Diseases Of Crop Plants Research Articles

Detection and Classification of Plant Disease with Deep Learning

Abstract: Deep learning is a branch of artificial intelligence. In recent years, with the benefits of automatic learning and feature extraction, it's been wide involved by educational and industrial circles. It has been wide utilized in image and video processing, voice processing, and natural language processing. At a similar time, it's conjointly become an enquiry hotspot within the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. the application of deep learning in disease recognition will avoid the disadvantages caused by artificial choice of illness spot options, make plant disease feature extraction additional objective, and improve the analysis potency and technology transformation speed. This paper provides the analysis progress of deep learning technology within the field of crop plant disease identification in recent years. during this paper, we tend to present this trends and challenges for the detection of plant leaf disease with deep learning and advanced imaging techniques. we tend to hope that this work are going to be a valuable resource for researchers UN agency study the detection of plant diseases and bug pests. At a similar time, we tend to conjointly mentioned some of the challenges and issues that require to be resolved. Keywords: Deep Learning, Disease Detection, Classification, Artificial Intelligence, Leaf Disease Detection;

Pseudomonas fluorescens VSMKU3054 mediated induced systemic resistance in tomato against Ralstonia solanacearum

Plant growth-promoting rhizobacteria (PGPR) are potential bio agents for the suppression of various soil-borne diseases in crop plants and considered as viable alternative to chemical pesticides. The selected Pseudomonas fluorescens isolate VSMKU3054 is an effective biological control inoculum for the suppression of wilt disease of tomato incited by Ralstonia solanacearum by inducing plant defense enzymes viz., peroxidase (POX), polyphenol oxidase (PPO), lipoxygenase (LOX), phenylalanine ammonia-lyase (PAL) and total phenol content. The highest level of defense enzymes viz., POX, PAL, PPO, LOX and total phenol contents were observed in tomato seedlings treated with P. fluorescens at 24h, 36h, 36h, 48h and 24h after challenge inoculation of R. solanacearum, respectively. Whereas application of P. fluorescens alone in tomato plants also brought about induction of higher levels of POX, PAL, LOX and total phenol contents, and a moderate level of PPO activity compared to pathogen inoculated plants and control plants. The maximum induction of four prominent PPO isoforms was observed in tomato plants upon the treatment of P. fluorescens VSMKU3054 and challenged with R. solanacearum. Thus, this study showed that induction of defense-related genes by P. fluorescens plays an important role against R. solanacearum.

Multiple Species of Asteraceae Plants Are Susceptible to Root Infection by the Necrotrophic Fungal Pathogen Sclerotinia sclerotiorum.

The necrotrophic fungal pathogen Sclerotinia sclerotiorum can cause disease on numerous plant species, including many important crops. Most S. sclerotiorum-incited diseases of crop plants are initiated by airborne ascospores produced when fungal sclerotia germinate to form spore-bearing apothecia. However, basal stalk rot of sunflower occurs when S. sclerotiorum sclerotia germinate to form mycelia within the soil, which subsequently invade sunflower roots. To determine whether other plant species in the Asteraceae family are susceptible to root infection by S. sclerotiorum, cultivated sunflower (Helianthus annuus L.) and seven other Asteraceae species were evaluated for S. sclerotiorum root infection by inoculation with either sclerotia or mycelial inoculum. Additionally, root susceptibility of sunflower was compared with that of dry edible bean and canola, two plant species susceptible to S. sclerotiorum but not known to display root-initiated infections. Results indicated that multiple Asteraceae family plants are susceptible to S. sclerotiorum root infection after inoculation with either sclerotia or mycelium. These observations expand the range of plant hosts susceptible to S. sclerotiorum root infection, elucidate differences in root inoculation methodology, and emphasize the importance of soilborne infection to Asteraceae crop and weed species.

