0.50. Similar trends were observed for canopy DA, with separate models for early and late groups achieving comparable R2 values of 0.53 and 0.51 (cross-validation) and 0.35 and 0.36 (test), respectively. All of our models had medium accuracy and tended to overfit. In this study, we analyzed the spectral response mechanism associated with durum wheat diseases, offering a novel crop disease severity assessment approach. Additionally, our findings serve as a foundation for detecting resistant wheat varieties, which is the most economical and environmentally friendly management strategy for wheat leaf diseases on a large scale in the future.

Impact of Treated Castor Leaves on Growth and Productivity of Eri Silkworms: A Bioassay Study

One of the most common foliar diseases in castor cultivation for ericulture is Cercospora leaf spot, caused by Cercospora ricinella Sacc. & Berl. which seriously damages leaves meant as food for eri silkworms. Deterioration in the quality or quantity of castor leaves primarily hampers the health of eri silkworm, which ultimately affects the economic parameters of the cocoons produced by them. A bioassay study was conducted during 2019-2021 at College of Sericulture, Assam Agricultural University, Jorhat, Assam, to check the effects of feeding castor leaves, treated with treatments found promising for eco-friendly management of Cercospora leaf spot of castor. Eight treatments with three replications were taken for the experiment which included a phytoextract of Tulsi (Ocimum sanctum) @ 5%, a bio agent (Trichoderma viride) @ 5% and a fungicide (Copper oxychloride) @ 0.2 % and their combinations, applied as foliar spray at 15 days interval after 30 days of transplanting. These treated leaves were fed as first feed to the 3rd instar silkworms after maintaining 25-30 days as safe period. Silkworms fed with castor leaves treated with T.viride + Tulsi extract showed the best results in overall health (ERR 91.66%, Fecundity 391, Shell ratio 13.44%) followed by worms fed with leaves treated with Tulsi extract + T. viride + COC (ERR 80.55%, Fecundity 370, Shell ratio 12.59%).

Colletotrichum cliviicola as a new causal agent causing leaf spot disease on Tilia tomentosa moench (silver linden) in China

Tilia tomentosa Moench (silver linden) is a globally preferred option for landscaping and street tree planting. Its intentional introduction to China in recent years has led to widespread cultivation. In 2021, a leaf spot disease significantly impacted silver linden trees in a sentinel garden in China, with an average incidence and disease severity of 94.3% and 5.0, respectively, adversely affecting the trees' growth and development. In this investigation, potential fungal pathogens were isolated from the affected leaves, and Koch's postulate was subsequently employed to validate the pathogenicity of the fungal isolates (TM-1 and TM-2). The pathogen was unequivocally identified as Colletotrichum cliviicola through a combination of morphological observation and a multi-locus analysis (ITS, ACT, ApMat, CAL, CHS-1, GAPDH, and TUB2). This marks the first record of C. cliviicola affecting silver linden in China. The information provided a valuable foundation for the rapid detection and effective prevention and control of the diseases affecting linden trees.

Cercospora leaf spot management with nitrogenous fertilizers in cotton is dependent on the disease amount in the plant canopies

