Maize productivity is increasingly constrained by water deficit stress (WDS), particularly under erratic rainfall conditions. Efficient early-stage phenotyping coupled with field validation is critical for breeding WDS-tolerant genotypes. In this study, we developed a two-tier screening strategy integrating hydroponics-based root trait evaluation at pre-reproductive stage with subsequent field validation of maize inbreds under managed WDS at CIMMYT, Hyderabad. A set of 50 diverse maize inbreds were evaluated for root architectural traits and plant growth stages including grain yield components. Hydroponic screening applied PEG6000-induced osmotic stress to assess root length, tips, forks, segments and diameter, whereas field trials imposed pre-reproductive WDS through cumulative growing degree day-based irrigation withdrawal. Significant genotypic variation and genotype × trait interactions were observed across both environments, reflecting trait and environment-specific responses. Key root traits, including root tips, total length, forks and segments, showed strong positive correlations (r ≥ 0.70) with yield components and Normalized difference vegetation index (NDVI), underscoring their importance in WDS resilience. Multivariate analysis further confirmed the alignment of root vigor with kernel traits and canopy health as critical determinants of yield stability. Among the evaluated lines, introgressed ILM23 and ILM24 emerged as the principal donor lines, while PML1249, PML1275, and PML1285 were identified as promising donor sources, all exhibiting robust root systems, stable anthesis-silking interval (ASI) and superior stress tolerance indices. Spearman’s rank correlation (ρ = 0.988) between hydroponics and field rankings highlighted the predictive reliability of controlled root phenotyping for field performance under WDS. This integrated hydroponics-to-field approach provides a rapid, efficient and cost-effective framework for the early identification of WDS-tolerant or high water-use-efficiency (WUE) maize hybrids, facilitating the accelerated breeding of resilient cultivars.

MAIZE cultivation is widespread throughout the world under a diverse array of climates. These diverse growing habits pose myriad biotic and abiotic threats to successful maize production. Waterlogging (WL) stress, the second most important abiotic constraint after drought, leads to around 25%-30% of production losses each year. Effective identification of suitable donor lines with tolerance against WL and using it in breeding programmes is the main concern for the breeders. The present study focused on the effective and early identification of maize genotypes tolerant to WL based on root traits. For this, 120 maize inbred lines along with Zea mays ssp. parviglumis from different genetic origin were evaluated for WL tolerance both at pre-emergence as well as the seedling stage at different WL durations, namely, 3, 6 and 9 days to flooding. Results based on percentage germination at pre-emergence and percentage survival at the seedling stage reveal that germination tolerance is independent of seedling stage tolerance. The chlorophyll content exhibited that the tolerant lines had almost similar levels of chlorophyll at 3 days of flooding with control and subsequent decline recorded in 6 and 9 days. The least reduction in chlorophyll content at 9 days of flooding was found in EML 285. Waterlogging tolerance coefficient (WTC) based on root and shoot traits is higher in tolerant lines than in susceptible ones. Root traits are foremost affected by WL stress and plants cope with such stress by having adaptive mechanisms against it; therefore, the identified lines were further revalidated by the radial oxygen loss (ROL) barrier test, which is an adaptive response under WL stress, which confirmed the line identified under WTC based on root traits, namely, I 185, I 172, LM 16 and teosinte (check), having more than 90% barrier formation corresponds more to the formation of a ROL barrier. These identified lines can be used as parents in hybrid development or deriving the WL-tolerant lines.

India’s North Western Zone is striving to diversify its traditional rice-wheat cropping system by promoting maize as a viable alternative to rice. However, maize cultivation in this region faces a significant challenge due to monsoon-induced waterlogging (WL), particularly in compacted soils formed by puddling during rice cultivation. To address this issue, the present study aimed to identify genomic regions associated with WL tolerance using a mapping population of 154 F2:3 lines derived from a cross between a WL-tolerant maize inbred line (I 185) and a WL-susceptible line (SE 565 A). The population was genotyped using SSR markers, which resulted in a genetic linkage map spanning 1950.44 cM with an average marker interval of 21.67 cM. The F2:3 families, along with control checks, were evaluated under artificially imposed WL conditions at the knee-high stage for six days during the kharif season. Phenotypic data were collected for cob yield and key physiological and morphological traits. A total of 15 quantitative trait loci (QTLs) were identified with a LOD threshold ≥ 3.5 and phenotypic variance explained (PVE) ≥ 4