Internet of Things (IoT) and Machine Learning Model of Plant Disease Prediction–Blister Blight for Tea Plant

Crop plant diseases are a significant threat to productivity and sustainable development in agriculture. Early prediction of disease attacks is useful for the effective control of the disease by taking proactive actions against their attacks. Modern Information and Communication Technologies (ICTs) have a predominant role in Precision Agriculture (PA) applications to support sustainable developments. There is an immense need for solutions for the early prediction of the disease attack for proactive control against the plant disease attack. The present solution of disease detection using the computer vision approach can only detect the existence of the disease once the disease has already appeared. This study aims to propose a Machine Learning (ML) approach for the early prediction of the probability of disease attack based on Internet of Things (IoT) directly sensed crop field environmental conditions. Plant disease life cycles are strongly correlated with environmental conditions. The crop field environmental conditions are used to predict the occurrence of plant diseases. The Multiple Linear Regression (MLR) is applied as the ML model due to the existence of a linear relationship between disease attack and environmental conditions. Internet of Things (IoT) based crop field environmental conditions help to accurately predict the occurrence of plant diseases using the ML approach. The proposed model is implemented for the prediction of blister blight ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Exobasidium vexans</i> ) for tea ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Camellia sinensis</i> ) plant to check the effectiveness of the proposed solution. The implementation of the proposed model from 2015 to 2019 reveals that the accuracy of prediction of occurrence of the disease reached up to 91% in 2019.

ОПТ ИМИЗАЦИЯ МОЛЕКУЛЯРНО-ГЕНЕТИЧЕСКИХ МЕТОДОВ ДИАГНОСТИКИ ГРИБОВ РОДА SCLEROTINIA

Phytopathogenic fungi are the causative agent for disruptive crop plant diseases, besides causing significant losses in crop yield annually. Sclerotinia is one of the most common and dangerous pathogens. Species of the Sclerotinia genus cause widespread disease across a broad range of economically important agricultural crops. Regular screening of Sclerotinia in plant material is necessary due to wide host-plants range and absence of resistance to this phytopathogen. The purpose of this study was to test the developed systems of primers and probes to identify the most dangerous species of Sclerotinia genus, such as S. sclerotiorum, S. nivalis, S. borealis and S. minor. This system has been tested on positive samples of fungal cultures. Screening study and determination of the infection level with S. borealis were carried out on winter wheat. The material for the study was 24 samples of fungi belonging to the genus Sclerotinia obtained from Syngenta LLC and 37 winter wheat samples from different places of growth. Species identification of fungal cultures was carried out by Sanger sequencing using the developed pairs of primers for the β-tubulin (tub) gene region and a fragment of the rRNA gene cluster. Additionally, oligonucleotides were designed for the identification of S. borealis by real-time polymerase chain reaction (qPCR). In determining the analytical characteristics of the kit, the absence of false positive and false negative results was demonstrated. Screening studies of 37 winter wheat samples showed the presence of nucleic acids of the studied pathogen in 24.3% among the analyzed samples. There were no severely infected samples among the tested, which indicates that the pathogen is in the initial stage of its development.

Long-Term Efficacy and Safety of RNAi-Mediated Virus Resistance in 'HoneySweet' Plum.

Interfering RNA technology has been established as an effective strategy to protect plants against viral infection. Despite this success, interfering RNA (RNAi) has rarely been applied due to the regulatory barriers that confront genetically engineered plants and concerns over possible environmental and health risks posed by non-endogenous small RNAs. ‘HoneySweet’ was developed as a virus-resistant plum variety that is protected by an RNAi-mediated process against Sharka disease caused by the plum pox virus. ‘HoneySweet’ has been approved for cultivation in the United States but not in countries where the plum pox virus is endemic. In this study, we evaluated the long-term efficacy of virus resistance in ‘HoneySweet,’ the nature and stability of its sRNA profile, and the potential health risks of consuming ‘HoneySweet’ plums. Graft-challenged ‘HoneySweet’ trees carrying large non-transgenic infected limbs remained virus-free after more than 10 years in the field, and the viral sequences from the non-transgenic infected limbs showed no evidence of adaptation to the RNAi-based resistance. Small RNA profiling revealed that transgene-derived sRNA levels were stable across different environments and, on average, were more than 10 times lower than those present in symptom-less fruits from virus-infected trees. Comprehensive 90-day mouse feeding studies showed no adverse health impacts in mice, and there was no evidence for potential siRNA off-target pathologies predicted by comparisons of the most abundant transgene-derived sRNAs to the mouse genome. Collectively, the data confirmed that RNAi provides a highly effective, stable, and safe strategy to combat virus diseases in crop plants.

