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The objective of this work was to accomplish the isolation, molecular identification and characterizing the physiology of the causal agent of the algal spot in mango trees. For this purpose, the pathogen growth was assessed in different culture media, with subsequent observation and measurements of the filamentous cells. The molecular identification was made using mycelium obtained from leaf lesions and pure algae colonies grown in culture medium. Descriptions based on DNA sequencing indicated that the algae is Cephaleuros virescens. The algae must be isolated primarily in liquid medium for further pricking into agar medium. The highest mycelial growth average in Petri dishes occurred when the algae were grown in Trebouxia and BBM. Trebouxia enabled larger cells in the filamentous cells when compared to other culture media.

In order to quickly control crop diseases and insect pests, chaos theory is used to optimize PSO, and CPSO algorithm is proposed. In the practical application of crop diseases and insect pests identification, CBPSO algorithm is obtained by binary operation of CPSO, and its performance and application are analyzed. The experimental results show that the classification accuracy of CBPSO-SVM algorithm is 98.22% and 97.78% respectively in gray spot disease and algal spot disease, higher than that of PCA-SVM (82.5% and 83.67%). In addition, the average classification accuracy of CBPSO-SVM is 91.26%, better than that of PCA-SVM (81.83%). At the same time, through the comparison of CBPSO algorithm, BPSO algorithm and PSO algorithm, it is found that CBPSO algorithm has great adaptability, the maximum fitness is 0.99, and the average fitness is 0.97. Therefore, CBPSO algorithm has good effect on global optimization, and its convergence speed is faster, and its execution task and efficiency are higher. In addition, among the five particle swarm optimization algorithms, the CBPSO algorithm performs best in the 20 dimension, with the minimum value of 1.0008 and the minimum value of variance of 8.4206. Therefore, it is determined that the search efficiency of the CBPSO algorithm in the 20 dimension is the best. Compared with other algorithms, the stability and accuracy of the CBPSO algorithm have been greatly improved, and it has high robustness in the actual UAV image detection and crop pest identification. [ABSTRACT FROM AUTHOR]

This study proposed a weighted sampling hierarchical classification learning method based on an efficient backbone network model to address the problems of high costs, low accuracy, and time-consuming traditional tea disease recognition methods. This method enhances the feature extraction ability by conducting hierarchical classification learning based on the EfficientNet model, effectively alleviating the impact of high similarity between tea diseases on the model's classification performance. To better solve the problem of few and unevenly distributed tea disease samples, this study introduced a weighted sampling scheme to optimize data processing, which not only alleviates the overfitting effect caused by too few sample data but also balances the probability of extracting imbalanced classification data. The experimental results show that the proposed method was significant in identifying both healthy tea leaves and four common leaf diseases of tea (tea algal spot disease, tea white spot disease, tea anthracnose disease, and tea leaf blight disease). After applying the "weighted sampling hierarchical classification learning method" to train 7 different efficient backbone networks, most of their accuracies have improved. The EfficientNet-B1 model proposed in this study achieved an accuracy rate of 99.21% after adopting this learning method, which is higher than EfficientNet-b2 (98.82%) and MobileNet-V3 (98.43%). In addition, to better apply the results of identifying tea diseases, this study developed a mini-program that operates on WeChat. Users can quickly obtain accurate identification results and corresponding disease descriptions and prevention methods through simple operations. This intelligent tool for identifying tea diseases can serve as an auxiliary tool for farmers, consumers, and related scientific researchers and has certain practical value. [ABSTRACT FROM AUTHOR]

Between 2023 and 2024, a type of green alga was observed for the first time settling on Oil-tea Camellia leaves and branches in the eastern Oil-tea Camellia planting area of Hainan Island, forming a layer of gray-green moss with a rough surface that seriously interfered with the leaves' normal photosynthesis. To further research the issue, this study used the plant photosynthesis measurement system and the paraffin sectioning technique to compare and analyze the changes in photosynthetic characteristics and anatomical structure of healthy and green algal-covered Oil-tea Camellia leaves. At the same time, the algal strain was effectively separated and purified using the plate delineation method, and its species classification was determined by combining morphological observation and molecular identification based on SSU-ITS sequences. The results of the study demonstrated that the coating of green alga facilitated the lignification of the leaf's epidermal cell walls. After being covered by the green alga, the intercellular CO2 concentration (Ci) increased significantly by 21.5%, while the net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs) all significantly decreased by 72.8%, 30.4%, and 24.9%, respectively. More specifically, the green alga that covers the leaves of Oil-tea Camellia was identified as Desmodesmus armatus of Chlorophyta. Notably, the green alga had a long growth cycle, did not undergo a decline phase within one month, had an optimal growth pH of 11.0, and could flourish in excessively alkaline conditions. In conclusion, this study not only reported for the first time the phenomena of D. armatus infesting Oil-tea Camellia leave, but also showed its unique physiological and ecological properties, providing a foundation for future research on relevant prevention and control approaches. [ABSTRACT FROM AUTHOR]

