An investigation over three consecutive years (2016-2018) evaluated eco-friendly management strategies for Maydis leaf blight (MLB) of maize caused by Bipolaris Maydis under field conditions. Ten treatments-nine herbal extracts and one animal refuse (cow urine)-were tested at varying concentrations. Azadirachta indica (neem) leaf extract was most effective, providing 20.75% disease control and a 52.75% yield increase, followed by Allium sativum extract with similar disease control and a 42.64% yield gain. Polyalthia longifolia and Parthenium hysterophorus extracts at 10% concentration showed moderate efficacy, achieving 15.09% disease control. Genotype (G) effects and genotype-by-environment (G & times; E) interaction assessed via GGE biplot revealed two distinct environmental groups for disease severity (Environment 2-Environment 3 versus Environment 1), while all environments clustered together for grain yield (E1-E3). The study highlights natural bioextracts as sustainable MLB management alternatives that avoid environmental pollution.

The present study aims to evaluate pathogenic variability among Drechslera maydis isolates, an incitant of maydis leaf blight (Southern leaf blight), across different maize-growing states of India. Pathogenic variation among five D. maydis isolates was assessed using ten maize genotypes under pot-house (protected) conditions. A substantial variation in virulence among five isolates was observed. Isolate Dm_1 (Ludhiana isolate) was found to be the most virulent with the shortest incubation period (5 days), maximum average per cent disease severity index (63.9

Foliar diseases are a significant constraint to maize productivity across key agro-climatic zones in India. This study assessed the efficacy of fungicide treatments for managing five major foliar diseases, viz., maydis leaf blight (MLB), turcicum leaf blight (TLB), curvularia leaf spot (CLS), sorghum downy mildew (SDM) and Rajasthan downy mildew (RDM), over multiple environments during 2017-2023. The comparative field trials were conducted under protected (fungicide application) and unprotected (control) conditions, with artificial inoculation, to assess disease intensity, grain yield and avoidable yield losses (AYL). Two foliar fungicide sprays were applied within 15 days after inoculation, whereas a single seed treatment was used for managing seed-borne and systemic diseases. Fungicide application significantly reduced foliar disease intensity across all locations and seasons, resulting in variable yield increases (AYL values) compared with untreated: highest (up to 50.64%) for TLB, intermediate (15.91%-24.73%) for MLB and lowest (around 16%) for CLS. AYL values were higher for the two systemic diseases, SDM (> 88%) and RDM (58.48%), following seed treatment. Among the different diseases assessed, SDM recorded the highest yield response to fungicide treatment, underlining the crucial role of early disease management in high-risk locations. These findings underscore the critical role of fungicides in integrated disease management and provide valuable, location-specific insights for stakeholders seeking to mitigate the impacts of foliar diseases and enhance maize productivity in India.

Maize is one of the most versatile and commercially produced crops used for food, feed, fodder, ethanol, oil, and industrial raw materials. Maize is affected by various diseases, but among these, maydis leaf blight (MLB) is one of the most serious diseases. The disease is caused by Cochliobolus heterostrophus and is responsible for yield losses up to 40%. When developing cultivars for a specific ecology, days to flowering and maturity are important breeding traits to consider. Thus, understanding the genetic basis of MLB resistance, specifically the “O” race of the pathogen, and maturity-related traits is crucial to develop climate-resilient maize hybrids. This study aimed to determine the gene actions and their interactions for MLB resistance and maturity-related traits using a six-parameter model (P1, P2, F1, BC1P1, BC1P2, and F2). Five experimental crosses were attended using resistant (R) (CML269-1 and P72c1Xbrasil1177-2) and susceptible (S) (HKIPC4B and ESM113) lines in R×S (1), S×R (2), R×R (1), and S×S (1) combinations. The susceptible lines belonged to the early (HKIPC4B) and medium (ESM113) maturity groups, while the resistant lines belonged to the medium (CML269-1) and late (P72c1Xbrasil1177-2) maturity groups. These six genetic populations were screened under artificially created epiphytotic conditions at a hot-spot site. In the analysis, MLB resistance showed a dominance genetic effect with significant (P<0.01) additive × additive interactions. Maturity-related traits showed significant dominance genetic effects (P< 0.01), with dominance × dominance interactions, suggesting the suitability of hybrid breeding for these traits. The estimated genes responsible for MLB resistance ranged from 0.002 to 5.78 per cross. In MLB resistance, broad and narrow-sense heritability were found to be 91.9% and 84.3%, respectively, which indicated the possibility of genetic improvement through selection. Disease response and maturity-related traits were negatively correlated, suggesting that long-duration genotypes are more resistant to disease than short-duration. The detailed understating of gene actions can aid in designing breeding strategies to develop resistant cultivars with the required duration for various stress-prone ecologies.