Biofabricated nanoscale ZnO and their prospective in disease suppression and crop growth of Brassica species: A review

Biofabricated zinc oxide nanoparticles (ZnO Np’s) are made by biological means for example microbes, plants, etc. by using nanotechnology. Nanotechnology is the technology that involves the manipulation of a matter up to a nanometre scale by various methods. The future of the industrial revolution is nanotechnology and almost all the industries developing or developed will be fundamentally renovated by this technology soon. Besides application in various fields nanotechnology have a promising role in agriculture which is the heart and soul of the Indian economy. As per the scientists, metal and metal oxide nanomaterials in agriculture can help in improving the poor agriculture conditions in India. Recent reports indicate that 80% of diseases in crop plants occur due to fungal pathogens and to cure these diseases, various chemical, as well as biological fungicides, have been used. Chemical fungicides are toxic to our environment whereas biofabricated metal and metal oxides nanoparticles for example ZnO Np’s are eco-friendly, non-toxic, cost-effective, have antimicrobial activity, and also have a role in crop growth. The mechanistic approach behind their antifungal activity and crop growth is not fully explored yet. Fungal pathogens like Alternaria species and Sclerotinia species infect Brassica species which is the second most economically important oilseed crop after groundnut. There is a need for new strategies to develop novel fungicides to control fungal infections. The present review will address the possible role of ZnO Np’s in disease suppression caused by the fungal pathogens of Brassica species and also their role in crop growth.

A Fuzzy Inference System for Maize Plant Yield Prediction

Ascertaining infections in maize plants is through observation of the crop plant for visual indications which a farmer is able to relate to specific diseases. The perception of the farmer is prone to human error which may sometimes link some symptoms to the wrong disease and could impact the application of suitable preventive and curable routines to combat the identified diseases. Hence, accurate identification of crop plant disease is of high importance to a farmer to aid response to diseases. The objective of this article is to apply fuzzy set and interpolation technique to develop an expert system to carry out field-based identification and yield forecast for the maize plant. For this study, some associated factors were recognized for maize plant diseases and confirmed by a professional Botanists. For this study, a number of associated factors were identified for maize plant diseases and validated by experienced Botanists. Further to this, triangular membership functions was used to develop the fuzzy inference system model following the preprocessing of identified factors and related output. 32 inferred rules were formulated using IF-THEN statements which adopted the values of the factors as antecedent and the yield of maize plant as the consequent part of each rule for classification of the yield of maize plant. The Fuzzy model was simulated for each of the identified five factors. The simulation results showed that the risk factors identified; black moldy growth on kernels and ears, blights on leaves, rotten cobs, infected husks and black kernels and seed decay have noticeable influence on the maize plant yield if timely remedy is not administered. The study established that the utilisation of fuzzy technique is helpful to appraise the yield of maize such that the lesser the manifestation of identified associated features then the higher the yield of the maize plant.

Azole Use in Agriculture, Horticulture, and Wood Preservation - Is It Indispensable?

Plant pathogens cause significant damage to plant products, compromising both quantities and quality. Even though many elements of agricultural practices are an integral part of reducing disease attacks, modern agriculture is still highly reliant on fungicides to guarantee high yields and product quality. The azoles, 14-alpha demethylase inhibitors, have been the fungicide class used most widely to control fungal plant diseases for more than four decades. More than 25 different azoles have been developed for the control of plant diseases in crops and the group has a world market value share of 20-25%. Azoles have proven to provide long-lasting control of many target plant pathogens and are categorized to have moderate risk for developing fungicide resistance. Field performances against many fungal pathogens have correspondingly been stable or only moderately reduced over time. Hence azoles are still, to date, considered the backbone in many control strategies and widely used as solo fungicides or as mixing partners with other fungicide groups, broadening the control spectrum as well as minimizing the overall risk of resistance development. This review describes the historic perspective of azoles, their market shares and importance for production of major crops like cereals, rice, oilseed rape, sugar beet, banana, citrus, and soybeans. In addition, information regarding use in amenity grass, in the wood preservation industry and as plant growth regulators are described. At the end of the review azoles are discussed in a wider context including future threats following stricter requirements for registration and potential impact on human health.

