UNASSIGNED: Trichoderma species establish symbiotic relationships with plants through both parasitic and mutualistic mechanisms. While some Trichoderma species act as plant pathogenic fungi, others utilize various strategies to protect and enhance plant growth.
UNASSIGNED: Phylogenetic positions of new species of Trichoderma were determined through multi-gene analysis relying on the internal transcribed spacer (ITS) regions of the ribosomal DNA, the translation elongation factor 1-α (tef1-α) gene, and the RNA polymerase II (rpb2) gene. Additionally, pathogenicity experiments were conducted, and the aggressiveness of each isolate was evaluated based on the area of the cross-section of the infected site.
UNASSIGNED: In this study, 13 Trichoderma species, including 9 known species and 4 new species, namely, T. delicatum, T. robustum, T. perfasciculatum, and T. subulatum were isolated from the diseased tubers of Gastrodia elata in Yunnan, China. Among the known species, T. hamatum had the highest frequency. T. delicatum belonged to the Koningii clade. T. robustum and T. perfasciculatum were assigned to the Virens clade. T. subulatum emerged as a new member of the Spirale clade. Pathogenicity experiments were conducted on the new species T. robustum, T. delicatum, and T. perfasciculatum, as well as the known species T. hamatum, T. atroviride, and T. harzianum. The infective abilities of different Trichoderma species on G. elata varied, indicating that Trichoderma was a pathogenic fungus causing black rot disease in G. elata.
UNASSIGNED: This study provided the morphological characteristics of new species and discussed the morphological differences with phylogenetically proximate species, laying the foundation for research aimed at preventing and managing diseases that affect G. elata.

Tobacco mosaic virus (TMV) was the first virus to be studied in detail and, for many years, TMV and other tobamoviruses, particularly tomato mosaic virus (ToMV) and tobamoviruses infecting pepper (Capsicum spp.), were serious crop pathogens. By the end of the twentieth and for the first decade of the twenty-first century, tobamoviruses were under some degree of control due to introgression of resistance genes into commercial tomato and pepper lines. However, tobamoviruses remained important models for molecular biology, biotechnology and bio-nanotechnology. Recently, tobamoviruses have again become serious crop pathogens due to the advent of tomato brown rugose fruit virus, which overcomes tomato resistance against TMV and ToMV, and the slow but apparently inexorable worldwide spread of cucumber green mottle mosaic virus, which threatens all cucurbit crops. This review discusses a range of mainly molecular biology-based approaches for protecting crops against tobamoviruses. These include cross-protection (using mild tobamovirus strains to \'immunize\' plants against severe strains), expressing viral gene products in transgenic plants to inhibit the viral infection cycle, inducing RNA silencing against tobamoviruses by expressing virus-derived RNA sequences in planta or by direct application of double-stranded RNA molecules to non-engineered plants, gene editing of host susceptibility factors, and the transfer and optimization of natural resistance genes.

Phytoplasma-associated diseases are mainly insect-transmitted and are present worldwide. Considering that disease detection is a relevant environmental factor that may elucidate the presence of these diseases, a review reporting the geographic distribution of phytoplasma taxa in geographically consistent areas helps manage diseases appropriately and reduce their spreading. This work summarizes the data available about the identification of the phytoplasma associated with several diverse diseases in South America in the last decades. The insect vectors and putative vectors together with the plant host range of these phytoplasmas are also summarized. Overall, 16 \'Candidatus Phytoplasma\' species were detected, and those most frequently detected in agricultural-relevant crops such as corn, alfalfa, grapevine, and other horticultural species are \'Ca. P. pruni\', \'Ca. P. asteris\', and \'Ca. P. fraxini\'.

