Hijacking phosphate signaling: A novel strategy of fungal pathogens in plant disease.

Agriculture crops are highly significant for the sustenance of human life and act as an essential source for national income development worldwide. Plant diseases and pests are considered one of the most imperative factors influencing food production, quality, and minimize losses in production. Farmers are currently facing difficulty in identifying various plant diseases and pests, which are important to prevent plant diseases effectively in a complicated environment. The recent development of deep learning techniques has found use in the diagnosis of plant diseases and pests, providing a robust tool with highly accurate results. In this context, this paper presents a comprehensive review of the literature that aims to identify the state of the art of the use of convolutional neural networks (CNNs) in the process of diagnosing and identification of plant pest and diseases. In addition, it presents some issues that are facing the models performance, and also indicates gaps that should be addressed in the future. In this regard, we review studies with various methods that addressed plant disease detection, dataset characteristics, the crops, and pathogens. Moreover, it discusses the commonly employed five-step methodology for plant disease recognition, involving data acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses various deep learning architecture-based solutions that have a faster convergence rate of plant disease recognition. From this review, it is possible to understand the innovative trends regarding the use of CNN’s algorithms in the plant diseases diagnosis and to recognize the gaps that need the attention of the research community.

Chapter 2 - Current status of plant diseases and food security

Lifestyles of Colletotrichum acutatum.

Alternaria causes pathogenic disease on various economically important crops having saprophytic to endophytic lifecycle. Pathogenic fungi of Alternaria species produce many primary and secondary metabolites (SMs). Alternaria species produce more than 70 mycotoxins. Several species of Alternaria produce various phytotoxins that are host-specific (HSTs) and non-host-specific (nHSTs). These toxins have various negative impacts on cell organelles including chloroplast, mitochondria, plasma membrane, nucleus, Golgi bodies, etc. Non-host-specific toxins such as tentoxin (TEN), Alternaric acid, alternariol (AOH), alternariol 9-monomethyl ether (AME), brefeldin A (dehydro-), Alternuene (ALT), Altertoxin-I, Altertoxin-II, Altertoxin-III, zinniol, tenuazonic acid (TeA), curvularin and alterotoxin (ATX) I, II, III are known toxins produced by Alternaria species. In other hand, Alternaria species produce numerous HSTs such as AK-, AF-, ACT-, AM-, AAL- and ACR-toxin, maculosin, destruxin A, B, etc. are host-specific and classified into different family groups. These mycotoxins are low molecular weight secondary metabolites with various chemical structures. All the HSTs have different mode of actions, biochemical reactions, and signaling mechanisms to causes diseases in the host plants. These HSTs have devastating effects on host plant tissues by affecting biochemical and genetic modifications. Host-specific mycotoxins such as AK-toxin, AF-toxin, and AC-toxin have the devastating effect on plants which causes DNA breakage, cytotoxic, apoptotic cell death, interrupting plant physiology by mitochondrial oxidative phosphorylation and affect membrane permeability. This article will elucidate an understanding of the disease mechanism caused by several Alternaria HSTs on host plants and also the pathways of the toxins and how they caused disease in plants.

Bean pod mottle virus: A Threat to U.S. Soybean Production.

Host–Pathogen interaction under favorable environmental variables results in disease syndrome followed by symptom production. There are different symptoms produced by plant pathogenic fungi namely necrosis, hypertrophy and hyperplasia and also hypotrophy and hypoplasia. Identification of the fungal pathogen and their classification is an important aspect to be dealt. Most of the pathogenic fungi are either obligate parasites or biotrophs. Around 13 million of fungi have been estimated and of which only 1,40,000 fungal species have been identified world over. India has got a record of 29,000 fungal species. Since 1/3 of global fungal biodiversity occurs in India, hence there is need to discover the fungi occurring in different ecological niches and also on crop plants. Around 30,000 plant pathogenic fungi have been reported in the world and of which 5000–7000 pathogenic fungi might have occurred on various crop plants and forest plants in India. Early detection of plant pathogenic fungi and diseases diagnosis are the important components that help in disease management. Morpho-taxonomy and molecular tools are employed in the identification of the plant pathogenic fungi. Establishment of relevant disease forecasting systems and models are important for early prediction of the outbreak of plant diseases. In India agriculture forms the backbone for the country’s economy besides offering food security and nutritional security to the growing population. The aspects and prospects related to biodiversity, taxonomy and plant disease diagnostics of plant pathogenic fungi from India are discussed.

