Plant Diseases Research Articles

Research progress of Bacillus velezensis in plant disease resistance and growth promotion

Bacillus velezensis has a wide range of beneficial activities, such as fighting plant diseases, promoting growth, improving crops’ ability to handle stress, and boosting plant defenses. These traits make it a strong candidate for agricultural use, especially as a biocontrol agent and growth-promoting bacteria. This review takes a close look at the origins, working mechanisms, and potential agricultural benefits of B. velezensis. Both lab and field studies have shown its success in reducing plant diseases and supporting crop growth by producing helpful compounds, taking up space in the environment, and strengthening plant defenses. Although B. velezensis is already used in some fertilizers and biopesticides, there are still challenges in scaling up production, selecting the right strains, and ensuring product stability. This review points out current research gaps and suggests future directions, such as improving strain selection, developing better biofertilizers, and advancing production techniques to make the most of B. velezensis in agriculture. These findings aim to guide further research and improve its use in sustainable farming.

RNA interference: a promising biotechnological approach to combat plant pathogens, mechanism and future prospects.

Plant pathogens and other biological pests represent significant obstacles to crop Protection worldwide. Even though there are many effective conventional methods for controlling plant diseases, new methods that are also effective, environmentally safe, and cost-effective are required. While plant breeding has traditionally been used to manipulate the plant genome to develop resistant cultivars for controlling plant diseases, the emergence of genetic engineering has introduced a completely new approach to render plants resistant to bacteria, nematodes, fungi, and viruses. The RNA interference (RNAi) approach has recently emerged as a potentially useful tool for mitigating the inherent risks associated with the development of conventional transgenics. These risks include the use of specific transgenes, gene control sequences, or marker genes. Utilizing RNAi to silence certain genes is a promising solution to this dilemma as disease-resistant transgenic plants can be generated within a legislative structure. Recent investigations have shown that using target double stranded RNAs via an effective vector system can produce significant silencing effects. Both dsRNA-containing crop sprays and transgenic plants carrying RNAi vectors have proven effective in controlling plant diseases that threaten commercially significant crop species. This article discusses the methods and applications of the most recent RNAi technology for reducing plant diseases to ensure sustainable agricultural yields.

Regulation of maize growth and immunity by ZmSKI3-mediated RNA decay and post-transcriptional gene silencing.

Disease resistance is often associated with compromised plant growth and yield due to defense-growth tradeoffs. However, key components and mechanisms underlying the defense-growth tradeoffs are rarely explored in maize. In this study, we find that ZmSKI3, a putative subunit of the SUPERKILLER (SKI) complex that mediates the 3'-5' degradation of RNA, regulates both plant development and disease resistance in maize. The Zmski3 mutants showed retarded plant growth and constitutively activated defense responses, while the ZmSKI3 overexpression lines are more susceptible to Curvularia lunata and Bipolaris maydis. Consistently, the expression of defense-related genes was generally up-regulated, while expressions of growth-related genes were mostly down-regulated in leaves of the Zmski3-1 mutant compared to that of wild type. In addition, 223 differentially expressed genes that are up-regulated in Zmski3-1 mutant but down-regulated in the ZmSKI3 overexpression line are identified as potential target genes of ZmSKI3. Moreover, small interfering RNAs targeting the transcripts of the defense- and growth-related genes are differentially accumulated, likely to combat the increase of defense-related transcripts but decrease of growth-related transcripts in Zmski3-1 mutant. Taken together, our study indicates that plant growth and immunity could be regulated by both ZmSKI3-mediated RNA decay and post-transcriptional gene silencing in maize.

Volatile Organic Compounds Produced by Trichoderma asperellum with Antifungal Properties against Colletotrichum acutatum.