A crucial determining factor in agricultural productivity is biotic stress. In addition, supply of quality food to the ever-increasing world\'s population has raised the food demand tremendously. Therefore, enhanced agricultural crop productivity is the only option to mitigate these concerns. It ultimately demanded the often and indiscriminate use of synthetic agrochemicals such as chemical fertilizers, pesticides, insecticides, herbicides, etc. for the management of various biotic stresses including a variety of plant pathogens. However, the food chain and biosphere are severely impacted due to the use of such harmful agrochemicals and their byproducts. Hence, it is need of hour to search for novel, effective and ecofriendly approaches for the management of biotic stresses in crop plants. Particularly, in plant disease management, efforts are being made to take advantage of newly emerged science i.e. nanotechnology for the creation of inorganic nanoparticles (NPs) such as metallic, oxide, sulphide, etc. through different routes and their application in plant disease management. Among these, green nanomaterials which are synthesized using environmentally friendly methods and materials reported to possess unique properties (such as high surface area, adjustable size and shape, and specific functionalities) making them ideal candidates for targeted disease control. Nanotechnology can stop crop losses by managing specific diseases from soil, plants, and hydroponic systems. This review mainly focuses on the application of biologically produced green NPs in the treatment of plant diseases caused due to bacteria, viruses, and fungi. The utilization of green synthesis of NPs in the creation of intelligent targeted pesticide and biomolecule control delivery systems, for disease management is considered environmentally friendly due to its pursuit of less hazardous, sustainable, and environmentally friendly methods.

Guava (Psidium guajava L.) is a popular fruit crop that is widely cultivated in Thailand. In November 2023, brown spot disease on guava was observed during postharvest storage at 22 to 31°C and 70 to 75% relative humidity over a period of 3 to 7 days in Fang District, Chiang Mai Province, Thailand. The disease incidence was ~20% of 100 fruits per pallet box. The disease severity on each fruit ranged from 40 to 70% of the surface area affected by lesions. The symptoms appeared as circular to irregular brown to dark brown spots, ranging from 5 to 30 mm in diameter. Fungi were isolated from lesions using a single conidial isolation method (Choi et al. 1999). Two fungal isolates (SDBR-CMU497 and SDBR-CMU498) with similar morphology were obtained. Colonies on potato dextrose agar (PDA) and malt extract agar (MEA) were 65 to 67 and 29 to 38 mm in diameter, respectively after incubation for 1 week at 25°C. Colonies on PDA and MEA were flat, slightly undulate, greenish gray in the center, greyish green at the margin; reverse black. Both isolates produced asexual structures. Pycnidia were black, granular, and grouped. Conidiogenous cells were hyaline, subcylindrical to cylindrical, 8.5 to 17.5 × 3 to 5.5 µm. Conidia were single-celled, hyaline, obovoid to ellipsoid, 5.2 to 9.4 × 3.6 to 7.5 µm (n = 50), smooth-walled, with a single apical appendage. Morphologically, both isolates resembled Phyllosticta capitalensis (Wikee et al. 2013). The internal transcribed spacer (ITS) region, large subunit (nrLSU), translation elongation factor 1-alpha (tef1-α), actin (act), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) genes were amplified using primer pairs ITS5/ITS4, LROR/LRO5, EF1-728F/EF2, ACT-512F/ACT-783R, and GPD1-LM/GPD2-LM, respectively (White et al. 1990; Zhang et al. 2022). Sequences were deposited in GenBank (ITS: PP946770, PP946771; nrLSU: PP948677, PP948678; tef1-α: PP948012, PP948013; act: PP948014, PP948015; GAPDH: PP948016, PP948017). Maximum likelihood phylogenetic analyses of the concatenated five genes identified both isolates as P. capitalensis. Thus, both morphology and molecular data confirmed the fungus as P. capitalensis. To confirm pathogenicity, healthy commercial guava fruits cultivar Kim Ju were surface disinfected by 0.1% NaClO for 3 min, rinsed three times with sterile distilled water, and wounded (Cruz-Lagunas et al. 2023). Conidia were collected from 2-week-old cultures on PDA and suspended in sterile distilled water. Fifteen microliters of a 1 × 106 conidia/ml suspension were dropped onto the wounded fruits. Mock inoculations were used as a control with sterile distilled water. Ten replications were conducted for each treatment and repeated twice. The inoculated fruits were stored in individual sterile plastic boxes at 25°C with 80 to 90% relative humidity. After 7 days, all inoculated fruits exhibited brown to dark brown lesions, while control fruits were asymptomatic. Phyllosticta capitalensis was consistently reisolated from the inoculated tissues on PDA to complete Koch\'s postulates. Prior to this study, P. capitalensis was known to cause brown or black spot disease on guava fruits cultivated in fields in China (Liao et al. 2020), Egypt (Arafat 2018), and Mexico (Cruz-Lagunas et al. 2023). To our knowledge, this is the first report of P. capitalensis causing postharvest brown spot disease on guava fruit in Thailand. The results will inform epidemiological investigations and future approaches to managing this disease.