This work is devoted to development of early diagnosis methods of fungal diseases in agricultural plants, which will increase effectiveness of protective measures against them. For today, there are many diagnosis methods of diseases and identifying fungal pathogens. These diagnosis methods of diseases allow the identification of fungal pathogens only after the beginning of development the pathological process. In such cases it is not able to protect the plants in time. Found that in the early stages of the disease, which is still impossible to identify visually, the first changes, are happening in the processes of photosynthesis and can be fixed. Chlorophyll fluorescence is the only indicator that allows studying the photochemical reactions in live objects that are connected with work of photosystem II (FS2) of Streptophyta plants. However, these methods will not allow identified of fungal diseases in agricultural plants. The aim of study is developing an express method for early sunflower fungal diseases diagnosis by modifying the methods for recording induction of chlorophyll fluorescence and fluorescence microscopy. Research objects were sunflower Helianthus annuus L. With the aim of identifying the state of photosynthetic apparatus are evaluated changes in the functioning of photosynthetic processes in sunflower leaves Helianthus annuus L. The investigation was conducted on fluorescent photoinduced indicators basis. Definition of fungal disease and assessment of its impact as a stress factor, they were conducted by comparison informative indicators of chlorophyll fluorescence induction of experimental plants. We investigated the interconnection between the changes of chlorophyll fluorescence induction parameters and the presence of fungal diseases in plants that didn`t have visual damage to viruses and diseases. In the process of performance work were conducted the expert research of chlorophyll fluorescence. The results of the study were monitored by light microscopy. In the process of performance work, we found that all investigated plants were characterized by a uniform emission of red fluorescence intensity. Absence of differences in the intensity of fluorescence doesn`t make it possible to determine the presence of fungal infection. With the aim of detecting hidden fungal infection. We have developed method of temperature influence on the plant leaves during the research. As a result, of the influence of temperatures on the leaves of individual plants were recorded flashes of yellow-green fluorescence. In these plants the fungal disease was detected visually by light microscopy in 12-18 days later and it was identified as Phomopsis helianthi M. The proposed modification of the method of photoinduction fluorescence in combination with the luminescent microscopy method have allowed to detect the presence of hidden fungal infection in annual sunflower at the earliest stages of its development. Perspective of the further development of this method for the purpose of early diagnosis of the presence of hidden infection in various agricultural plants. Keywords: annual sunflower, fungal diseases, diagnosis, fluorescence induction of chlorophyll, Phomopsis helianthi M.

ilamentous fungi of the genus Colletotrichum and its teleomorph Glomerella are considered major plant pathogens worldwide. They cause significant economic damage to crops in tropical, subtropical, and temperate regions. Cereals, legumes, ornamentals, vegetables, and fruit trees may be seriously affected by the pathogen (3). Although many cultivated fruit crops are infected by Colletotrichum species, the most significant economic losses are incurred when the fruiting stage is attacked. Colletotrichum species cause typical disease symptoms known as anthracnose, characterized by sunken necrotic tissue where orange conidial masses are produced. Anthracnose diseases appear in both developing and mature plant tissues (4). Two distinct types of diseases occur: those affecting developing fruit in the field (preharvest) and those damaging mature fruit during storage (postharvest). The ability to cause latent or quiescent infections has grouped Colletotrichum among the most important postharvest pathogens. Species of the pathogen appear predominantly on aboveground plant tissues; however, belowground organs, such as roots and tubers, may also be affected. In this article, we deal in particular with methods used to identify and characterize Colletotrichum species and genotypes from almond, avocado, and strawberry, as examples, using traditional and molecular tools. The three pathosystems chosen represent different disease patterns of fruitassociated Colletotrichum. Multiple Species on a Single Host Numerous cases have been reported in which several Colletotrichum species or biotypes are associated with a single host. For example, avocado and mango anthracnose, caused by both C. acutatum and C. gloeosporioides, affect fruit predominantly as postharvest diseases (25,40,41). Strawberry may be infected by three Colletotrichum species, C. fragariae, C. acutatum, and C. gloeosporioides, causing anthracnose of fruit and other plant parts (31). Almond and other deciduous fruits may be infected by C. acutatum or C. gloeosporioides (Table 1) (1,5,46,50). Citrus can be affected by four different Colletotrichum diseases (61): postbloom fruit drop and key lime anthracnose, both caused by C. acutatum, and shoot dieback and leaf spot, and postharvest fruit decay, both caused by C. gloeosporioides. Additional examples of hosts affected by multiple Colletotrichum species include coffee, cucurbits, pepper, and tomato. Single Species on Multiple Hosts It is common to find that a single botanical species of Colletotrichum infects multiple hosts. For example, C. gloeosporioides (Penz.) Penz. & Sacc. in Penz. (teleomorph: Glomerella cingulata (Stoneman) Spauld. & H. Schrenk), which is considered a cumulative species and forms the sexual stage in some instances, is found on a wide variety of fruits, including almond, avocado, apple, and strawberry (Table 2) (6,15,31,46). Likewise, C. acutatum J.H. Simmonds has been reported to infect a large number of fruit crops, including avocado, strawberry, almond, apple, and peach (1,5,16,25,27). Examples of other species with multiple host ranges include C. coccodes, C. capsici, and C. dematium (14,56).

