Reduce Yield Losses Research Articles

Phenotypic profiling of lentil (Lens culinaris Medikus) accessions enabled identification of promising lines for use in breeding for high yield, early flowering and desirable traits

AbstractIn the face of climate change, developing resilient crops is crucial for global food security in the 21st century to feed a growing population. Lentil (Lens culinaris Medikus) plays a vital role in ensuring global food and nutritional security. Traits like early flowering enable the crop to mature faster, thereby shortening the growing window and reducing yield losses caused by moisture and heat stresses during the reproductive phase. However, issues like limited genetic diversity in this trait remain unaddressed. To address this gap, our study aims to comprehensively assess genetic variability and trait associations in 158 lentil accessions. In the present study, we observed significant variations for days to 50% flowering (67–90 days), days to maturity (109–122 days) and 100 seed weight (1.69–2.68 g) throughout a period of two consecutive years (2020–2021 and 2021–2022). The observed variability in these traits offers a valuable avenue for the improvement of lentil yield through targeted selection and hybridization. Additionally, correlation analysis showed negative correlation between days to 50% flowering and grain yield per plant, while plant height had a significant (P < 0.01) positive correlation with all traits except yield per plant. Furthermore, we identified specific germplasm with exceptional traits that hold significant potential for future breeding programmes. The genotypes EC 223197-A and EC 267696 were identified for early flowering with high yield, and other genotypes that were identified for various traits would serve as breeding material for the introgression of these traits into elite cultivars.

Estimating the impact on maize production by the weed Parthenium hysterophorus in Pakistan

Parthenium hysterophorus L. (Asteraceae) is an aggressive annual herbaceous weed which causes severe yield losses on a global scale. However, the precise impact of this weed in several countries is not yet known. To assess the impact of P. hysterophorus on maize crop losses in Pakistan, a 2 year field experiment was carried out at the CABI Rawalpindi campus in 2019 and 2020. Yield was measured on maize plants planted along with different densities (0, 1, 2, 4, 8, and 16 plants per m2) of P. hysterophorus. The trial was laid in a randomized complete block design with five replicates per density each year. The highest maize seed yield was obtained in weed-free plots (4256.5 ± 118 kg ha−1), while maize yield at weed infestation levels of 1, 2, 4, 8 and 16 plants m−2 was reduced by 14, 22, 29, 38 and 46%, respectively, compared to weed free plots. In the light of these findings, to reduce yield losses to non-significant levels it is proposed to reduce P. hysterophorus densities below 2 plants per m2 in maize fields.

Favorable Loci Identified for Stripe Rust Resistance in Chinese Winter Wheat Accessions via Genome-Wide Association Study.

Stripe rust (or yellow rust), caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating diseases of wheat worldwide. Currently, the utilization of resistant cultivars is the most viable way to reduce yield losses. In this study, a panel of 188 wheat accessions from China was evaluated for stripe rust resistance, and genome-wide association studies were performed using high-quality Diversity Arrays Technology markers. According to the phenotype and genotype data, a total of 26 significant marker-trait associations were identified, representing 18 quantitative trait loci (QTLs) on chromosomes 1B, 2A, 2B, 3A, 3B, 5A, 5B, 6B, 7B, and 7D. Of the 18 QTLs, almost all were associated with adult plant resistance (APR) except QYr.nwsuaf-6B.2, which was associated with all-stage resistance (also known as seedling resistance). Three of the 18 QTLs were mapped far from previously identified Pst resistance genes and QTLs and were considered potentially new loci. The other 15 QTLs were mapped close to known resistance genes and QTLs. Subsequent haplotype analysis for QYr.nwsuaf-2A and QYr.nwsuaf-7B.3 revealed the degrees of resistance of the panel in the APR stage. In summary, the favorable alleles identified in this study may be useful in breeding for disease resistance to stripe rust.