Background: The occurrence, development, and outbreak of tea diseases and pests pose a significant challenge to the quality and yield of tea, necessitating prompt identification and control measures. Given the vast array of tea diseases and pests, coupled with the intricacies of the tea planting environment, accurate and rapid diagnosis remains elusive. In addressing this issue, the present study investigates the utilization of transfer learning convolution neural networks for the identification of tea diseases and pests. Our objective is to facilitate the accurate and expeditious detection of diseases and pests affecting the Yunnan Big leaf kind of tea within its complex ecological niche. Results: Initially, we gathered 1878 image data encompassing 10 prevalent types of tea diseases and pests from complex environments within tea plantations, compiling a comprehensive dataset. Additionally, we employed data augmentation techniques to enrich the sample diversity. Leveraging the ImageNet pre-trained model, we conducted a comprehensive evaluation and identified the Xception architecture as the most effective model. Notably, the integration of an attention mechanism within the Xeption model did not yield improvements in recognition performance. Subsequently, through transfer learning and the freezing core strategy, we achieved a test accuracy rate of 98.58% and a verification accuracy rate of 98.2310%. Conclusions: These outcomes signify a significant stride towards accurate and timely detection, holding promise for enhancing the sustainability and productivity of Yunnan tea. Our findings provide a theoretical foundation and technical guidance for the development of online detection technologies for tea diseases and pests in Yunnan. [ABSTRACT FROM AUTHOR]

Cephaleuros species are well-known as plant pathogens that cause red rust or algae spot diseases in many economically cultivated plants that grow in shady and humid environments. Despite their prevalence, the adaptive evolution of these pathogens remains poorly understood. We sequenced and characterized three Cephaleuros (Cephaleuros lagerheimii, Cephaleuros diffusus, and Cephaleuros virescens) chloroplast genomes, and compared them with seven previously reported chloroplast genomes. The chloroplast sequences of C. lagerheimii, C. diffusus, and C. virescens were 480,613 bp, 383,846 bp, and 472,444 bp in length, respectively. These chloroplast genomes encoded 94 genes, including 27 tRNA genes, 3 rRNA genes, and 64 protein-coding genes. Comparative analysis uncovered that the variation in genome size was principally due to the length of intergenic spacer sequences, followed by introns. Furthermore, several highly variable regions (trnY-GTA, trnL-TAG, petA, psbT, trnD-GTC, trnL-TAA, ccsA, petG, psaA, psaB, rps11, rps2, and rps14) were identified. Codon bias analysis revealed that the codon usage pattern of Cephaleuros is predominantly shaped by natural selection. Additionally, six chloroplast protein-coding genes (atpF, chlN, psaA, psaB, psbA, and rbcL) were determined to be under positive selection, suggesting they may play a vital roles in the adaptation of Cephaleuros to low-light intensity habitats. [ABSTRACT FROM AUTHOR]