Managing plant diseases caused by phytopathogenic fungi, such as anthracnose caused by Colletotrichum species, is challenging. Different methods have been used to identify compounds with antibiotic properties. Trichoderma strains are a source of novel molecules with antifungal properties, including volatile organic compounds (VOCs), whose production is influenced by the nutrient content of the medium. In this study, we assessed the VOCs produced in dual confrontation systems performed in two culture media by Trichoderma strains (T. atroviride IMI206040, T. asperellum T1 and T3, and Trichoderma sp. T2) on Colletotrichum acutatum. We analysed the VOC profiles using gas chromatography coupled with mass spectrometry. The Luria Bertani (LB) medium stimulated the production of VOCs with antifungal properties in most systems. We identified 2-pentyl furan, dimethyl disulfide, and α-phellandrene and determined their antifungal activity in vitro. The equimolar mixture of those VOCs (250 µM ea.) resulted in 14% C. acutatum diametral growth inhibition. The infective ability and disease severity caused by the mycelia exposed to the VOCs mixture were notably diminished in strawberry leaves. Application of these VOCs as biofumigants may contribute to the management of anthracnose. LB represents a feasible strategy for identifying novel VOCs produced by Trichoderma strains with antifungal properties.

Biological Control of Microbial Pectinolytic Plant Pathogens Causing Soft Rot of Fruits and Vegetables.

It is crucial to implement appropriate measures to prevent the spread of plant pathogens that lead to the decay of fruits and vegetables. From this perspective, we evaluated the biocontrol potential of five Bacillus-plant growth promoting rhizobacteria (PGPR) strains against twenty-one pectinolytic phytopathogens causing soft rot in fruits and vegetables. These phytopathogens had been previously studied. Three in vitro methods were utilized to accomplish this objective: competition, extraction of bioactive substances, and direct confrontation. The inhibitory effect of the direct confrontation method resulted in a slower growth of 11 microbial plant pathogens. In addition, it was noted that 11 strains of plant pathogens generated inhibitory constituents, while 15 plant pathogens produced inducible inhibitory substances. Furthermore, volatile inhibitory compounds were detected in the six tested strains. Overall, strains of PGPR-Bacillus demonstrated strong antifungal and antibacterial properties against phytopathogens. These PGPR can be regarded as potential biocontrol agents for soft microbial rot in fruits and vegetables as well as producers of substances that effectively suppress plant diseases.

Evaluation of Agronomic Performance and Resistance to Cercospora (Cercospora sojina) Leaf Spot of Selected Soya bean Genotypes in Western Tigray, Ethiopia

Aim of the current study was to evaluate soya bean genotypes for yield and yield traits and cercospora (Cercosporasojina) leaf spot disease under field condition. Field experiment was conducted at Humera district during 2018 cropping season using 16soya bean genotypes. Treatments were evaluated with RCBD and replicated three times. Number of days from planting to days of flowering and days of maturity, number of pod per plant, pod shattering, seed yield and cercospora leaf spot disease were showed a significant (P<0.01) variation among genotypes. All varieties were moderately resistance to pod shattering except PN13-1 (3.1) was moderately susceptible and Parc-3, Parc-5 and PN13-10were resistance. Highest yields were recorded from genotypes PN13-2 at 20.32q/ha and PN13-9 at 19.77q/ha. On the other hand, the lowest yields were recorded from genotypes PN13-3 at 7.49q/ha and PN13-1 at 9.95q/ha. Genotypes PN13-2 and PN13-7 have been identified as displaying resistance to Cercospora leaf spot. Highest disease severity recorded in genotypes Awassa 04 and PN13-1. Further research is needed in future to cover a wide range of environments and genotypes to screen high yielder and cercospora leaf spot disease resistance.

Leveraging machine learning to discriminate wheat scab infection levels through hyperspectral reflectance and feature selection methods

Real-time or pre-symptomatic wheat scab (WS) detection is inevitable for precision agriculture to secure yield and quality at the critical grain formation stage. For this, feature selection (FS) techniques and machine learning (ML) have demonstrated their capabilities. However, for the same type and size of dataset, all FS and ML techniques behave differently due to their diverse primary constituents. This study attempts to leverage ML for WS classification and prediction employing different FS techniques on hyperspectral data of wheat spikes. The spectral features were selected and assessed to regress and classify disease occurrence. Relief-F-neural net (NN) manifested the best results with classification accuracy (CA) of 67 % and 89 % at the pre-symptomatic scale and 3 days after inoculation (DAI), respectively. Followed by continuous wavelet transform (CWT)-NN with 63 % CA at the pre-symptomatic scale and CWT-Xgboost with 89 % CA at 3DAI. For prediction, random forest regression revealed best accuracy of R2 = 0.94 and RMSE = 7.70, followed by partial least squares regression with R2 = 0.90 and RMSE = 10.37. The results offer a precise quantitative benchmark for future investigations into the capacity of hyperspectral data and FS for the real-time quantification of plant diseases.