Modern technologies such as Unmanned Aerial Vehicles (UAVs), Artificial Intelligence (AI), and Machine Learning (ML) are revolutionizing precision agriculture. This paper is related to the classification of UAV based maize crop images as soil and leaf using color space and ML models. A standardized method for quantitatively representing and manipulating colors numerically, color space models such as HSV (Hue, Saturation, Value) and Grayscale are fundamental to both image processing and computer vision. In this research work, HSV and Grayscale color spaces are specifically taken into consideration to determine the amount of green color present in an image. Color space models use high-resolution UAV photos of maize crops that have been further subdivided into low-resolution sub-images. Numerical datasets are then generated for two classes of images, namely leaf and soil, which are further used to train ML models such as Logistic Regression with L2 Regularization, Support Vector Machine (SVM), and Naive Bayes (NB). Compared to SVM and NB, Logistic Regression with L2 Regularization achieves 99 percent test accuracy.

Maize is a highly versatile crop holding significant importance in global food, feed and nutritional security. Grain yield is a complex trait and difficult to improve without targeting the improvement of grain yield attributing traits, which are relatively less complex in nature. Hence, considering the erosion in genetic diversity, there is an urgent need to use wild relatives for genetic diversification and unravel the genomic regions for grain yield attributing traits in maize. Thus, the current study aimed to identify quantitative trait loci (QTLs) linked with grain yield and yield attributing traits. Two BC2F2 populations developed from the cross of LM13 with Zea parviglumis (population 1) and LM14 with Zea parviglumis (population 2) were genotyped and phenotyped in field conditions in the kharif season. BC2F2:3 lines in both populations were phenotyped again for grain yield and attributing traits in the spring season. In total, three QTLs each for ear height (EH), two QTLs for flag leaf length (FLL) and one QTL each for ear diameter (ED), plant height, flag leaf length (FLL), flag leaf width and 100 kernel-weight were identified in population 1. In population 2, two QTLs for kernel row per ear (KRPE) and one QTL for FLL were detected in. QTLs for EH, FLL and KPRE showed consistency across seasons. Among the identified QTLs, six QTLs were found to be co-localized near identified genomic regions in previous studies, validating their potential in contributing to trait expression. The identified QTLs can be utilized for marker assisted selection, transferring favorable alleles from wild relatives in modern maize.

Precision agriculture is undergoing revolution by the integration of modern technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Artificial Intelligence (AI) and Machine Learning (ML). In this paper, high resolution UAV images of maize crops is used and further split into low resolution sub-images for training Convolutional Neural Network (CNN) models. The paper work provides an practical approach of applying an lightweight custom CNN architecture on UAV images to analyse the maize crops health status by classifying them into healthy crops, Maydis Leaf Blight (MLB) disease crops and also weeds and soil back ground images. The proposed custom CNN model shows better performance by achieving 95% accuracy when compared to other considered CNN pre-trained models such as MobileNet-V2 and ResNet-50.

Maydis leaf blight (MLB) is a prevalent disease, caused by the necrotrophic plant pathogen Bipolaris maydis (Nisikado and Miyake), affecting maize worldwide. Depending on environmental conditions, MLB can lead to yield losses of up to 40% or more. The existing management approach of chemical disease control is expensive and unsustainable. Hence the need to evaluate an integrated approach of chemical and biocontrol/botanical agents for its sustainable management. This study aimed to assess the efficacy of three management modules namely organic, chemical, and integrated disease management (IDM) against this disease in maize. The effectiveness of three modules was tested at three hot spot locations (Ludhiana, Karnal, and Delhi), during 2019 and 2020. The chemical module was most effective in controlling the disease followed by the IDM module, with control rates of 54.16% and 45.87% in Ludhiana and 52.92% and 44.69% in Karnal, respectively. Conversely, the organic module showed the lowest effectiveness. Notably, at the Delhi location, the standard control (foliar spray with Mancozeb 75WP@ 2.5 g/l water) proved most effective, achieving a disease control percentage of 64.29%, followed by the IDM module at 50.00%. The chemical module exhibited the highest increase in yield at Ludhiana (86.47%) and Karnal (52.92%), compared to other treatments. Overall, based on location-wise averages, the chemical module gave the highest mean percent disease control at 52.36% and mean percent yield increase at 49.18%. This study emphasizes the benefits of integrated disease management and underscores the enhanced efficacy of chemicals when compared to the positive control.