Climate change has increased the frequency of abiotic stresses, such as waterlogging (WL), caused by heavy, unpredictable rainfall in compacted soils that adversely affects the growth and yield of maize (Zea mays L.). To breed WL-tolerant maize hybrid, understanding genetic variability for WL tolerance traits in source germplasm is critical. The experiment was conducted during rainy (kharif) season 2023 at Punjab Agricultural University, Ludhiana, Punjab in F2:3 maize populations derived by crossing WL tolerant and susceptible parent for WL tolerance under pot and field conditions. Physiological, root, and yield-related traits were assessed after WL stress at the V3–5 stage for six days. Experiment I identified root dry weight, shoot dry weight, root surface area, and root diameter as promising traits due to high heritability (h2) and genetic advance, suggesting their utility in breeding WL-tolerant lines. Chlorophyll content before (CCBT-P) and after (CCAT-P) treatment showed low heritability, requiring further studies. In Experiment II, yield-related traits like ear height, plant height, and ear yield exhibited moderate to high heritability, making them suitable for selection. The findings highlight the importance of prioritising high-heritability traits for selection and fixing superior lines through continuous selfing. This approach can aid in developing WL tolerant maize hybrids, enhancing productivity in WL prone regions of northern India and supporting sustainable maize farming amid increasing climate-induced abiotic stresses.

Fall armyworm (FAW), a latest introduce pest in India during 2018, is a highly destructive invasive pest of maize (Zea mays L.). It threatens to reduce maize productivity by around 80

Waterlogging (WL) is an important abiotic stress, severely affecting plant growth and development, inhibiting root respiration and degradation of chlorophyll, senescence of leaves and chlorosis leading to substantial yield loss. These intensities of yield losses generally depend on the duration of WL and crop growth stages. Maize being a dry land crop is particularly sensitive to WL. Systematic screening techniques to identify parameters linked with tolerance are not well established which serves as a major bottleneck in the identification of promising genotypes. In this study, 120 maize inbred lines belonging to diverse genetic backgrounds were evaluated for WL tolerance both at pre-emergence as well as the seedling stage. Results based on percentage germination at pre-emergence and percentage survival at the seedling stage under WL established that pre-germination tolerance is independent of seedling stage tolerance. Membership function value based on WL tolerance coefficient of shoot and root fresh weights, dry weights, lengths, root surface area, shoot area and root volume was used to identify tolerant lines. Established mathematical models were used and identified root dry weight as a single reliable parameter to judge the tolerance level of genotypes. The use of BLPSI and ESIM selection indices as well as MTSI to judge the stability as well as genetic worth of genotypes further strengthens the selection efficiency. Lines thus performing best across all the models included I 185, I 172 and SE 503 and were identified as tolerant lines for WL. A combination of these different selection approaches would further strengthen selection efficiency and is believed to be a rapid and effective selection approach.

To diversify the traditional rice–wheat cropping system, maize is promoted as a potential alternative to rice. However, waterlogging (WL) during the kharif season, especially in soils compacted due to rice puddling, poses a significant challenge to maize cultivation. This study aimed to identify genomic regions associated with WL tolerance in maize using an F₂:₃ population derived from a cross between a WL-tolerant line (I 185) and a WL-susceptible line (SE 565A). A total of 154 F2:3 lines were evaluated under controlled WL conditions at the V3–V5 growth stage, with physiological and root traits recorded after WL stress. A total of 21 QTLs associated with key traits under WL stress were identified at a LOD threshold of ≥ 3 and a phenotypic variance explained (PVE) of ≥ 10