Agriculture Crop Leaf Disease Detection using Image Processing

As we all know that the Agriculture plays an important role in the Indian economy and majority of the individuals depends upon it and offers huge amount of the crops through the worldwide. The Illnesses in these crops are generally on the leaf’s influences on the decrease of both quality and number of horticultural items. We should know the disease of the crop correctly to solve the problem. There will be a huge loss if we do not find the disease and treat properly. The view of natural eye isn’t so a lot more grounded in order to watch minutevariety in the contaminated piece of leaf. In thisreport, we are giving a programming answer fornaturally identify and arrange plant leaf diseases. In this we are utilizing picture preparing methods to characterize alignments and rapidly finding can be completed according to infection. This methodology will upgrade the efficiency of yields in a efficient way and can get us the accurate disease which helps us to find the solution for the diseased crop. It observes a few stages with the help of these pictures obtaining, picture pre-handling, division, highlights extraction and genetic algorithm-based grouping. Relating to the cultivation of land, efficiency is something on which economy exceptionally depends. This is the one of the reasons that sickness identification in plants assumes a significant job in the agriculture business field, as having the illness in plants are very normal. In an event that legitimate consideration isn’t taken here, at that point it causes true consequences for plantsand because of which quality of each and every item, amount or efficiency is being influenced. The recognition of plant infections through some programmed step is gainful as it avoids a huge work of checking in huge homesteads of harvests. At the beginning of the crop harvesting step itself, it shows the side effects or the symptoms of the diseases. This proposed method surfaces into a new programmed manner by distinguishing the effects of the crop plant diseases. We are using some image processing techniques for the identification of the disease. Additionally, it watches the review on the various diseases order strategies which also can be utilized for plant leaf alignment. Picture division, which is a significant viewpoint for sickness identificationin a plant leaf alignment, is finalized by the input RGB mask images.

Controlling Geminiviruses before Transmission: Prospects.

Whitefly (Bemisia tabaci)-transmitted Geminiviruses cause serious diseases of crop plants in tropical and sub-tropical regions. Plants, animals, and their microbial symbionts have evolved complex ways to interact with each other that impact their life cycles. Blocking virus transmission by altering the biology of vector species, such as the whitefly, can be a potential approach to manage these devastating diseases. Virus transmission by insect vectors to plant hosts often involves bacterial endosymbionts. Molecular chaperonins of bacterial endosymbionts bind with virus particles and have a key role in the transmission of Geminiviruses. Hence, devising new approaches to obstruct virus transmission by manipulating bacterial endosymbionts before infection opens new avenues for viral disease control. The exploitation of bacterial endosymbiont within the insect vector would disrupt interactions among viruses, insects, and their bacterial endosymbionts. The study of this cooperating web could potentially decrease virus transmission and possibly represent an effective solution to control viral diseases in crop plants.

Fungal effectors, the double edge sword of phytopathogens.

Phyto-pathogenic fungi can cause huge damage to crop production. During millions of years of coexistence, fungi have evolved diverse life-style to obtain nutrients from the host and to colonize upon them. They deploy various proteinaceous as well as non-proteinaceous secreted molecules commonly referred as effectors to sabotage host machinery during the infection process. The effectors are important virulence determinants of pathogenic fungi and play important role in successful pathogenesis, predominantly by avoiding host-surveillance system. However, besides being important for pathogenesis, the fungal effectors end-up being recognized by the resistant cultivars of the host, which mount a strong immune response to ward-off pathogens. Various recent studies involving different pathosystem have revealed the virulence/avirulence functions of fungal effectors and their involvement in governing the outcome of host-pathogen interactions. However, the effectors and their cognate resistance gene in the host remain elusive for several economically important fungal pathogens. In this review, using examples from some of the biotrophic, hemi-biotrophic and necrotrophic pathogens, we elaborate the double-edged functions of fungal effectors. We emphasize that knowledge of effector functions can be helpful in effective management of fungal diseases in crop plants.

Do Lytic Polysaccharide Monooxygenases Aid in Plant Pathogenesis and Herbivory?

Lytic polysaccharide monooxygenases (LPMOs), copper-dependent enzymes mainly found in fungi, bacteria, and viruses, are responsible for enabling plant infection and degradation processes. Since their discovery 10 years ago, significant progress has been made in understanding the major role these enzymes play in biomass conversion. The recent discovery of additional LPMO families in fungi and oomycetes (AA16) as well as insects (AA15) strongly suggests that LPMOs might also be involved in biological processes such as overcoming plant defenses. In this review, we aim to give a comprehensive overview of the potential role of different LPMO families from the perspective of plant defense and their multiple implications in devising new strategies for achieving crop protection from plant pathogens and insect pests.