Cephaleuros parasiticus Karst, a parasitic green alga that causes severe damage to agricultural and horticulture crops needs a critical understanding of its dispersion patterns to develop an effective management strategy. This work aimed to examine the potential distribution of C. parasiticus Karst in India using maximum entropy (MaxEnt) modelling strategy. The species occurrence data were gathered from several places across India. Using MaxEnt, these occurrence records were linked with environmental characteristics such as climatic, topographic indicators to create a predictive distribution model. The model's performance was assessed using the Area Under the Curve (AUC) and visualized using distribution maps. This algal pathogen is most commonly found in tropical and subtropical areas with high humidity and warm temperatures; and in our study, the MaxEnt model accurately anticipated the algal parasite's potential distribution, suggesting its appropriateness for ecological niche modelling. The most relevant environmental variables were observed to be slope, Mean Diurnal Range (BIO2), Land Use and Land Cover (LULC), Precipitation seasonality (BIO15) and Annual precipitation (BIO12), providing insights into the species' ecological requirements and preferences. In addition, the analysis highlighted possible high-risk areas in Assam, West Bengal, Tamil Nadu, Kerala, Karnataka and Eastern and south western coastal lines of India for this algal parasite invasion, highlighting places that require immediate attention for prevention and management techniques. The results of this research will be beneficial in the management of ecological impacts of C. parasiticus Karst in India and other regions with similar environmental conditions. [ABSTRACT FROM AUTHOR]

Rubber (Hevea brasiliensis) is a priority tree crop that produces natural rubber (NR), making it an important plantation commodity in the Philippines. However, NR production is confronted with major constraints, including rubber diseases resulting in low latex yield. Twenty-five rubber farms located in five major rubber-producing municipalities (Kidapawan, Antipas, Makilala, Matalam and President Roxas) of Cotabato, Philippines, were surveyed for prevalence of major rubber leaf diseases. Information on farm practices and environmental variables was collected. The majority of rubber farmers were smallholders with hectarage planted ranging between 1 and 15 hectares. The most planted clones are RRIM 600 and PB 260, which are high-yielding yet susceptible to many foliar pathogens. Six leaf diseases, viz. Oidium powdery mildew, Colletotrichum leaf disease, Corynespora leaf fall/spot, Phytophthora leaf blight, bird's eye spot and algal spot, were documented in this study. Powdery mildew was the most prevalent in Cotabato with the highest percentage and severity of infections in all plantations, followed by Colletotrichum leaf disease. Information on disease prevalence in surveyed areas is important for disease management actions. [ABSTRACT FROM AUTHOR]

Camellia oleifera Abel, a woody oil plant, that is endemic to China. Tea oil, also referred to as “oriental olive oil,” is a superior quality plant-based cooking oil. The production of tea oil accounts for 8% of the total edible vegetable oil production in the country. Since 2022, the annual output value of C. oleifera industry has exceeded 100 billion yuan, making it one of the major economic contributors to China’s rural revitalization development strategy. In recent years, demand and production have grown in parallel. However, this has led to an increase in the incidence levels of pest and diseases. Pests and diseases significantly reduce the quality and yield of C. oleifera. C. oleifera diseases are mainly caused by pathogenic fungi. C. oleifera anthracnose, soft rot, leaf spot, coal stain, leaf gall disease, and root rot are the most important fungal diseases affecting the C. oleifera industry. However, the same disease may be caused by different pathogenic fungi. C. oleifera can be found in half of China and is found in several climatic zones. The geographical distribution of woody plant diseases is consistent with the distribution of the tree species and the ecology of the range, which also results in a highly complex distribution of fungal diseases of C. oleifera. The management of fungal diseases in C. oleifera is extremely challenging due to the variety of pathogenic fungal species, multiple routes of transmission, the lack of resistant plants, and the environmental safety of chemical measures. The optimal strategy for addressing fungal diseases in C. oleifera is to develop and apply an integrated disease management plan. This review provides a brief overview of the pathogenic species, pathogenesis, pathogenesis, geographical distribution, current management strategies, and potentially new methods of C. oleifera fungal diseases, to provide direction for the development of comprehensive management measures for C. oleifera fungal diseases in the future. [ABSTRACT FROM AUTHOR]

In order to realize the intelligent diagnosis of maize diseases with complicated backgrounds and similar disease spot characteristics in the real field environment, MFaster R-CNN is proposed by improving the Faster R-CNN algorithm. Firstly, a batch normalization processing layer is added to the convolution layer to speed up the convergence speed of the network and improve the generalization ability of the model; secondly, a central cost function is proposed to construct a mixed loss function to improve the detection accuracy of similar lesions; then, four kinds of pre-trained convolution structures are selected as the basic feature extraction network of Faster R-CNN for training, and the random gradient descent algorithm is used to optimize the training model to test the optimal feature extraction network; finally, the trained model is used to select test sets under different weather conditions for comparison, and MFaster R-CNN is compared with Faster R-CNN and SSD. The experimental results show that in MFaster R-CNN disease detection framework, VGG16 convolution layer structure as feature extraction network has better performance, the average recall rate is 0.9719, F1 is 0.9718, the overall average accuracy rate can reach 97.23%; compared with Faster R-CNN, MFaster R-CNN has an average accuracy of 0.0886 higher and a single image detection time of 0.139 s less; compared with the SSD, the average accuracy is 0.0425 higher, and the single image detection time is reduced by 0.018 s. Our method also provides a basis for timely and accurate prevention and control of maize diseases in the field. [ABSTRACT FROM AUTHOR]