The aim of this study was to examine the impact of both organic and inorganic nitrogen fertilizers on the intensity and epidemiological components of Cercospora leaf spot across three different canopies of cotton plants, specifically the variety DP ACALA 90, under field conditions. Fertilizers used in the study included bovine manure, Jatropha curcas seedcake, poultry manure, and urea (the latter serving as a control). These were applied at 20 days after plant emergence and then during the flowering stage until the total nitrogen (N) dose reached 50, 100, 150, or 200 kg N per hectare. The incidence and severity of the disease were assessed starting at the reproductive stage B1 (the first visible flower bud) across the lower, middle, and upper canopies of four cotton plants, with six evaluations conducted over time. To calculate the initial inoculum (Y0) and the disease progress rate (r), the Exponential, Gompertz, and Logistic models were employed based on temporal data. The study was designed as a randomized complete block with a 4x4 factorial arrangement (fertilizer type x dose), and mean comparisons were made using Tukey's test (p ≤ 0.05). It was found that disease intensity within each canopy level varied in response to the type of nitrogen source and the applied dose, with the severity of the disease (including the area under the disease progress curve) being particularly influenced. A significant interaction between the type of fertilizer and the dose regarding the intensity of Cercospora leaf spot was observed. The Exponential model most accurately depicted the disease's temporal progression. Notably, poultry manure and urea were the fertilizers that most adversely influenced the intensity and initial inoculum (Y0) of Cercospora leaf spot across the various canopies of the cotton plants. The findings suggest that the use of organic fertilizers in cotton cultivation could represent a viable sustainable management strategy.

Screening of Mung bean Germplasm for Resistance against Cercospora Leaf Spot under Natural Field Conditions

Mung bean is an excellent protein source pulse crop grown throughout the world. Punjab province accounts for 80% area and production of mung bean in Pakistan. Mung bean is cultivated on a large scale for its green manuring ability and grain consumption. Mung bean is prone to economically important fungal, bacterial, and viral diseases. Cercospora leaf spot (CLS) caused by Cercospora canescens is one of the most serious disease concerns in Pakistan. The pulses program at the National Agriculture Research Centre, Islamabad is trying hard to get the resistant germplasm against CLS. Sixty-eight advance lines/approved varieties were evaluated in the augmented design under natural field conditions. Forty-one advance lines/approved varieties were found resistant and twenty-five advance lines/approved varieties were found moderately resistant. Two advanced lines/approved varieties did not perform well and were susceptible to CLS infection. This data indicates that the germplasm at pulses program is excellent in resistance against CLS under natural field conditions in the Arid Zone and can serve as potential future varieties.

Assessing the Effectiveness of Neem oil and Microalgae in the Management of Cowpea (Vigna unguiculata L.) Leaf Spot Caused by Alternaria spp.

The current study aimed to assess the effectiveness of neem oil and microalgae in controlling leaf spot caused by Alternaria spp. in cowpea (Vigna unguiculataL.). The research was conducted during the Zaid season of 2022 at the Central Research Field of the Department of Plant Pathology, SHUATS, Prayagraj, Uttar Pradesh, using a Randomized Block Design with three replications and seven treatments. Analysis of variance (ANOVA) was utilized to draw conclusions from the data. Leaf spot disease caused by Alternaria spp poses a significant threat to cowpea crops, resulting in yield losses and decreased plant vigor. An in vivo study was carried out to evaluate the impact of different concentrations of microalgae combined with neem oil at 1% on plant growth parameters and Disease Intensity (%). The application of microalgae @540 grams in various splits and neem oil @1% was done. Among all the treatments, T4 (Microalgae 48gms @5 DAS 96gms@15,45 DAS + Neem oil @1% showed promising results. Notably, the T4 treatment significantly increased plant height (84.63 cm) and the number of branches(10.83) at60DAS. Moreover, an increased numberof nodules per plant (21.44) was observed at 75 DAS, indicating improved root health and nitrogen fixation. Additionally, the treatment led to longer pod length (34.44 cm) in three pickings, suggesting enhanced yield potential. Furthermore, the combined application of microalgae and neem oil resulted in a significant reduction in disease intensity, with a notable decrease of 24.47% compared to untreated plants and those treated with chemicals (Bavistin). This indicates that the use of microalgae and neem oil together effectively suppressed leaf spot disease caused by Alternaria spp. in cowpea, thereby improving plant health and productivity. In conclusion, these findings underscore the potential use of eco-friendly alternatives like microalgae and neem oil for managing leaf spot disease in cowpea cultivation. Further research in this area is recommended for sustainable agricultural practices.