A novel rice plant leaf diseases detection using deep spectral generative adversarial neural network

The farming industry widely requires automatic detection and analysis of rice diseases to avoid wasting financial and other resources, reduce yield loss, improve processing efficiency, and obtain healthy crop yields. The proposed Deep Spectral Generative Adversarial Neural Network (DSGAN2) method is used for detecting rice plant leaf disease. Initially, fed into the input of healthy and non-healthy leaves from the collected dataset. Then, apply an Improved Threshold Neural Network (ITNN) method to enhance the image quality. Next, it uses a Segmentation using a Segment Multiscale Neural Slicing (SMNS) algorithm to identify the support-intensive color saturation based on the enhanced image. After that, the Spectral Scaled Absolute Feature Selection (S2AFS) method is applied to select optimal features and the closest weight from segmented rice plant leaves. Social Spider Optimization will select the feature using the Closest Weight (S2O-FCW) algorithm to analyze the feature weight values. Finally, the proposed Soft-Max Logistic Activation Function with Deep Spectral Generative Adversarial Neural Network (DSGAN2) algorithm detects rice plant disease based on selected features. With an accuracy of 97 %, the model helps farmers identify and identify Rice Plant diseases. The proposed system Deep Spectral Generative Adversarial Neural Network (DSGAN2) produces a decreasing false rate compared to the existing system of ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN) is 55.2 %, Alex Net is 50.4 %, and Convolutional Neural Network (CNN) is 49.5 %.

Research progress and mitigation strategies for pod shattering resistance in rapeseed.

Mature rapeseed pods typically shatter when harvested, resulting in approximately 8-12% yield loss. Adverse weather conditions and mechanized harvesting can diminish pod yield by up to 50%, primarily owing to delays in harvesting and mechanical collisions. The pod shatter resistance index (PSRI) assesses pod damage. Recent research focused on comparing pod shatter resistance among varieties, evaluating methods, and studying gene knockout mechanisms. However, there remains a pressing need to broaden the scope of research. In particular, it is essential to recognize that pod shatter, a complex trait, influenced by genetics, environment, agronomic practices, and harvest techniques. Future studies should integrate these factors to develop comprehensive strategies to mitigate pod shatter, enhancing rapeseed yields and agricultural mechanization. This review explores factors affecting pod shatter resistance and strategies to improve it. Scoping literature review that adhered to the methodological framework for systematic reviews was performed using search engines such as Google Scholar, Web of Science, and the Chinese National Knowledge Infrastructure. This review aimed to identify pertinent articles, which were subsequently subjected to thorough screening and evaluation. The protocol for this literature review involved the following key steps: definition of research questions, development of a search strategy, development of data extraction strategy, synthesis of the extracted data, and organization and analysis of the extracted data. The review presents strategies for enhancing rapeseed yield during mechanized harvesting, focusing on four key areas: (i) selecting and breeding shatter-resistant varieties using DNA markers to establish a robust germplasm resource; (ii) optimizing cultivation technologies and agronomic measures to elicit favorable interactions between compact plant-type genotypes and the environment, thereby facilitating nutrient-related regulatory mechanisms of rapeseed pods to improve pod dry weight and resistance; (iii) innovating combine header design and structure to better suit rapeseed harvesting; and (iv) providing training for operators to enhance their harvesting skills. These comprehensive measures aim to minimize yield loss, increase production efficiency. To effectively reduce yield loss during mechanized harvesting of rapeseed, it is crucial to enhance resistance to pod shattering by addressing both internal physiological factors and external environmental conditions. This requires a holistic approach that includes genetic improvements, optimization of ecological conditions, careful cultivation management, and precise harvesting timing, along with ongoing research into traits related to machine harvesting to boost production efficiency and sustainability.