Hybrid breeding program in maize is dependent on the availability of heterotic inbred lines for full exploitation of heterosis. Identification of heterotic patterns among inbred lines is prerequisite for obtaining high yielding superior hybrids. A total of 156 inbred lines were evaluated thoroughly, out of which, a set of 25 promising parental inbred lines, derived from different heterotic pools, semi exotic and indigenous lines, were evaluated at Ludhiana for two years. Based on their reproductive synchronization and pre-determined heterotic grouping, a total of 25 hybrids have been developed. Hybrids thus developed, were evaluated for eight yield related traits for two years at two locations (Ludhiana and Gurdaspur). Highly significant differences have been observed among the inbred lines for almost all of the traits. Likewise, high significant differences have also been observed in hybrids for both years at both locations. Yield was exhibiting significant but negative correlation with days to 50

Quantitative genetics and plant breeding are non-exclusive and these disciplines has been benefited from each other for the past 100 years. As a matter of fact, the majority of economically significant traits in crops and livestock species are quantitative in nature rather than qualitative. Several biometricians have made a significant contribution to understand how quantitative genetics works. Different methods of plant breeding had evolved, and are still evolving. Traditional plant breeding methods emphasize the components of quantitative variance but accuracy, time, and effectiveness are the three key factors that conventional breeding faces while trying to decode this. High throughput next-generation sequencing has made it easier, more accurate, and more exact to understand the genetics of complex traits in a shorter time, which has also shortened the breeding cycles required for desired genetic gain. This book chapter mostly focused on the statistical tools that plant breeders used in their research to dissect the genetics of complex traits that are significant economically including both traditional and advance plant breeding methods.

Maize holds significant economic importance as a cereal crop on a global scale. Among several abiotic stresses, waterlogging poses a substantial challenge in attainment of potential crop yield. To recognize inbred lines that exhibit resilience to waterlogging, it is crucial to gain insights into the fundamental mechanisms and effects of waterlogging stress on various morphological, physiological and biochemical traits. The present study was carried out to identify waterlogging tolerant inbred lines using a set of 86 inbred lines for six and nine days of water logging stress at V 3-4 stage along with control. The results indicate that under increasing waterlogging stress, notable decrease in germination percentage, chlorophyll content and root traits viz., root length, root area, and root volume were observed. However, in the case of tolerant genotypes, the percentage reduction in these traits compared to the control was lower than in the susceptible ones. Both fresh and dry weights of roots and shoots exhibited a reduction compared to control; however, the tolerant genotypes displayed the least reduction, while the susceptible genotypes experienced a sharp reduction. Also, the chlorophyll content experienced the least reduction in tolerant genotypes as waterlogging stress increased. To validate the identified lines, a subset of 13 lines shown to be tolerant or susceptible were selected based on various experiments performed and then these lines were subjected to biochemical analysis viz., superoxide dismutase, catalase, peroxidase, ascorbic acid and tocopherol content. Tolerant genotypes viz., I 185, I 172 and SE 616 exhibited higher enzyme activity and antioxidant content, compared to susceptible genotypes.

Root system architecture (RSA), also known as root morphology, is critical in plant acquisition of soil resources, plant growth, and yield formation. Many QTLs associated with RSA or root traits in maize have been identified using several bi-parental populations, particularly in response to various environmental factors. In the present study, a meta-analysis of QTLs associated with root traits was performed in maize using 917 QTLs retrieved from 43 mapping studies published from 1998 to 2020. A total of 631 QTLs were projected onto a consensus map involving 19,714 markers, which led to the prediction of 68 meta-QTLs (MQTLs). Among these 68 MQTLs, 36 MQTLs were validated with the marker-trait associations available from previous genome-wide association studies for root traits. The use of comparative genomics approaches revealed several gene models conserved among the maize, sorghum, and rice genomes. Among the conserved genomic regions, the ortho-MQTL analysis uncovered 20 maize MQTLs syntenic to 27 rice MQTLs for root traits. Functional analysis of some high-confidence MQTL regions revealed 442 gene models, which were then subjected to in silico expression analysis, yielding 235 gene models with significant expression in various tissues. Furthermore, 16 known genes viz., DXS2, PHT, RTP1, TUA4, YUC3, YUC6, RTCS1, NSA1, EIN2, NHX1, CPPS4, BIGE1, RCP1, SKUS13, YUC5, and AW330564 associated with various root traits were present within or near the MQTL regions. These results could aid in QTL cloning and pyramiding in developing new maize varieties with specific root architecture for proper plant growth and development under optimum and abiotic stress conditions.