Isolation and evaluation of rhizobacteria against Ralstonia solanacearum the incitant of bacterial wilt of tomato

Bacterial wilt caused by Ralstonia solanacearum is the world’s most economically important destructive disease of crop plants. In the current study, we aimed to evaluate the novel bacterial isolates from tomato rhizosphere for biocontrol of Ralstonia solanacearum. One eighty bacterial strains were isolated from the tomato rhizosperic soils collected from different regions of Uttarakhand state and evaluated for their biocontrol activity against R. solanacearum under in vitro conditions. Among them, six isolates were found to be highly effective in inhibiting the growth of R. solanacearum. The isolate GP2NA8 produced the highest inhibition zone followed by that of GP1NA2 and GP3NA6.

The Role of Nanoparticles in the Diagnosis and Treatment of Diseases

Nanotechnology is involved in the diagnosis and treatment of infectious and inflammatory diseases. It has shown a good role in regenerating, restoring and repairing damaged body parts, such as the heart, lungs and blood vessels. Nanoparticles (NPs) are helpful in osteoblasts formation and also used in the treatment of bone inflammation, skin infections, tuberculosis (TB), human immunodeficiency virus (HIV), Parkinson’s disease, atherosclerosis, cardiovascular and pulmonary diseases. They also assist the anti-inflammatory drugs in penetrating the skin. Platinum nanoparticles (NP) are used in bone allograft and dentistry while silver NPs possess an excellent potential against viruses, fungi and bacteria. For diagnostic purposes, nanoparticles are mostly used in the form of nanorobots, microchips and biosensors. Quantum dots give information about the tumor; the gold nano-particles are ideal to detect the antibodies of hepatitis and also for RNA and DNA delivery. Nanomaterials play an important role in the management of plant diseases and the activation of their defense mechanisms. The NPs of copper and silver are directly toxic to microorganisms while those of zinc, silicon, manganese, copper and boron have a function in host defense as a fertilizer and alter the nutritional status of the crop. Enzyme-based biosensors coated with Ti, Cu, Ag or Au-NPs greatly enhance the sensitivity of diagnostic probes for the detection of plant infections. The nano-Zn products have been effectively used to control viral, fungal, phytoplasma or bacterial diseases in crop plants. Nanoparticles are also used in packing edible food films.

Auxins of microbial origin and their use in agriculture.

To maintain the world population demand, a sustainable agriculture is needed. Since current global vision is more friendly with the environment, eco-friendly alternatives are desirable. In this sense, plant growth-promoting rhizobacteria could be the choice for the management of soil-borne diseases of crop plants. These rhizobacteria secrete chemical compounds which act as phytohormones. Indole-3-acetic acid (IAA) is the most common plant hormone of the auxin class which regulates various processes of plant growth. IAA compound, in which structure can be found a carboxylic acid attached through a methylene group to the C-3 position of an indole ring, is produced both by plants and microorganisms. Plant growth-promoting rhizobacteria and fungi secrete IAA to promote the plant growth. In this review, IAA production and mechanisms of action by bacteria and fungi along with the metabolic pathways evolved in the IAA secretion and commercial prospects are revised.Key points• Many microorganisms produce auxins which help the plant growth promotion.• These auxins improve the plant growth by several mechanisms.• The auxins are produced through different mechanisms.

An ankyrin-repeat and WRKY-domain-containing immune receptor confers stripe rust resistance in wheat

Perception of pathogenic effectors in plants often relies on nucleotide-binding domain (NBS) and leucine-rich-repeat-containing (NLR) proteins. Some NLRs contain additional domains that function as integrated decoys for pathogen effector targets and activation of immune signalling. Wheat stripe rust is one of the most devastating diseases of crop plants. Here, we report the cloning of YrU1, a stripe rust resistance gene from the diploid wheat Triticum urartu, the progenitor of the A genome of hexaploid wheat. YrU1 encodes a coiled-coil-NBS-leucine-rich repeat protein with N-terminal ankyrin-repeat and C-terminal WRKY domains, representing a unique NLR structure in plants. Database searches identify similar architecture only in wheat relatives. Transient expression of YrU1 in Nicotiana benthamiana does not induce cell death in the absence of pathogens. The ankyrin-repeat and coiled-coil domains of YrU1 self-associate, suggesting that homodimerisation is critical for YrU1 function. The identification and cloning of this disease resistance gene sheds light on NLR protein function and may facilitate breeding to control the devastating wheat stripe rust disease.