Определение аналитической чувствительности и специфичности методов ПЦР для диагностики черной бактериальной пятнистости томата

The work is devoted to molecular genetic methods for the diagnosis of pathogens of bacterial spot of tomato – a harmful bacterial disease of tomatoes and peppers, predominantly spread in the open ground in the southern regions. The research was conducted on the basis of the All- Russian Plant Quarantine Center (FGBU “VNIIKR”) in 2022–2023. The objective of the work was to determine the analytical sensitivity and specificity of molecular genetic methods for the detection and identification of three species of different pathological types of bacteria of the genus Xanthomonas – pathogens of bacterial spot of tomato. Due to the necessity of import substitution of expensive reagents, the tests were carried out with domestic reagents produced by OOO “Evrogen” and ZAO “Dialat”. The results of the effectiveness of real-time PCR (qPCR) are presented, the analysis of which showed that all tested primer systems can be recommended for the detection of bacteriosis pathogens. At the same time, qPCR AFLP derived Taqman PCR and XopD Taqman PCR were found to have high analytical sensitivity (102–103 CFU/ml). It has been shown that three out of four primer systems according to to the methodology of Koenraadt et al. (2009) also have a high sensitivity of 102 CFU/ml. The analytical sensitivity of the Bs-XpF/R primers for the detection of X. euvesicatoria pv. perforans was 104 CFU/ml, which is sufficient for samples with a high concentration of the pathogen and for the identification of a pure bacterial culture. All primers are 100% specific for the target strains of pathogens; no cross-reactions with other strains have been observed. The proposed reliable operational method for detecting pathogens of bacterial spot in tomato and pepper seeds will significantly reduce yield losses and increase the economic efficiency of domestic vegetable production.

Revolutionizing Potato Farming: Dynamic Innovations Reshaping Late Blight Control in Pakistan

Potato (Solanum tuberosum L.) is one of the most extensively cultivated crops around the world, and Pakistan’s weather favor production and cultivation. Despite the ease of cultivating potatoes and the nominal labor supplies, Pakistan’s output of potatoes has not yet achieved a promising level when compared to nearby nations like India and Bangladesh. The crop's low production in Pakistan is caused by several biotic and abiotic stresses that instigated several pathogenic diseases such as early blight, bacterial wilt, viral infections, nematode infestations and late blight. Late blight disease is one of the most dreaded diseases of potatoes globally. It is caused by the fungus Phytophthora infestans which is highly adaptive to fungicides. Several fungicides have been in use against this disease for a long time, however, it has developed a high degree of resistant strains to these fungicides due to their unselective use over time and ultimately may cause colossal losses to the crop. In response, scientists have developed different potato varieties like Setanta, Nicola, Cara and Acoustic that are highly resistant to late blight. Similarly, cultural methods are also employed to combat late blight disease. This article provides an overview of the implications of late blight disease in potato cultivation and explores various strategies to reduce yield losses brought on by this disease via the application of synthetic fungicides.

Classification and Prediction of Crop Diseases: A Review

Agriculture is the foundation of civilization which faces several problems in the 21st century. Crop diseases threaten global food security by reducing yields. Visual inspection to detect diseases can be time-consuming, subjective, and error-prone. Recent advances in various machine learning (ML) and deep learning (DL) techniques have led to ease in the identification of crop diseases. ML and DL demonstrate their versatility in image recognition, segmentation, and anomaly detection. In this paper, some of the recent works based on crop-disease detection using various ML and DL techniques are reviewed. It includes early illness detection and appropriate interventions to reduce yield loss. The review emphasizes the relevance of crop disease detection for food security and sustainable agriculture. ML and DL techniques can help farmers monitor crop health and optimize resource allocation.

Understanding the Degradation of Carbofuran in Agricultural Area: A Review of Fate, Metabolites, and Toxicity

Pesticides are commonly applied in agriculture to manage pests, parasites, weeds, and other crop diseases to maximise product output value and minimise or reduce yield losses. Pesticides are widely and indiscriminately used in crop production in most countries. Carbofuran is a highly toxic insecticide commonly used to protect crops in agricultural areas. Exposure to carbofuran can cause harmful effects on both the ecological environment and human health, particularly on non-target species such as birds and aquatic organisms. Carbofuran continues to be used, although it has been banned in some countries. This review paper highlights carbofuran usage, its residue, toxicity, and mechanisms of degradation pathways in water, soil, and food, especially in agriculture. It has been shown that hydrolysis, photolysis, and microbial degradation are the main pathways for carbofuran degradation. Carbofuran phenol is significantly less toxic than all photo-metabolites, while 3-ketocarbofuran and 3-hydroxycarbofuran are practically as harmful as the parent compound. Hence, more study is needed on degradation pathways that may thoroughly minimise the toxicity of this deadly pesticide.