The decline in tropical maize productivity due to climatic vulnerability is a matter of serious concern as being a food and feed/fodder commodity, it is an important crop for the sustenance of human life. Genetic selections and development of water deficit stress (WDS) tolerant commercial varieties have potential to offset the impact of changing temperatures and precipitation. For trait-specific genetic enhancement, there is a need to understand a suite of adaptation strategies for crop. We studied the response of various shoot and root traits in 71 maize inbreds of diverse origin under simulated sub-optimal water supply controlled conditions, delineated an array of traits which must be considered for selection for WDS and validated the inbreds harbouring tolerance to WDS for selection of authentic donor lines to develop WDS tolerant hybrids. A large data set was limited to uncorrelated traits based on principal component analysis and variability among maize lines was deciphered using heatmap dendrogram. We also reported the relevance of root anatomical plasticity to the inherent potential of lines to combat WDS. We recommend incorporating the changes in number and diameter of xylem and metaxylem under simulated controlled conditions as a part of precise phenotyping for WDS in maize. The study led to identification of WDS tolerant line LM22 in maize.

Genetic improvement for nitrogen use efficiency (NUE) can play a very crucial role in sustainable agriculture. Root traits have hardly been explored in major wheat breeding programs, more so in spring germplasm, largely because of the difficulty in their scoring. A total of 175 advanced/improved Indian spring wheat genotypes were screened for root traits and nitrogen uptake and nitrogen utilization at varying nitrogen levels in hydroponic conditions to dissect the complex NUE trait into its component traits and to study the extent of variability that exists for those traits in Indian germplasm. Analysis of genetic variance showed a considerable amount of genetic variability for nitrogen uptake efficiency (NUpE), nitrogen utilization efficiency (NUtE), and most of the root and shoot traits. Improved spring wheat breeding lines were found to have very large variability for maximum root length (MRL) and root dry weights (RDW) with strong genetic advance. In contrast to high nitrogen (HN), a low nitrogen (LN) environment was more effective in differentiating wheat genotypes for NUE and its component traits. Shoot dry weight (SDW), RDW, MRL, and NUpE were found to have a strong association with NUE. Further study revealed the role of root surface area (RSA) and total root length (TRL) in RDW formation as well as in nitrogen uptake and therefore can be targeted for selection to further the genetic gain for grain yield under high input or sustainable agriculture under limited inputs.

Human face recognition is distinguished by a method of identifying facts or confirmation that tests personality. The technique essentially relies on two stages, one is face identification, and another is face recognition. Facial recognition applies to a PC device with a few implementations in which human faces can be identified in pictures. Usually, facial identification is achieved by using "right" data from full-frontal facial photographs. Although there are a variety of situations in which full frontal faces are not visible, blemished faces captured by CCTV cameras are an excellent demonstration. Subsequently, the use of fractional facial data as tests is still, to a large extent, an unexplored field of research on the PC-based face recognition problem. In this research, through using incomplete facial evidence to concentrate on face recognition. By implementing critical analysis to evaluate the presentation of AI using the Haar Cascade Classifier is proposed and used to build our framework. There are three phases of the proposed face detection method such as the face data gathering (FDG) process, train the stored image (TSI) phase, face recognition using the local (FRUL) binary patterns histograms (LBPH) algorithm, and this classifier computation was tested by splitting it into four phases. In this analysis, Haar feature selection is applied to complete the detection phase, and also to generate an integral image, Adaboost preparing, Cascading Classifiers. To complete this venture's human protection facial recognition framework with face detection, local binary patterns histograms (LBPH) is used to estimate the model. In LBPH, a few parameters are used and a dataset is obtained by implementing an algorithm. By adding the LBPH operation and extracting the histograms, I got the Final computational part. "Image Processing Based Human Face Recognition Using Haar Cascade Classifier" Image Processing-Based Human Face Recognition Using Haar Cascade Classifier.