Searching for pathogenic fungi of Passiflora foetida sensu lato in Colombia, South America, with potential for classical biological control in Australia

ABSTRACT Interest in plant pathogens for classical biological control (CBC) of invasive introduced plants has increased steadily since the 1970s. The introduced plant Passiflora foetida sensu lato invades large tracts of land across northern Australia and CBC represents a possible solution to reduce its abundance and spread. Field surveys for foliar fungal pathogens of P. foetida s.l. were performed between 2017 and 2020 in Colombia. Molecular sequencing was used to confirm the identity of plants surveyed at each site and to explore genetic variation between sites. Leaf spots of various sizes and sometime associated with chlorosis as well as necrosis of leaf edges were the main disease symptoms observed. These symptoms were not severe and did not appear to affect plant growth. No disease symptoms caused by obligate biotrophic fungi nor girdling lesions on stems were observed. Of the 125 fungal isolates recovered from the collected diseased material, only 21 were considered as pathogenic on P. foetida s.l. in detached-leaf assays. Morphological examinations and sequencing of the 21 pathogenic isolates revealed that they belong to 11 genera. Based on the low severity of disease symptoms observed in the field, we conclude that these isolates do not warrant further investigations as potential CBC agents.

Evaluation and Control of Fusarium acuminatum Causing Leaf Spot in Saposhnikovia divaricata in Northeast China.

Saposhnikovia divaricata is an authentic Chinese herbal medicine in Northeast China named Guanfangfeng, which is made from very high-quality plants for sufficient efficacy. However, leaf spot causes a very large reduction in the yield and quality of S. divaricata in Shuangyashan (46.58°N, 131.28°E), Northeast China. A total of 18 isolates were isolated from the diseased leaves of S. divaricata, following Koch's postulates, and identified as Fusarium acuminatum based on morphological, molecular biological, and phylogenetic tree analyses. To the authors' knowledge, this is the first report of F. acuminatum causing S. divaricata leaf spot in China. F. acuminatum infected perilla and mung beans but not foxtail millet, peanuts, wheat, peas, rye, red beans, or sorghum. Susceptibility assessment of F. acuminatum to fungicides using the mycelial growth rate method showed that isolates of F. acuminatum were most sensitive to prochloraz, with effective concentration values of 0.0005413 to 0.0009523 μg/ml. In the two field experiments, the average control efficacy of prochloraz at 0.450 g/liter on S. divaricata leaf spot caused by F. acuminatum was 75.42%. Therefore, nonhost plant rotation or intercropping with suitable chemical fungicides may be used to control S. divaricata leaf spot. This study's results provide a theoretical basis for controlling S. divaricata leaf spot and will facilitate the development of effective disease management programs.

PEPPER AND CORN LEAVES CLASSIFICATION AND SEVERITY IDENTIFICATION USING HYBRID OPTIMIZATION BASED U-NET MODEL

Abstract An agricultural product plays a major role in the economical growth of developing countries. Agricultural products like pepper and corn are the essential crops with respect to human health food security. But, these two crops are prone to different diseases such as gray leaf spot, common rust and fruit rot which affects the productivity of crops. Further, the severity identification is also a challenging one. To address these limitations, this work presents different approaches for identifying the crop lesions and predicting the severity and thereby increasing the productivity of crops. The development of the proposed model includes steps such as dataset collection, noise removal, segmentation, feature extraction, classification and severity prediction. Initially, the crop images are pre-processed by the median filter and the pre-processed images are processed are segmented, extracted and classified by the optimized U-Net model. Moreover, hybrid optimizer which is the integration of GJA (Golden jackal algorithm) and RDA (Red deer algorithm) are utilized for precise segmentation and classification. Finally, the severity prediction is computed for the diseased leaves by the measuring the size of image pixels. The experimentation is carried out on the PlantVillage dataset; the accuracy and precision values achieved are 99.2% and 99.1%. Thus, the experimental outcomes show the effectiveness and stability of the model.