Callose and Salicylic Acid Are Key Determinants of Strigolactone-Mediated Disease Resistance in Arabidopsis

Research has demonstrated that strigolactones (SLs) mediate plant disease resistance; however, the basal mechanism is unclear. Here, we provide key genetic evidence supporting how SLs mediate plant disease resistance. Exogenous application of the SL analog, rac-GR24, increased Arabidopsis thaliana resistance to virulent Pseudomonas syringae. SL-biosynthetic mutants and overexpression lines of more axillary growth 1 (MAX1, an SL-biosynthetic gene) enhanced and reduced bacterial susceptibility, respectively. In addition, rac-GR24 promoted bacterial pattern flg22-induced callose deposition and hydrogen peroxide production. SL-biosynthetic mutants displayed reduced callose deposition but not hydrogen peroxide production under flg22 treatment. Moreover, rac-GR24 did not affect avirulent effector-induced cell death between Col-0 and SL-biosynthetic mutants. Furthermore, rac-GR24 increased the free salicylic acid (SA) content and significantly promoted the expression of pathogenesis-related gene 1 related to SA signaling. Importantly, rac-GR24- and MAX1-induced bacterial resistance disappeared completely in Arabidopsis plants lacking both callose synthase and SA. Taken together, our data revealed that callose and SA are two important determinants in SL-mediated plant disease resistance, at least in Arabidopsis.

Agriculture Robot Using Image Processing

Background: Agriculture is a cornerstone of the economy, but traditional farming is labor-intensive and time-consuming. Advances in technology, such as Convolutional Neural Networks (CNNs) and robotics, can help address challenges like disease detection, weed control, and labor shortages. Tomatoes, a key crop, benefit from proper management using these innovations. Objective: This project aims to design and develop an agriculture robot using IoT and Machine Learning to automate farming tasks, detect tomato plant diseases, and manage weeds more efficiently. Methods: The robot is controlled by an ESP32 microcontroller connected via Wi-Fi for real-time monitoring. The system uses CNNs for disease detection in tomato plants, along with a custom-trained dataset using the TensorFlow framework. Mechanical arms perform tasks like seeding, watering, spraying fertilizers, and harvesting. A user interface for disease detection was designed for real time disease detection. Results: The model achieved an accuracy of 95.0% for disease detection, 95.56% for fruit detection, and 95.14% for plant detection. These results highlight the effectiveness of the robot in managing tomato crops. Conclusion: The agriculture robot provides a comprehensive solution to key farming challenges, enhancing efficiency through automation and improving crop health monitoring. The integration of IoT and ML offers a pathway toward sustainable and profitable agriculture, particularly in managing tomatoes. Further improvements and scaling could expand its application to other crops and farming tasks. Keywords: Machine Learning, Internet of Things, Agricultural Robot, Disease Detection, Convolutional Neural Networks, Mechanical Tasks.

A Review on ML and DL Techniques in Detecting Plant Diseases

In Our country, Agriculture is the main occupation of the people. Plant diseases are a major problem today as they affect the quality and production of agricultural production. Most plant-borne diseases are caused by viruses and fungi. Identifying the diseases in early stage by manual power is not possible in large sector of Area. Subsequent maintenance of plants conditions are major challenging task. To overcome this problem in Agriculture, we use Image Processing Technique to identify the disease in beginning stage . To Apply Image Processing Technique, we have to undergo certain Image concepts that deals with image acquisition, image pre-processing, image segmentation, feature extraction and classification of disease. This Methodology reduces the destruction of plants and make the crop production high. The digital Image Processing Technique is the main solution to solve the problems in Plant diseases by identifying the plant diseases in early stage. It is very much useful to the Farmers who are facing plant diseases problem in their Agriculture Area. Many techniques are used to identify the diseases in plants using classifiers such as , K-Nearest Neighbors ,Support Vector Machine methods etc. This paper gives the overview of available methods for plant disease detection