A Genome-wide association (GWAS) study was conducted for phosphorous (P)-use responsive physiological traits in bread wheat at the seedling stage under contrasting P regimes. A panel of 158 diverse advanced breeding lines and released varieties, and a set of 10,800 filtered single nucleotide polymorphism (SNP) markers were used to study marker-trait associations over the eight shoot traits. Principle component analysis separated the two environments (P regimes) because of the differential response of the traits indicating the essentiality of the separate breeding programmes for each environment. Significant variations for genotypic, environmental, and genotype × environment (GEI) effects were observed for all the traits in the combined analysis of variance with moderately high broad sense heritability traits (0.50–0.73). With the different algorithms of association mapping viz., BLINK, FarmCPU, and MLM, 38 unique QTLs under non-limiting P (NLP) and 45 QTLs for limiting P (LP) conditions for various shoot traits were identified. Some of these QTLs were captured by all three algorithms. Interestingly, a Q.iari.dt.sdw.1 on chromosome 1D was found to explain the significant variations in three important physiological traits under non-limiting phosphorus (NLP) conditions. We identified the putative candidate genes for QTLs namely Q.iari.dt.chl.1, Q.iari.dt.sdw.16, Q.iari.dt.sdw.9 and Q.iari.dt.tpc.1 which are potentially involved in the mechanism regulating phosphorus use efficiency through improved P absorption due to improved root architectural traits and better mobilization such as sulfotransferase involved in postembryonic root development, WALLS ARE THIN1 (WAT1), a plant-specific protein that facilitates auxin export; lectin receptor-like kinase essentially involved in plant development, stress response during germination and lateral root development and F-box component of the SKP-Cullin-F box E3 ubiquitin ligase complex and strigolactone signal perception. Expression profiling of putative genes located in identified genomic regions against the wheat expression atlas revealed their significance based on the expression of these genes for stress response and growth development processes in wheat. Our results thus provide an important insight into understanding the genetic basis for improving PUE under phosphorus stress conditions and can shape the future breeding programme by developing and integrating molecular markers for these difficult-to-score important traits.

Data is the most valuable resource in the present. Classifying the data and using the classified data to make a decision holds the highest priority. Computers are trained to manage the data automatically using machine learning algorithms and making judgments as outputs. Several data mining algorithms can be obtained for Artificial Neural Network classification, Nearest Neighbor Law and Baysen classifiers, but the decision tree mining is most commonly used. Data can be classified easily using the decision tree classification learning process. It’s trained on a training dataset and then implemented on a test set from which a result is expected. There are three decision trees (ID3 C4.5 and CART) that are extensively used. The algorithms are all based on Hut’s algorithm. This paper focuses on the difference between the working processes, significance, and accuracy of the three (ID3 C4.5 and CART) algorithms. Comparative analysis among the algorithms is illustrated as well.

Mostly in the agriculture sector, identifying rotten fruits has been critical. The classification of fresh and rotting fruits is typically carried out by humans, which is ineffective for fruit growers. Humans wear out by doing the same role many days, but robots do not. As a result, the study proposed a method for reducing human effort, lowering production costs, and shortening production time by detecting defects in agricultural fruits. If the defects are not detected, the contaminated fruits can contaminate the good fruits. As a result, we proposed a model to prevent the propagation of rottenness. From the input fruit images, the proposed model classifies the fresh and rotting fruits. We utilized three different varieties of fruits in this project: apple, banana, and oranges. The features from input fruit images are collected using a Convolutional Neural Network, and the images are categorized using Max pooling, Average pooling, and MobileNetV2 architecture. The proposed model's performance is tested on a Kaggle dataset, and it achieves the highest accuracy in training data is 99.46% and in the validation set is 99.61% by applying MobileNetV2. The Max pooling achieved 94.49% training accuracy and validation accuracy is 94.97%. Besides, the Average pooling achieved 93.06% training accuracy and validation accuracy is 93.72%. The findings revealed that the proposed CNN model is capable of distinguishing between fresh and rotting fruits.