Crop leaf disease grade identification based on an improved convolutional neural network

To achieve high yield of crops and to avoid pesticide abuse, different control methods have been adopted according to different degrees of disease in crop plants. To address the issue, a leaf disease grade identification method based on a convolutional neural network (CNN) was proposed. First, nonuniform illumination images were processed using an adaptive adjustment algorithm based on a two-dimensional (2-D) gamma function. Then a threshold segmentation method was used to segment diseased leaf images and thus to obtain binary images. Next, the ratio of the number of pixels in the lesion area to that in the diseased leaf area was calculated. This ratio was regarded as the classification threshold of the disease grades, and therefore was used to determine the disease grade category. In addition, use of a ResNet50-based CNN was proposed to identify disease grades, with a focal loss function replacing the standard cross entropy loss function, and with the Adam optimization method. Finally, leaf disease grade identification was performed on a database containing 10 types of disease leaf images for 8 crops, and it yielded a recognition accuracy of 95.61%. The experimental results showed that the proposed method was feasible and effective for the classification of leaf disease grades.

Hazardous Impacts of Pesticide Usage on Farmer’s Health in Cotton Growing Region of District Muzaffargarh, Pakistan

&#x0D; &#x0D; &#x0D; Pesticides are the chemicals used to control the crop harms by killing the pests causing various diseases in crop plants, thereby playing vital role in yield and crop production. The ingredients of these chemical pesticides are not only lethal to target arthropods but also for human beings. Therefore, the aim of present study was to find out the perception about the impacts of various pesticide usage on the health of cotton growing farmers in rural areas of District Muzaffargarh, Pakistan. Three villages were selected purposively and 130 farmers were approached to perform a questionnaire via face to face interview. Our findings revealed that a major proportion of the respondents (56.9%) were agreed that pesticides cause illness in cotton growing farmers. The results showed the perceptions of respondents that pesticides were responsible to cause various diseases to a great extent in farmers, like increase in headache (48.5%), fatigue (40.0%), insomnia (45.4%), dizziness (37.7%), hand tremors (42.3%), skin disorders (46.9%), birth defects (36.2%), damage of liver (37.7%), damage of kidney (43.8%), respiratory problems (56.9%) and cancer (29.2%). Our results showed non-significant association between age and impacts of pesticide usage on cotton growing farmer’s health. It was illustrated that health issues were significantly correlated with education, income, lack of awareness, lack of precautionary measures and lack of facilities or personal protective equipment (PPE).There is need to implement risk reduction strategies and to arrange formal training sessions to address the proper use and storage of pesticides.&#x0D; &#x0D; &#x0D;

Seed priming with biotic agents invokes defense response and enhances plant growth in pearl millet upon infection with Magnaporthe grisea

Abstract The induction of host-defense in plants is a reliable approach for the management of diseases in crop plants. In the present study, biotic agents like Pseudomonas fluorescens, Trichoderma virens and neem leaf extract have been evaluated for their efficacy to induce resistance against blast disease of pearl millet caused by Magnaporthe grisea and promote growth. Seeds treated with P. fluorescens exhibited maximum seed germination of 83.50% and seedling vigor of 1447.93 followed by seed treatment with T. virens and neem leaf extract. Seeds treated with P. fluorescens showed improved vegetative and reproductive parameters compared with other biotic agents. Under greenhouse conditions, P. fluorescens offered maximum disease protection of 62.25% against blast disease followed by T. virens with 58.25%. It was also found that the activity of the defense-related enzymes like Phenylalanine ammonia lyase (PAL), Peroxidase (POX), Lipoxygenase (LOX) and β-1,3-glucanase was increased considerably in P. fluorescens treated and challenge inoculated seedlings thereby indicating that these defense enzymes play a pivotal role in induction of blast disease resistance in pearl millet.