The chlorophyte algae are a dominant group of photosynthetic eukaryotes. Although many are photoautotrophs, there are also mixotrophs, heterotrophs, and even parasites. The physical characteristics of green algae are also highly diverse, varying greatly in size, shape, and habitat. Given this morphological and trophic diversity, we postulated that diversity may also exist in the protein components controlling intracellular movement of material by vesicular transport. One such set is the multisubunit tethering complexes (MTCs)—components regulating cargo delivery. As they span endomembrane organelles and are well‐conserved across eukaryotes, MTCs should be a good proxy for assessing the evolutionary dynamics across the diversity of Chlorophyta. Our results reveal that while green algae carry a generally conserved and unduplicated complement of MTCs, some intriguing variation exists. Notably, we identified incomplete sets of TRAPPII, exocyst, and HOPS/CORVET components in all Mamiellophyceae, and what is more, not a single subunit of Dsl1 was found in Cymbomonas tetramitiformis. As the absence of Dsl1 has been correlated with having unusual peroxisomes, we searched for peroxisome biogenesis machinery, finding very few components in Cymbomonas, suggestive of peroxisome degeneration. Overall, we demonstrate conservation of MTCs across green algae, but with notable taxon‐specific losses suggestive of unusual endomembrane systems. [ABSTRACT FROM AUTHOR]

The knowledge of the red rust disease remains limited in Cameroon, with a view to developing a control method. This work consisted in studying red rust on some fruit species such as Annona muricata (soursop), Dacryodes edulis (safou), Psidium guajava (guava) and Theobroma cacao (cocoa). Diseased leaves were collected in the field in the Maham site, in west region of Cameroon. The symptomatology of disease (colour, number and diameter of lesions) was studied. Coefficient of variation (%) was calculated. The incidence and severity of the disease was assessed in the different orchards surveyed. The measurement of the different structures of the thallus (length, width of sporangia and sporangiophores) was carried out using a microscope with a micrometer. The disease is characterized by circular orange to orange-brown spots on the upper surfaces and rarely on the lower surface. Number of lesions, varied from 245-510 respectively with D. edulis and T cacao. Lesion diameters varied from 0.1-1 cm, 0.1-7 cm, 0.1-1.5 cm in safou (African pear), guava and soursop respectively. The length and width of sporangiophores varied from 280.5-714×10.2-25.5 µm for A. muricata, 408-612×15.3-25.5 µm for Dacryodes edulis, 433.3-663×15.3-20.4 µm for P. guajava and 484.5-612×20.4-35.7 µm for T. cacao. The number of sporangiophores varied from 1 to 11 at the maximum threshold. But, number of sporangia was the same in the four species, 1-9. The pathogenicity test was negative. The data measurements show that the specie observed is Cephaleurus virescens which is a parasitic alga. The data obtained are a basis for the development of an integrated control strategy against this emerging disease. [ABSTRACT FROM AUTHOR]

Cephaleuros is often known as an algal pathogen with 19 taxonomically valid species, some of which are responsible for red rust and algal spot diseases in vascular plants. No chloroplast genomes have yet been reported in this genus, and the limited genetic information is an obstacle to understanding the evolution of this genus. In this study, we sequenced six new Trentepohliales chloroplast genomes, including four Cephaleuros and two Trentepohlia. The chloroplast genomes of Trentepohliales are large compared to most green algae, ranging from 216 to 408 kbp. They encode between 93 and 98 genes and have a GC content of 26–36%. All new chloroplast genomes were circular-mapping and lacked a quadripartite structure, in contrast to the previously sequenced Trentepohlia odorata , which does have an inverted repeat. The duplicated trnD -GTC , petD , and atpA genes in C. karstenii may be remnants of the IR region and shed light on its reduction. Chloroplast genes of Trentepohliales show elevated rates of evolution, strong rearrangement dynamics and several genes display an alternative genetic code with reassignment of the UGA/UAG codon presumably coding for arginine. Our results present the first whole chloroplast genome of the genus Cephaleuros and enrich the chloroplast genome resources of Trentepohliales. [ABSTRACT FROM AUTHOR]