An efficient deep learning model for tomato disease detection

Tomatoes possess significant nutritional and economic value. However, frequent diseases can detrimentally impact their quality and yield. Images of tomato diseases captured amidst intricate backgrounds are susceptible to environmental disturbances, presenting challenges in achieving precise detection and identification outcomes. This study focuses on tomato disease images within intricate settings, particularly emphasizing four prevalent diseases (late blight, gray leaf spot, brown rot, and leaf mold), alongside healthy tomatoes. It addresses challenges such as excessive interference, imprecise lesion localization for small targets, and heightened false-positive and false-negative rates in real-world tomato cultivation settings. To address these challenges, we introduce a novel method for tomato disease detection named TomatoDet. Initially, we devise a feature extraction module integrating Swin-DDETR’s self-attention mechanism to craft a backbone feature extraction network, enhancing the model’s capacity to capture details regarding small target diseases through self-attention. Subsequently, we incorporate the dynamic activation function Meta-ACON within the backbone network to further amplify the network’s ability to depict disease-related features. Finally, we propose an enhanced bidirectional weighted feature pyramid network (IBiFPN) for merging multi-scale features and feeding the feature maps extracted by the backbone network into the multi-scale feature fusion module. This enhancement elevates detection accuracy and effectively mitigates false positives and false negatives arising from overlapping and occluded disease targets within intricate backgrounds. Our approach demonstrates remarkable efficacy, achieving a mean Average Precision (mAP) of 92.3% on a curated dataset, marking an 8.7% point improvement over the baseline method. Additionally, it attains a detection speed of 46.6 frames per second (FPS), adeptly meeting the demands of agricultural scenarios.

Occurrence of Cassava (Manihot esculenta) Leaf Spot Disease Caused by Diaporthe ueckeri in China.

Cassava (Manihot esculenta) is a perennial crop of the family Euphorbiaceae, widely cultivated due to its phytopharmacological and economic values in China. In November 2022, a leaf spot disease on cassava was observed in Zhanjiang, Guangdong, China (21.17° N, 110.18° E), with 100% disease incidence. About 80 % of leaves were covered with spots on the infected plants. Typical symptoms initially appeared as irregular water-soaked lesions that became brown and whitish with the progress of the disease, lesions gradually expanded and coalesced, causing leaf withering, drying and final fall. Tissues (4 to 5 mm) were excised from the margin of lesions, sterilized in 3% H2O2 solution for 3 min, rinsed three times with sterile water, placed on potato dextrose agar (PDA) medium (containing 50mg/L penicillin), and incubated at 25-28 °C. Ten single hypha isolates with similar morphology were obtained and further purified as single conidium subcultures. The colony was grey whitish with sparse aerial mycelium and colony diameter reached 70.4 mm after four days incubation at 25-28℃ in the dark. Black pycnidia occurring as clusters were spherical or irregular, erumpent at maturity, often with a creamy whitish, conidial cirrus extruding from ostiole after 30-days incubation. Conidiophores were hyaline, smooth, unbranched. Alpha conidia were bi-guttulate, hyaline, ellipsoidal, aseptate, with dimensions of 5.1~7.5×1.9~3.4µm (mean 6.2×2.8 µm, n>50). Beta conidia were abundant, filiform, hyaline, smooth, curved in a hooked shape, with a truncate base and dimensions of 18.5-26.4 × 0.6-1.2μm (mean 23.4 × 1.0 μm, n= 40) . Gamma conidia were not observed. The morphological characteristics were similar to those of Diaporthe ueckeri (Udayanga et al. 2015). The internal transcribed spacer (ITS) region, large subunit (LSU) rRNA sequence, actin (ACT), calmodulin (CAL), histone H3 (HIS), translation elongation factor 1-alpha (TEF1-α), and β-tubulin (TUB) genes of a representative isolate CCAS-MS-6 (ACCC 35497) were amplified and sequenced using primer pairs: ITS5/ITS4, LR0R/LR5, ACT-512F/ACT-783R, CAL228F/CAL737R, CYLH3F/ H3-1b, EF1-728F/ EF1-986R and Bt2a/Bt2b (Gao et al 2017；Udayanga et al 2014). All sequences were deposited in GenBank (OR361671, OR361672, and OR365605-9). BLAST search showed high similarities with sequences of Diaporthe ueckeri (Tab 1). Maximum likelihood analyses of the concatenated data of CAL, HIS, ITS, TEF and TUB using Mega 11 placed CCAS-MS-6 in the D. ueckeri clade. Thus, the fungus was identified as D. ueckeri. Three one-year old healthy plants were used for pathogenicity tests in pots. Two 15-day old leaves of each plant were cleaned with 75% alcohol, three sites on each leaf were wounded, and sites on one of the leaf were covered with fungal plugs from 15-day-old cultures on PDA, and sites on the other leaf with PDA plugs as a control. All plants were kept at ambient temperature (about 28℃) and covered with plastic bags containing sterile wet cotton to maintain the humidity. Seven days after inoculation, all inoculated sites showed symptoms of necrosis, while control sites showed no symptoms. The same fungus identified on the basis of morphological and molecular criteria was reisolated from symptomatic inoculated leaves. In China, D. ueckeri had been reported to cause diseases on Eucalyptus citriodora, Camellia sinensis, and Michelia shiluensis (Gao et al 2016; Liao et al 2023; Yi et al 2018), this is the first report on M. esculenta. The definition of the disease etiology is a prerequisite to develop effective management strategies.