Control measure of sweet potato weevil (Cylas formicarius Fab.) (Coleoptera: Curculionidae) in endemic land of entisol type using mulch and entomopathogenic fungus Beauveria bassiana

Abstract Sweet potato weevil Cylas formicarius (Fab.) is the main obstacle for sweet potato production in various countries. Root damage caused by C. formicarius larvae reduced root yield up to 100%. The aim of this study is to test the measures using mulch and entomopathogenic fungus Beauveria bassiana for controlling C. formicarius in endemic land of entisol type. The control measure tested was the use of straw mulch and plastic mulch as well as the application of the entomopathogenic fungus Beauveria bassiana. The research was conducted at the experimental station at Indonesian Legumes and Tuber Crops Research Institute, Malang from July to December 2018. The results showed that the measure for controlling C. formicarius using straw or plastic mulch combined with the entomopathogenic fungus B. bassiana produces root yields between 17 and 26 t/ha. Using plastic mulch as a cover for mounds with the application of the fungus B. bassiana is more effective and efficient in controlling C. formicarius than the insecticide deltamethrin. Plastic mulch can physically inhibit the process of laying eggs and the formation of C. formicarius larvae, while B. bassiana is toxic to eggs, larvae, and adults of C. formicarius. The efficacy of control measure using plastic mulch and the application of B. bassiana can reduce yield losses by up to 96.76%. Technological innovation using plastic mulch to cover the mound with the application of the entomopathogenic fungus B. bassiana can be recommended to control C. formicarius on land endemic to the entisol type.

Advancement in Integrated Crop Management System for Sustainable Agriculture

The Crop Management System (CMS) is an innovative web application designed to revolutionize agricultural practices by integrating advanced technologies. This project encompasses four essential modules: Crop Prediction, Disease Detection, Marketing, and Government Forum Dashboard. Leveraging the power of Python, the CMS aims to provide a comprehensive solution for modern farming. The Crop Prediction module employs machine learning algorithms to forecast optimal crops based on factors such as soil type, climate, and historical data. This feature empowers farmers to make informed decisions, enhancing crop yield and profitability. The Disease Detection module employs image processing techniques to identify and diagnose diseases affecting crops, allowing for timely intervention and reducing yield loss. The Marketing module serves as a platform for farmers to streamline the selling process. Through features like price tracking and market trends analysis, farmers can optimize their sales strategies. The Government Forum Dashboard acts as a central hub for stakeholders to exchange information, policies, and best practices, fostering a collaborative ecosystem. Implemented as a web application, the CMS ensures accessibility across devices, providing a user-friendly interface for farmers and stakeholders. The backend is built using Python, leveraging its versatility and robust libraries for data processing, machine learning, and web development. In conclusion, the Crop Management System addresses critical aspects of modern agriculture, ranging from crop selection to disease management, marketing, and policy advocacy. By harnessing the power of Python and cutting-edge technologies, this project stands as a pivotal tool for advancing agricultural practices, ultimately contributing to sustainable and efficient farming practices

Yield components affected by rice black-streaked dwarf virus disease in rice cultivars with different resistance levels.

Rice black-streaked dwarf virus disease (RBSDVD) is one of the most destructive rice viral diseases, leading to severe yield losses in rice production. However, little is known about the yield-related components associated with the disease and no resistance cultivars have been successfully used in rice breeding. Seven rice cultivars were analyzed in this study, including six commercial rice varieties and a new line Zhongjian No. 201 (ZJ201) containing the resistance gene OsAP47. Resistance levels of these cultivars were evaluated by artificial inoculation and yield components were collected, including panicle length (PL), spikelets per panicle (SPP), ripened grains per panicle (RGPP), as well as panicles per square meter (PPSM) and 1000-grain weight (TGW). Seed setting rate (SSR) were calculated with the data of SPP and RGPP. The results showed that ZJ201 displayed the highest resistance level and most of the commercial rice cultivars exhibited susceptible to RBSDVD. Yields of all the rice cultivars were significantly declined except ZJ201 and yield losses produced by RBSDVD were mainly due to the reduction of PL, SPP, RGPP, and TGW, suggesting that developments of these traits are associated with RBSDV infection. Resistant rice cultivar could reduce yield losses by maintaining normal development of these traits. Significant correlations were identified between resistance levels and the yield components except SSR and PPSM. The results provided useful clues for understanding the mechanisms of RBSDV invasion and its effect on rice production. ZJ201 was demonstrated as a resistance material that could be used in rice breeding.