Plant diseases pose a significant threat to agricultural productivity worldwide. Convolutional neural networks (CNNs) have achieved state-of-the-art performances on several plant disease detection tasks. However, the manual development of CNN models using an exhaustive approach is a resource-intensive task. Neural Architecture Search (NAS) has emerged as an innovative paradigm that seeks to automate model generation procedures without human intervention. However, the application of NAS in plant disease detection has received limited attention. In this work, we propose a two-stage meta-learning-based neural architecture search system (ML NAS) to automate the generation of CNN models for unseen plant disease detection tasks. The first stage recommends the most suitable benchmark models for unseen plant disease detection tasks based on the prior evaluations of benchmark models on existing plant disease datasets. In the second stage, the proposed NAS operators are employed to optimize the recommended model for the target task. The experimental results showed that the MLNAS system\'s model outperformed state-of-the-art models on the fruit disease dataset, achieving an accuracy of 99.61%. Furthermore, the MLNAS-generated model outperformed the Progressive NAS model on the 8-class plant disease dataset, achieving an accuracy of 99.8%. Hence, the proposed MLNAS system facilitates faster model development with reduced computational costs.

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40 × : 95.50%, and BreakHis 100 × : 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net\'s aptness for agricultural growth as well as human cancer classification.

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Caladium (Caladium × hortulanum) is an ornamental plant popular for its variable and colorful foliage. In 2020, plants showing leaf spots and blight, typical of anthracnose, were found in a field trial at the University of Florida\'s Gulf Coast Research and Education Center (UF/GCREC) in Wimauma, FL, USA. Leaf samples consistently yielded a Colletotrichum-like species with curved conidia and abundant setae production in the acervuli. The internal transcribed spacer (ITS), partial sequences of the glyceraldehyde-3-phosphate dehydrogenase gene (gapdh), actin gene (act), chitin synthase 1 gene (chs-1), beta-tubulin gene (tub2), and histone3 gene (his3) were amplified and sequenced. Blastn searches in the NCBI GenBank database revealed similarities to species of the Colletotrichum truncatum species complex. Phylogenetic analyses using multi-locus sequence data supports a distinct species within this complex, with the closest related species being C. curcumae. Based on morphological and phylogenetic analyses, a new species of Colletotrichum, named C. caladii, is reported. Pathogenicity assays and subsequent isolation confirmed that this species was the causal agent of the disease.

In the context of global population growth expected in the future, enhancing the agri-food yield is crucial. Plant diseases significantly impact crop production and food security. Modern microfluidics offers a compact and convenient approach for detecting these defects. Although this field is still in its infancy and few comprehensive reviews have explored this topic, practical research has great potential. This paper reviews the principles, materials, and applications of microfluidic technology for detecting plant diseases caused by various pathogens. Its performance in realizing the separation, enrichment, and detection of different pathogens is discussed in depth to shed light on its prospects. With its versatile design, microfluidics has been developed for rapid, sensitive, and low-cost monitoring of plant diseases. Incorporating modules for separation, preconcentration, amplification, and detection enables the early detection of trace amounts of pathogens, enhancing crop security. Coupling with imaging systems, smart and digital devices are increasingly being reported as advanced solutions.