Maydis leaf blight (MLB) is a prevalent disease affecting maize worldwide, caused by the necrotrophic plant pathogen Bipolaris maydis (Nisikado and Miyake). Depending on environmental conditions, MLB can lead to yield losses of up to 40% or more. To combat this disease, various chemical and biocontrol/botanical agents have been developed and proven effective. This study aimed to assess the efficacy of different combinations of disease management components as an alternative approach. The effectiveness of three modules, namely organic, chemical, and IDM, was tested in hot spot locations, namely Ludhiana, Karnal, and Delhi, during 2019 and 2020. Results indicated that the chemical module demonstrated superior disease control, achieving percentages of 54.16 and 52.92 at Ludhiana and Karnal, respectively. The IDM module also showed promising results, with disease control percentages of 45.87 and 44.69 at Ludhiana and Karnal, respectively. Conversely, the organic module exhibited the least effectiveness. Notably, at the Delhi location, the standard control (Foliar spray with Mancozeb 75 WP @ 2.5 g / l water) proved most effective, achieving a disease control percentage of 64.29, followed by the IDM module at 50.00. The chemical module exhibited the highest percent increase in yield (PIY), with figures of 86.47 and 52.92 at Ludhiana and Karnal, respectively, compared to other treatments. This study highlights the superior efficacy of the chemical and IDM modules in comparison to the positive control (check). Consequently, these modules present alternative strategies for effectively managing MLB.

Banded leaf and sheath blight (BLSB) of maize ( Zea mays L.) is caused by most widespread and destructive pathogen Rhizoctonia solani f. sp. sasakii . The disease is difficult to manage through fungicides or crop rotation alone due to its soil-borne nature and unavailability of host resistance sources. Four modules namely one chemical, two organic and one integrated disease management (IDM) modules were evaluated at four hot spot locations viz., Ludhiana, Karnal, Delhi and Pantnagar during Kharif 2018 and 2019. Mean disease severity of BLSB was found significantly low in all the treatments compared to positive control across the locations. The chemical module showed the highest percent disease control (PDC) in Ludhiana and Pantnagar while the IDM module showed the highest percent disease control (PDC) in Karnal and Delhi. The Benefit-cost (B:C) ratio was the highest in the chemical module at Ludhiana (1.35) and Pantnagar (1.11) while standard control showed the highest B:C ratio at Delhi (1.72) and Karnal (0.84) over the control. It is concluded that effectiveness of modules varied among the locations due to weather and soil conditions. Based on the B:C ratio of different treatments, the specific module had been identified to manage BLSB location wise. Chemical and IDM were effective at all the locations to manage the BLSB disease.

Soybean is one of the largest sources of protein and oil in the world and is also considered a “super crop” due to several industrial advantages. However, enhanced acreage and adoption of monoculture practices rendered the crop vulnerable to several diseases. Phytophthora root and stem rot (PRSR) caused by Phytophthora sojae is one of the most prevalent diseases adversely affecting soybean production globally. Deployment of genetic resistance is the most sustainable approach for avoiding yield losses due to this disease. PRSR resistance is complex in nature and difficult to address by conventional breeding alone. Genetic mapping through a cost-effective sequencing platform facilitates identification of candidate genes and associated molecular markers for genetic improvement against PRSR. Furthermore, with the help of novel genomic approaches, identification and functional characterization of Rps (resistance to Phytophthora sojae) have also progressed in the recent past, and more than 30 Rps genes imparting complete resistance to different PRSR pathotypes have been reported. In addition, many genomic regions imparting partial resistance have also been identified. Furthermore, the adoption of emerging approaches like genome editing, genomic-assisted breeding, and genomic selection can assist in the functional characterization of novel genes and their rapid introgression for PRSR resistance. Hence, in the near future, soybean growers will likely witness an increase in production by adopting PRSR-resistant cultivars. This review highlights the progress made in deciphering the genetic architecture of PRSR resistance, genomic advances, and future perspectives for the deployment of PRSR resistance in soybean for the sustainable management of PRSR disease.

Maize is a global principal crop, after wheat and rice. Maydis leaf blight (MLB), also known as Southern Corn Leaf Blight (SCLB), caused by Cochliobolus heterostrophus is a massive foliar disease in maize of fungal origin and prevalent in warm (20-30 ?C), humid (> 80%) temperate to tropical regions of the world. It has noted signification in the agricultural history due to its epidemic propositions in 1970 in the United States. At present, the primary form of C. heterostrophus is Race ' O ', which can cause around 40% yield losses in maize. Symptoms appear as small lesions with a dark brown margin and straw to light brown coloured center to absolute foliage blight. In spite of much availability of management practices, the use of host-plant resistance together with other integrated disease management practices may contribute effectively to the sustainable management of diseases. Both, qualitative and quantitative resistances have been reported in the literature, but quantitative is preferable over qualitative one, as it is non-race specific and highly durable. The different quantitative trait loci (QTLs) identified for MLB resistance have been mapped on chromosomes 2, 3, 4, 6, 8, and 9. Considering MLB as an important disease in India, we recommend confirming these findings and identifying new genomic regions using Indian maize germplasm as well. The progress made in the area of genomics may revolutionize the understanding of interactions between host and pathogen, and the identification and deployment of MLB resistance genes in the breeding programme.