EQID: Entangled quantum image descriptor an approach for early plant disease detection

In present day agriculture, early and accurate identification of plant diseases is essential for prompt response, which protects crop quality and output. This paper presents the Entangled Quantum-Inspired Deep learning model (EQID), a unique method that improves feature representation and classification in plant disease prediction by utilizing the concepts of quantum computing. Two different datasets with images of potatoes and tomatoes as leaves were used to test the EQID model, which performed better than traditional models. EQID obtained 98.96% accuracy, 98.98% precision, 98.96% recall, and 98.90% F1 score on images of potato leaves. For tomato leaves, comparable outcomes were noted, with accuracy, precision, recall, and F1 score all above 99.61%. The accuracy of disease prediction is greatly increased by the efficient and effective feature representation made possible by the EQID model's inclusion of quantum computing techniques. Additionally, the model outperformed other cutting-edge models such as DenseNet-121, VGGNet 16, and Xception Net, illustrating the potentially revolutionary effects of quantum-inspired models in agriculture. Future work will focus on applying the EQID model to a broader range of crops and plant diseases, as well as incorporating additional data sources to further enhance the model's predictive capabilities.

Identifying Crop Distress and Stress-Induced Plant Diseases Using Hyper Spectral Image Analysis

The purpose of this study is to determine whether hyper spectral images can be used to identify plant diseases and crop pressure from aerial photographs. With extensive research on the prevalent methods used closer to the problem, this study offers a potent strategy for identifying crop distress and illnesses using this efficient imaging technique. To identify the spectral fingerprints of common indications and symptoms of plant diseases and crop strains, this study evaluates the available hyper spectral photo datasets. After that, the data are examined using two learning algorithms—the highly randomized trees and the Random Woodland set of rules—to create predictions that are entirely dependent on the results that are discovered. In the end, a benchmarked set of test statistics is used to assess the prediction accuracy. The results of this study show that hyper spectral photo evaluation has a strong and promising utility for crop stress and disease identification. Hyper spectral light evaluation is a method for identifying plant diseases caused by strain on crops. By gathering and evaluating high-dimensional spectral reflection data from satellite or aircraft structures, details regarding the physiological homes of flowers can be identified. The health of plants, illnesses brought on by stress, and agricultural productivity predictions can all be made using these facts. Hyper spectral recordings can also be used to create actions that reduce agricultural losses and enhance the health of plants that are prone to disease.

Variations of pest and disease attacks on horticultural and non-horticultural crops in South Sumatra

Horticultural crops of olericultural types that were given a lot of chemical pesticides cause many negative impacts such as the emergence of pests and pathogens that were immune to pesticides, the emergence of new pests, increased pest populations and secondary pathogens, reduced populations of beneficial insects (predators). The study aimed to consider the use of pesticides by farmers to eradicate pests and diseases in horticulture and non-horticulture in South Sumatra. The research was conducted in South Sumatra Province. The research was conducted in 7 districts and 1 city, namely Indralaya District, North Indralaya District, Air Kumbang District, South Pemulutan District, Gelumbang District, Belitang Mulya District, Tanjung Batu District, and Palembang City, which was conducted from May to September 2022. Data were collected using the Secondary Variables method with Purposive sampling technique and processed into analyzed data. The results of this study showed that the most horticulture cultivated by farmers was eggplant(Solanum melongena L.). While for non-horticulture the most widely cultivated by farmers was corn(Zea mays). The percentage of pest attacks on non-horticultural land was 57.36%, the percentage of pest attacks on horticultural land was 68.28%. Variations in pest and disease attacks that occur in 180 farms on horticultural and non-horticultural crops in South Sumatra. Horticultural crops that are widely cultivated in South Sumatra include cucumber, long beans, and eggplant. Meanwhile, non-horticultural crops are corn, rice, and rubber. Spodoptera frugiperda was a pest that has been found to damage corn crops and Leptocorisa sp species that damage food crops (No Horticulture). Counseling was provided to farmers to find out the types of pests that could attack and the symptoms and diseases in plants to know how to control them.