New records of Pestalotioid species associated with leaf spot disease on Camellia sinensis from northern Thailand

Camellia sinensis (L.) Kuntze var. assamica (Miang tea) is widely distributed in northern Thailand due to its traditional and industrial attributes, including black tea and Miang production. In this study, two Pestalotioid taxa associated with C. sinensis leaf spots were collected in Mae Taeng district, Chiang Mai Province, Thailand. Species delineation was based on the evidence from morphological and multi-locus phylogenies using ITS, tub2 and tef1-α. Neopestalotiopsis saprophytica is herein reported as a new record on Camellia sinensis, while Pseudopestalotiopsis chinensis is recorded as a new geographical record from Thailand. The findings of this research have the potential to offer fresh insights into the two previously documented species within the existing fungal community associated with C. sinensis in Thailand. This, in turn, could enhance our comprehension of their interactions with the host plant in the times ahead.

Plant density and variety effect on yield, leaf spot disease, weed species richness and diversity of groundnut production in northern Ghana

AbstractLow plant density and weed infestation are major challenges for groundnut production in northern Ghana. A two‐year on‐farm study was conducted to determine the effect of plant density and variety on grain and fodder yields, incidence of leaf spot disease, weed species diversity and biomass. A factorial treatment combination of 6 varieties and 4 plant densities laid in strip plot design with 4 replications was used. The varieties were (early maturity type: Chinese, Yenyewoso, Samnut 23 and late maturity type: Azivivi, Manipinta, Samnut 22). The plants density included 9, 11, 15 and 22 plants/m2. The late maturity varieties recorded higher (p < .05) canopy cover, grain and fodder yields relative to that of the early maturity varieties. The late maturity varieties also recorded the least sedge weed species frequency, density and incidence of leaf spot disease compared with that of the early maturity varieties. The canopy cover, grain and fodder yields increased with increasing plant density. Broadleaf weed species frequency and density, weed biomass, richness, and diversity declined with increasing plant density. Grain yield showed negative and significant correlation with broadleaf weed species frequency, density and weed biomass. The results suggest that both early and late maturity groundnut varieties can be planted at a density of 22 plants/m2 to increase grain and fodder yields and reduce weed species richness, diversity and growth in northern Ghana and similar agro‐ecology in West Africa.