Melatonin enhances waterlogging tolerance of field-grown cotton through quiescence adaptation and compensatory growth strategies

ContextWaterlogging stress inhibits plant growth and development, resulting in significant yield losses. Melatonin, a naturally occurring molecule in plants, has attracted attention in recent years for its potential role in aiding plants in response to abiotic stresses. However, it remains unclear whether melatonin can enhance cotton’s tolerance to waterlogging stress. MethodsTo fill this gap, field-grown cotton plants at early flowering were subjected to a 10-day waterlogging stress and were foliar sprayed with melatonin during waterlogging. The effects of melatonin on the endogenous melatonin concentration, plant biomass, and related physiological and molecular events, as well as the seedcotton yield and yield components, were evaluated over two consecutive years. ResultsIn waterlogged cotton without melatonin treatment (W), melatonin concentration increased by 10.2%, but it increased by 26.9% with melatonin treatment (W+M), compared to the non-waterlogged cotton (NW). Under waterlogging without the application of melatonin, there was a reduction of 27.1% in biological yield and 46.6% in seedcotton yield. However, these reductions were reduced to 21.8% and 39.0%, respectively, following melatonin application. Quantitative real-time PCR analysis revealed that melatonin up-regulated the melatonin biosynthesis genes GhSNAT1 and GhCOMT, as well as the gibberellin biosynthetic gene GhGA20ox1 by 2.1, 3.6 and 1.8 times, respectively, compared to non-waterlogged cotton. However, melatonin down-regulated the ABA synthesis gene GhNCED2, the H2O2 generation related gene GhRBOHC, and the glycolysis and fermentation gene GhADH2 by 61.9%, 65.0% and 40.0%, respectively. Additionally, melatonin increased the leaf chlorophyll content, photosynthetic rate, levels of auxin (IAA) and gibberellic acid (GA), as well as the activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT). Conversely, melatonin reduced contents of hydrogen peroxide (H2O2), malonaldehyde (MDA) and ABA, as well as the activities of alcohol dehydrogenase (ADH) and pyruvate decarboxylase (PDC), compared to waterlogging without application of melatonin. ConclusionsThese findings demonstrate that foliar application of melatonin effectively improved the waterlogging tolerance of field-grown cotton, leading to a reduction in yield loss by 16.5% under waterlogged conditions. This outcome was achieved by a series of physiological, biological and molecular adjustments involved in quiescence adaption and compensatory growth strategies. Therefore, melatonin plays a crucial role in enhancing waterlogging tolerance and mitigating yield loss in waterlogged cotton through these strategies.

Promoting grain production through high-standard farmland construction: Evidence in China

Food security is a strategic priority for a country’s economic development. In China, high-standard farmland construction (HSFC) is an important initiative to stabilize grain production and increase grain production capacity. Based on panel data from 31 sample provinces, autonomous regions, and municipalities in China from 2005–2017, this study explored the impact of HSFC on grain yield using the difference-in-differences (DID) method. The results showed that HSFC significantly increased total grain production, which is robust to various checks. HSFC increased grain yield through three potential mechanisms. First, it could increase the grain replanting index. Second, it could effectively reduce yield loss due to droughts and floods. Last, HSFC could strengthen the cultivated land by renovating the low- and medium-yielding fields. Heterogeneity analysis found that the HSFC farmland showed a significant increase in grain yield only in the main grain-producing areas and balanced areas. In addition, HSFC significantly increased the yields of rice, wheat, and maize while leading to a reduction in soybean yields. The findings suggest the government should continue to promote HSFC, improve construction standards, and strictly control the “non-agriculturalization” and “non-coordination” of farmland to increase grain production further. At the same time, market mechanisms should be used to incentivize soybean farming, improve returns and stabilize soybean yields.