Aspiration From Dysphagia Associated With the Presence of Pantoea agglomerans in a Horse

Pantoea agglomerans is a gram-negative bacterium that usually lives in symbiosis in soil and plants. However, it may also lead to serious diseases in plants, animals and humans. We describe a case of aspiration from dysphagia associated with the isolation of P. agglomerans in a horse admitted to an Italian hospital. The patient was presented to the hospital for the onset of chronic cough after an acute episode of pulmonary hemorrhage during a race recorded two months before and was treated with dexamethasone and penicillin. The cough was accentuated during meals, leading to a suspicion of aspiration from dysphagia. A bronchoalveolar lavage was collected, and cytological examination showed the presence of vegetable fibers, rod bacteria, and a high macrophage population (89%). From the tracheal wash collected for bacteriological culture, P. agglomerans was isolated in monoculture, and the antimicrobial susceptibility test revealed that the isolate was susceptible to all tested antimicrobials except sulfonamide. The horse recovered well after 10 days of enrofloxacin treatment. The present report highlights the role of P. agglomerans as an opportunistic respiratory pathogen in horses, especially in the presence of plants or their parts.

Stimuli-responsive sulfur-containing amino acid functionalized boron nitride nanosheets as nanocarriers via improved adhesion to enhance antibacterial performance against plant diseases

Bacterial wilt caused by Ralstonia solanacearum severely impacts agricultural production, urgently requiring effective control measures. Herein, we designed a strategy via boron nitride nanosheets with enhanced adhesion ability as nanocarriers to improve the antibacterial efficacy of active ingredients. To achieve this, we utilized three sulfur-containing amino acids (SAAs), including methionine (MET), cysteine (CYS), and cystine (CYST) for the simultaneously exfoliation and functionalization of boron nitride nanosheets (BNNS) through mechanochemical method, and compared their structure–activity relationship. The obtained BNNS-SAAs exhibited size around 160–250 nm and thickness near 1.5–2.0 nm. The presence of disulfide bond (S-S) within BNNS-CYS and BNNS-CYST promoted them with the responsiveness by redox stimuli. BNNS-CYST acquired highest functionalization degree of 63.44 % among three BNNS-SAAs. And tea tree essential oil (TTO) loading capacity within BNNS-SAAs was around 36.40–37.11 %. The existence of SAAs within BNNS significantly inhibited the volatilization of TTO via nanoconfinement effect fitting to Logistic model, specially, BNNS-CYST held with slowest release rate among the BN-based carriers, due to the stronger attachment or affinity. Furthermore, BNNS-CYST-TTO exhibited the most excellent synergistic antibacterial effect with the increased inhibition zone diameter by 26.89 % against Ralstonia solanacearum in comparison with pure TTO, with the minimum inhibition concentration reduced from 1.8 mg·mL−1 to 0.6 mg·mL−1. The enhanced antibacterial mechanism was proposed based on physical disruption via strong adhesion. BNNS-SAAs also presented superior adhesion and wettability on seed and foliar surfaces, and also promoted seed germination by providing more S nutrients, and exhibited no significant cytotoxicity. Our system provides a green and effective platform response to redox stimuli to load and release essential oils for application in bacterial wilt control.

Design of a portable bioassay system based on highly efficent nanozyme and Au-DNA nanocluster: Toward current/colorimetric dual-signal detection of sugarcane smut

Sugarcane smut is a major agricultural pest affecting sugarcane yield and quality, posing a significant threat to the sustainable and healthy development of the global sugar industry. The best strategy for controlling Sugarcane smut is early and precise diagnosis. An intelligent mobile bio-platform is constructed based on Au-Co/Zn ZIF nanozymes and Au-DNA nanoclusters for current/colorimetric dual signal detection of the sugarcane smut pathogen. Efficient and specific cycling of the specific gene fragment (bE4′) for Sugarcane smut is achieved by strand displacement reaction. The output strand then forms Au-DNA nanoclusters with AuNPs-DNA, and the bioconjugate is captured by H2 probe, which results in the formation of more double-stranded structures, causing methylene blue (MB) to adsorb on electrode. The anodic nanozyme efficiently catalyzes the oxidation of glucose, generating a large number of electrons, and MB in the system is therefore reduced to MBH, causing a color change. This results in distinct current/colorimetric dual output signals, significantly enhancing the accuracy. A capacitor is further coupled with the system to improve the sensitivity. The detection signals are read in real-time by a smartphone. The developed method for sugarcane smut genes shows good linearity within a concentration range of 0.0001–10000 pM, with a detection limit as low as 23.59 aM (S/N = 3). This work integrates advanced technologies from various fields to create a portable sensing device with extremely high sensitivity and precision for detecting sugarcane smut. It provides a reliable new option for early and precise diagnosis of the disease and offers valuable theoretical and technical insights for early prevention, intelligent detection, and real-time monitoring of other plant diseases.