Guava is one of the most important fruits in Pakistan, and is gradually boosting the economy of Pakistan. Guava production can be interrupted due to different diseases, such as anthracnose, algal spot, fruit fly, styler end rot and canker. These diseases are usually detected and identified by visual observation, thus automatic detection is required to assist formers. In this research, a new technique was created to detect guava plant diseases using image processing techniques and computer vision. An automated system is developed to support farmers to identify major diseases in guava. We collected healthy and unhealthy images of different guava diseases from the field. Then image labeling was done with the help of an expert to differentiate between healthy and unhealthy fruit. The local binary pattern (LBP) was used for the extraction of features, and principal component analysis (PCA) was used for dimensionality reduction. Disease classificationwas carried out usingmultiple classifiers, including cubic support vector machine, Fine K-nearest neighbor (F-KNN), Bagged Tree and RUSBoosted Tree algorithms and achieved 100% accuracy for the diagnosis of fruit flies disease using Bagged Tree. However, the findings indicated that cubic support vector machines (C-SVM) was the best classifier for all guava disease mentioned in the dataset. [ABSTRACT FROM AUTHOR]

Algae are commonly referred as the aquatic algae and the existence of the terrestrial green algae is less portrayed. Trentepohliales is an order of filamentous green algae living terrestrially. These algae can be parasitic, epiphytic or free living organism. They are frequently found on parts of tropical plants and rocks. However, research regarding these organisms is still limited especially in Malaysia. Hence, the objectives of this study were to survey the distribution of the epiphytic green algae (Phycopeltis Millardet) on plant leaves in Royal Belum Rainforest, Perak and to recognise the genus and species of the algae. Leaves of forest plants (mainly shrubs) with green or brownish orange spots were collected and examined. Identification of the genus and species of the algae was done morphologically. In the present study, epiphytic green algae from the genus Phycopeltis were collected from 10 host plants. Among the identified species were P. amboinensis (Karsten) Printz, P. arundinacea (Mont.) de Toni, P. epiphyton Millardet, P. irregularis (Schmidle) Wille, P. flabellata R.H. Thompson & Wujek and P. treubii Karsten while P. amboinensis was the most common species. [ABSTRACT FROM AUTHOR]

Plant-parasitic green algae in the genus Cephaleuros infect leaves, twigs and fruits of numerous host plants worldwide. The reproductive structures of Cephaleuros are important in the infection process. The goal of this study was to determine the in vitro morphology and behavior of the zoospores and gametes of five Cephaleuros species: Cephaleuros karstenii, C. pilosa, C. solutus, C. virescens and Cephaleuros sp. Microscopic observations revealed that zoospores were ellipsoidal, rod-shaped, or spherical with four flagella. Gametes were spherical in shape with two flagella. Zoospores were released from all five Cephaleuros species, but gametes were released only by C. karstenii, C. solutus and Cephaleuros sp. After their release from gametangia, gametes swarmed in a water drop in irregular and circular motions until the resting stage; some gametes attached to each other, and others burst. Zoospores were released from papilla-pores located at the base of zoosporangia and swarmed in a water drop in irregular and circular motions. Some zoospores germinated and others burst, similar to the gametes. In this study, germinated zoospores formed germ tubes, and filaments containing carotenoid pigment, and then died without forming thalli. Keywords: germination, reproductive cells, subaerial algae, Trentepohliaceae, Trentepohliales, Green algae in the genus Cephaleuros Kunze ex E.M. Fries are plant parasites belonging to the order Trentepohliales, family Trentepoh liaceae (Guiry and Guiry 2017). They are subaerial algae that [...]

Abstract: Recent work on species composition, taxonomy, and diversity of plant-parasitic algae in the genus Cephaleuros in Thailand has provided additional knowledge of the parasitic algae in the country. The [...]