Comparison of the Efficiency of Hyperspectral and Pulse Amplitude Modulation Imaging Methods in Pre-Symptomatic Virus Detection in Tobacco Plants.

Early detection of pathogens can significantly reduce yield losses and improve the quality of agricultural products. This study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants. Reflectance spectra, normalized indices, and fluorescence parameters were studied in healthy and infected areas of leaves. Potato virus X with GFP fluorescent protein was used to assess the spread of infection throughout the plant. The study found that infection increased the reflectance of leaves in certain wavelength ranges. Analysis of the normalized reflectance indices (NRIs) revealed indices that were sensitive and insensitive to infection. NRI700/850 was optimal for virus detection; significant differences were detected on the 4th day after the virus arrived in the leaf. Maximum (Fv/Fm) and effective quantum yields of photosystem II (ΦPSII) and non-photochemical fluorescence quenching (NPQ) were almost unchanged at the early stage of infection. ΦPSII and NPQ in the transition state (a short time after actinic light was switched on) showed high sensitivity to infection. The higher sensitivity of PAM compared to HS imaging may be due to the possibility of assessing the physiological changes earlier than changes in leaf structure.

Genome-wide association study for charcoal rot resistance in soybean harvested in Kazakhstan.

Charcoal rot (CR) caused by the fungal pathogen Macrophomina phaseolina is a devastating disease affecting soybean (Glycine max (L.) Merrill.) worldwide. Identifying the genetic factors associated with resistance to charcoal rot is crucial for developing disease-resistant soybean cultivars. In this research, we conducted a genome-wide association study (GWAS) using different models and genotypic data to unravel the genetic determinants underlying soybean resistance to сharcoal rot. The study relied on a panel of 252 soybean accessions, comprising commercial cultivars and breeding lines, to capture genetic variations associated with resistance. The phenotypic evaluation was performed under natural conditions during the 2021-2022 period. Disease severity and survival rates were recorded to quantify the resistance levels in the accessions. Genotypic data consisted of two sets: the results of genotyping using the Illumina iSelect 6K SNP (single-nucleotide polymorphism) array and the results of whole-genome resequencing. The GWAS was conducted using four different models (MLM, MLMM, FarmCPU, and BLINK) based on the GAPIT platform. As a result, SNP markers of 11 quantitative trait loci associated with CR resistance were identified. Candidate genes within the identified genomic regions were explored for their functional annotations and potential roles in plant defense responses. The findings from this study may further contribute to the development of molecular breeding strategies for enhancing CR resistance in soybean cultivars. Marker-assisted selection can be efficiently employed to accelerate the breeding process, enabling the development of cultivars with improved resistance to сharcoal rot. Ultimately, deploying resistant cultivars may significantly reduce yield losses and enhance the sustainability of soybean production, benefiting farmers and ensuring a stable supply of this valuable crop.

The Utilization of Technology in the Production of Trichocompost Fertilizer and its Subsequent Application to Chrysanthemum Plants at the Pudak Lestari Agro Ornamental Plant Farmer Group

The ornamental plant farmer group Agro Pudak Lestari has long cultivated chrysanthemums. In the cultivation of chrysanthemums, the Agro Pudak Lestari farmer group experienced several obstacles, namely yields that did not meet market criteria caused by white rust disease. Antagonistic microorganisms (Trichoderma sp.) effectively control white rust disease. White rust disease is a soil infectious disease so countermeasures are carried out by making compost containing the biological agent Trchoderma (Trichocompost fertilizer). From the survey results, the results of flower damage due to rust disease reached 80%. Furthermore, after the application of Trichocompost fertilizer treatment, it suppressed rust disease by 56.4%. From this activity, it was able to reduce yield losses by 23.4% and increase farmers' income by 28.2%. The use of Trichocompost fertilizer can overcome white rust disease in chrysanthemum plants so it can increase farmer productivity.