PCR and qPCR are important methods for plant disease diagnosis and quantification of pathogen populations in host tissues or soil. For most plant diseases, DNA extracted from infected tissue or soil is a prerequisite for PCR or qPCR. The efficiency of DNA extraction should have a direct impact on the sensitivity of PCR and qPCR systems. In this study, plant pathogen DNA extraction efficiencies were evaluated based on three plant disease systems and three DNA extraction methods. DNA of bacterial (Pectobacterium atrosepticum), protist (Plasmodiophora brassicae) and fungal (Botrytis cinerea) pathogens was extracted and aliquots were prepared. Aliquots of the DNA were mixed with healthy tissues of the corresponding host plant and DNA was extracted again from the mixture. The resultant mixed DNA, as well as the original pathogen DNA, were analysed by qPCR to assess the quantities of the pathogen DNA. Extraction efficiency was calculated based on the qPCR Cq values of the mixed DNA and the original pathogen DNA for each extraction method on each pathogen. Our results indicated the pathogen DNA extraction efficiencies were generally less than, or near, 50%. This result called attention to the importance of: 1) including the DNA extraction efficiency in the evaluation and announcement of new qPCR diagnostic systems, and 2) developing and using high efficient DNA extraction methods in plant disease diagnosis and pathogen quantification.

Breaking the Stereotype: Virulence Factor–Mediated Protection of Host Cells in Bacterial Pathogenesis

Mango Anthracnose: Economic Impact and Current Options For Integrated Managaement.

Chapter 2 - Biocontrol: a novel eco-friendly mitigation strategy to manage plant diseases

Both theory and prior studies predict that climate warming should increase attack rates by herbivores and pathogens on plants. However, past work has often assumed that variation in abiotic conditions other than temperature (e.g. precipitation) do not alter warming responses of plant damage by natural enemies. Studies over short time periods span low variation in weather, and studies over long time‐scales often neglect to account for fine‐scale weather conditions. Here, we used a 20+ year warming experiment to investigate if warming affects on herbivory and pathogen disease are dependent on variation in ambient weather observed over 3 years. We studied three common grass species in a subalpine meadow in the Colorado Rocky Mountains, USA. We visually estimated herbivory and disease every 2 weeks during the growing season and evaluated weather conditions during the previous 2‐ or 4‐week time interval (2‐week average air temperature, 2‐ and 4‐week cumulative precipitation) as predictors of the probability and amount of damage. Herbivore attack was 13% more likely and damage amount was 29% greater in warmed plots than controls across the focal species but warming treatment had little affect on plant disease. Herbivory presence and damage increased the most with experimental warming when preceded by wetter, rather than drier, fine‐scale weather, but preceding ambient temperature did not strongly interact with elevated warming to influence herbivory. Disease presence and amount increased, on average, with warmer weather and more precipitation regardless of warming. Synthesis. The effect of warming over reference climate on herbivore damage is dependent on and amplified by fine‐scale weather variation, suggesting more boom‐and‐bust damage dynamics with increasing climate variability. However, the mean effect of regional climate change is likely reduced monsoon rainfall, for which we predict a reduction in insect herbivore damage. Plant disease was generally unresponsive to warming, which may be a consequence of our coarse disease estimates that did not track specific pathogen species or guilds. The results point towards temperature as an important but not sufficient determinant and regulator of species interactions, where precipitation and other constraints may determine the affect of warming.

The early stage pathogens of plant diseases have the characteristic of low concentration and difficult detection, which exacerbates the difficulty of tracing the disease, leading to rapid spread and difficulty in effective control. Currently, common plant disease detection techniques such as imaging and spectroscopy can only be applied after the occurrence and manifestation of diseases, and it is difficult to accurately locate the source of disease outbreaks during spore germination or propagation stages. Therefore, this paper proposes a method for locating the source of airborne plant diseases based on the non-local-interpolation algorithm. Firstly, a highly sensitive concentration sensor was designed based on Mie scattering theory to accurately count spores in plant diseases, and a multi-sensor collaborative computing network model was constructed. Secondly, by collecting spore quantity data at different locations, a particle diffusion model is established to summarize the propagation patterns of particles under specific regional conditions. Finally, a non-local-interpolation algorithm coupled with improved power-law equations was designed for precise localization of airborne plant disease sources under different wind and direction conditions. The experimental results in the greenhouse show that the maximum error of light scattering counting does not exceed 10%; Under windless and windy conditions, our method achieved localization accuracies of 94.7% and 92.9%, respectively, with approximately three nodes per square meter. This provides new ideas and insights for early diagnosis and precise localization of plant diseases.

Direct identification of fungi associated with indoor and outdoor ornamental plants by utilizing PCR assay