Plant Leaf Disease Detection System using Machine Learning and Deep Learning: A Survey

Plant diseases are becoming more common, which is a serious threat to profitability of agriculture worldwide and requires the creation of reliable and efficient disease detection technologies. This work uses machine learning (ML) and deep learning (DL) approaches to give an extensive assessment of previous research on plant leaf disease identification. We examine and evaluate the many approaches used in the last several research, emphasizing the advantages, disadvantages, and useful uses of each. The study covers both sophisticated deep learning designs, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), as well as more conventional machine learning techniques, such support vector machines (SVM) and decision trees. According to our research, plant leaf diseases can be more accurately identified and classified using deep learning techniques, especially CNNs, when applied to a variety of crop categories. The study also looks at the field’s obstacles and promising directions, highlighting the use of multifaceted approaches and real-time illness monitoring systems.

Plant Leaf Disease Detection System Using Machine Learning And Deep Learning: A Survey

Plant diseases are becoming more common, which is a serious threat to profitability of agriculture worldwide and requires the creation of reliable and efficient disease detection technologies. This work uses machine learning (ML) and deep learning (DL) approaches to give an extensive assessment of previous research on plant leaf disease identification. We examine and evaluate the many approaches used in the last several research, emphasizing the advantages, disadvantages, and useful uses of each. The study covers both sophisticated deep learning designs, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), as well as more conventional machine learning techniques, such support vector machines (SVM) and decision trees. According to our research, plant leaf diseases can be more accurately identified and classified using deep learning techniques, especially CNNs, when applied to a variety of crop categories. The study also looks at the field’s obstacles and promising directions, highlighting the use of multifaceted approaches and real-time illness monitoring systems.

The mango bacterial black spot altered the endophyte community structure and diversity

Mango bacterial black spot is a major disease limiting mango production, which is now causing increasingly severe economic losses. In this study, we analyzed the differences in the composition and structure of bacterial and fungal communities in the pulp and leaves between the healthy mangoes of variety Kate and those affected by bacterial black spot, and attempted to explore potential biocontrol microorganisms for mangoes. The results showed there existed significant differences in microbial communities, the bacterial Stenotrophomonas, Curtobacterium, Massilia and fungous Penicillium, Alternaria, Aureobasidiu showed great abundance both in pulps and leaves. Some potential pathogenic bacteria, such as Pseudomonas, Xanthomonas, and Burkholderia, were also significantly enriched in the infected groups. In both the infected and healthy groups, the overall community structure of endophytic bacteria and fungi within the same organs was more similar, indicating that the composition of endophytes is organ-specific. After infection, the abundance of the potential probiotic bacterium class Paenibacillus was significantly increased both in leaves and pulp, suggesting that the pathogen invasion stimulated the defense systems of endophytes. Presumably, these Paenibacillus might be developed as defense bacteria for black spot as well as other plant diseases.

Microbial Biopesticides: An Ecofriendly Plant Protection Measures

Microbial pesticides possess active microbes capable of controlling plant pests in agriculture, horticulture, and forests. Microbes benefit plants through metagenomics, metabolomics, and strain enhancement efforts, keeping the loss below the economic threshold. The study looked at over 50 years of literature from various sources. Other investigations combined the factors used in this review study. They are biologically effective in controlling plant disease and insect pests. Since the modern agroecosystem depends more on chemical-based pesticides for pest control, microbial biopesticides are gaining popularity in terms of their natural, eco-friendly, and cost-effectiveness. Furthermore, the higher demand for organic food products further propels the future market for microbial pesticides. This review updates the mechanisms of controlling insect pests and plant diseases through biological control using biopesticides.