Thermal Image Processing for Automatic Detection of Fusarium Root and Crown Rot Disease In Tomato Plants

Plant diseases can lead to significant yield losses and economic damages, but these losses can be mitigated through early disease diagnosis. In recent times, remote sensing techniques have been widely used for early disease detection even before visible symptoms appear. This study focused on the potential of early detection of Fusarium Root and Crown Rot in Tomato Plants, which causes substantial yield losses in tomato plants, under controlled conditions using thermal images. In this research, thermal images were obtained from both disease-inoculated and disease-free control plants throughout the plant growth period under controlled conditions. These images underwent preprocessing in a computer environment, and various feature parameters related to temperature changes in both groups (such as minimum, maximum, standard deviation, and skewness) were extracted. These extracted features were then used as inputs for different machine learning techniques, including K-Nearest Neighbors (KNN), Logistic Regression (LR), and Naive Bayes (NB), to classify healthy and diseased plants. Overall, the disease-inoculated plants exhibited higher average temperatures compared to the healthy control plants. The performance of the compared machine learning techniques in distinguishing between healthy and diseased plants was found to be in the order of KNN, NB, and LR, with success rates of 72%, 68%, and 60%, respectively. This study demonstrated the potential of using combined thermal images with different machine learning techniques for early diagnosis of Fusarium Root and Crown Rot in Tomato Plants. The results show promising prospects for utilizing thermal imaging in the early detection of plant diseases, leading to better management and reduction of yield losses and economic impacts.

Breeding of Okra for Resistance to Yellow Vein Mosaic Virus

Okra (Abelmoschus esculentus L. Moench) commonly known as Lady’s finger or Bhendi is most delicious vegetable relished worldwide. The productivity of okra has been low in recent times due to various reasons viz., inadequate use of fertilizers, irrigation and occurrence of various pest and diseases. In India okra crop is highly susceptible to yellow vein mosaic virus (YVMV) disease probably due to warm tropical climate and intensive crop cultivation which supports the survival of whitefly population round the year. Host plant resistance to virus is one of the most practical, economical and eco-friendly strategies for reducing yield loss in okra. Identification of stable and reliable sources of resistance to viral diseases is an important step in future breeding programme. Understanding the genetics of resistance in donor parent helps in choosing suitable breeding method and attempts should be made to incorporate broad spectrum resistance through gene pyramiding and develop okra varieties with desirable resistance or tolerance to okra YVMV followed by maintenance breeding.

Hordeum vulgare differentiates its response to beneficial bacteria

BackgroundIn nature, beneficial bacteria triggering induced systemic resistance (ISR) may protect plants from potential diseases, reducing yield losses caused by diverse pathogens. However, little is known about how the host plant initially responds to different beneficial bacteria. To reveal the impact of different bacteria on barley (Hordeum vulgare), bacterial colonization patterns, gene expression, and composition of seed endophytes were explored.ResultsThis study used the soil-borne Ensifer meliloti, as well as Pantoea sp. and Pseudomonas sp. isolated from barley seeds, individually. The results demonstrated that those bacteria persisted in the rhizosphere but with different colonization patterns. Although root-leaf translocation was not observed, all three bacteria induced systemic resistance (ISR) against foliar fungal pathogens. Transcriptome analysis revealed that ion- and stress-related genes were regulated in plants that first encountered bacteria. Iron homeostasis and heat stress responses were involved in the response to E. meliloti and Pantoea sp., even if the iron content was not altered. Heat shock protein-encoding genes responded to inoculation with Pantoea sp. and Pseudomonas sp. Furthermore, bacterial inoculation affected the composition of seed endophytes. Investigation of the following generation indicated that the enhanced resistance was not heritable.ConclusionsHere, using barley as a model, we highlighted different responses to three different beneficial bacteria as well as the influence of soil-borne Ensifer meliloti on the seed microbiome. In total, these results can help to understand the interaction between ISR-triggering bacteria and a crop plant, which is essential for the application of biological agents in sustainable agriculture.