Improving crop resistance against insect pests is crucial for ensuring future food security. Integrating genomics with modern breeding methods holds enormous potential in dissecting the genetic architecture of this complex trait and accelerating crop improvement. Insect resistance in crops has been a major research objective in several crop improvement programs. However, the use of conventional breeding methods to develop high-yielding cultivars with sustainable and durable insect pest resistance has been largely unsuccessful. The use of molecular markers for identification and deployment of insect resistance quantitative trait loci (QTLs) can fastrack traditional breeding methods. Till date, several QTLs for insect pest resistance have been identified in field-grown crops, and a few of them have been cloned by positional cloning approaches. Genome editing technologies, such as CRISPR/Cas9, are paving the way to tailor insect pest resistance loci for designing crops for the future. Here, we provide an overview of diverse defense mechanisms exerted by plants in response to insect pest attack, and review recent advances in genomics research and genetic improvements for insect pest resistance in major field crops. Finally, we discuss the scope for genomic breeding strategies to develop more durable insect pest resistant crops.

Maydis leaf blight (MLB) resistance and days to flowering are the important yield determining traits in maize. Breeding for MLB resistance and days to flowering can be accelerated by understanding their genetics and identifying genomic regions contributing for their expression. Two F2s population with 338 and 349 individuals along with their recombinants inbred lines (RILs) having 283 and 277 individuals were developed from F1 crosses HKIPC4P × CML269 and ESM113 × P72clXbrasil1117 for genetic studies of MLB resistance and flowering. The populations along with their parents were screened under artificially inoculated conditions at hot-spot sites during 2015–17. Race O inoculum was artificially inoculated in the leaf whorl of each plant at 4-6 leaf stage. The inoculation was repeated after 8-10 days of first inoculation to avoid any chance of disease escape. The partial dominance in F1s, normal distribution patterns in F2s and RILs for both the traits has indicated their polygenic nature. Correlation analysis found negative and significant association (P≤0.001) between disease scores and days to flowering across the populations. Total 250 simple sequence repeats (SSR) markers, uniformly selected from all linkage groups were used for parental polymorphism survey between parents of the populations contrasting for target traits. Of total 250 SSRs, 122 (48.8% polymorphism) were identified as polymorphic between either of the parents. Sufficient genetic variation was observed within and between different F2s and RILs mapping populations. The information on inheritance, parental polymorphism survey and genetic materials developed will be useful for fine mapping and systematic breeding of targeted traits in tropical maize germplasm.

Banded leaf and sheath blight disease caused by Rhizoctonia solani f. sp. sasakii is a major constraint of Kharif maize. Wide host range of pathogen, its ability to survive as sclerotia under adverse environmental conditions and lack of resistant sources are some of the bottlenecks in its management. To reduce our dependence on chemicals, experiment was conducted at five hot-spots in India viz., Ludhiana (PAU and Ladhowal), Delhi, Karnal and Pantnagar centres to study the effect of leaf stripping method on disease severity and yield parameters of present day maize hybrids of different maturity groups as well as speciality corn cultivars. Per cent disease control achieved with leaf stripping treatment in different cultivars varied from 16.66 to 54.76% being highest at PAU, Ludhiana centre and lowest at Pantnagar centre. Maximum percent increase in yield was observed at Delhi centre (28.37%) closely followed by PAU, Ludhiana centre (28.23%). Positive correlation (r) was observed between mean per cent disease control and mean percent increase in yield.

The ever-rising population of the twenty-first century together with the prevailing challenges, such as deteriorating quality of arable land and water, has placed a big challenge for plant breeders to satisfy human needs for food under erratic weather patterns. Rice, wheat, and maize are the major staple crops consumed globally. Drought, waterlogging, heat, salinity, and mineral toxicity are the key abiotic stresses drastically affecting crop yield. Conventional plant breeding approaches towards abiotic stress tolerance have gained success to limited extent, due to the complex (multigenic) nature of these stresses. Progress in breeding climate-resilient crop plants has gained momentum in the last decade, due to improved understanding of the physiochemical and molecular basis of various stresses. A good number of genes have been characterized for adaptation to various stresses. In the era of novel molecular markers, mapping of QTLs has emerged as viable solution for breeding crops tolerant to abiotic stresses. Therefore, molecular breeding-based development and deployment of high-yielding climate-resilient crop cultivars together with climate-smart agricultural practices can pave the path to enhanced crop yields for smallholder farmers in areas vulnerable to the climate change. Advances in fine mapping and expression studies integrated with cheaper prices offer new avenues for the plant breeders engaged in climate-resilient plant breeding, and thereby, hope persists to ensure food security in the era of climate change.