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Real-Time Plant Health Detection Using Deep Convolutional Neural Networks
In the twenty-first century, machine learning is a significant part of daily life for everyone. Today, it is adopted in many different applications, such as object recognition, object classification, and medical purposes. This research aimed to use deep convolutional neural networks for the real-time detection of diseases in plant leaves. Typically, farmers are unaware of diseases on plant leaves and adopt manual disease detection methods. Their production often decreases as the virus spreads. However, due to a lack of essential infrastructure, quick identification needs to be improved in many regions of the world. It is now feasible to diagnose diseases using mobile devices as a result of the increase in mobile phone usage globally and recent advancements in computer vision due to deep learning. To conduct this research, firstly, a dataset was created that contained images of money plant leaves that had been split into two primary categories, specifically (i) healthy and (ii) unhealthy. This research collected thousands of images in a controlled environment and used a public dataset with exact dimensions. The next step was to train a deep model to identify healthy and unhealthy leaves. Our trained YOLOv5 model was applied to determine the spots on the exclusive and public datasets. This research quickly and accurately identified even a small patch of disease with the help of YOLOv5. It captured the entire image in one shot and forecasted adjacent boxes and class certainty. A random dataset image served as the model’s input via a cell phone. This research is beneficial for farmers since it allows them to recognize diseased leaves as soon as they noted and take the necessary precautions to halt the disease’s spread. This research aimed to provide the best hyper-parameters for classifying and detecting the healthy and unhealthy parts of leaves in exclusive and public datasets. Our trained YOLOv5 model achieves 93 % accuracy on a test set.
Physiological and Antioxidative Effects of Strontium Oxide Nanoparticles on Wheat
We explored the impact of strontium oxide nanoparticles (SrO-NPs), synthesized through a green method, on seedling growth of bread wheat in hydroponic systems. The wheat plants were exposed to SrO-NPs concentrations ranging from 0.5 mM to 8.0 mM. Various parameters, including shoot length (cm), shoot fresh weight (g), root number, root length (cm), root fresh weight (g), chlorophyll value (SPAD), cell membrane damage (%), hydrogen peroxide (H2O2) value (µmol/g), malondialdehyde (MDA) value (ng/µL), and enzymatic activities like ascorbate peroxidase (APX) activity (EU/g FW), peroxidase (POD) activity (EU/g FW), and superoxide dismutase (SOD) activity (U/g FW), were measured to assess the effects of SrO-NPs on the wheat plants in hydroponic conditions. The results showed that the SrO-NPs in different concentrations were significantly affected considering all traits. The highest values were obtained from the shoot length (20.77 cm; 0.5 mM), shoot fresh weight (0.184 g; 1 mM), root number (5.39; 8 mM), root length (19.69 cm; 0 mM), root fresh weight (0.142 g; 1 mM), SPAD (33.20; 4 mM), cell membrane damage (58.86%; 4 mM), H2O2 (829.95 µmol/g; 6 mM), MDA (0.66 ng/µl; 8 mM), APX (3.83 U/g FW; 6 mM), POD (70.27 U/g FW; 1.50 mM), and SOD (60.77 U/g FW; 8 mM). The data unequivocally supports the effectiveness of SrO-NPs application in promoting shoot and root development, chlorophyll levels, cellular tolerance, and the activation of enzymes in wheat plants.
Ethyl Methanesulfonate (EMS) Mutagen Toxicity-Induced DNA Damage, Cytosine Methylation Alteration, and iPBS-Retrotransposon Polymorphisms in Wheat (Triticum aestivum L.)
The use of mutagens in plant breeding is used to create new germplasm, increase agricultural yield, quality, and resistance to diseases and pests. Mutagens are physical or chemical factors that can alter the DNA or RNA structure of an organism, causing mutations above the expected level. One of the most common and potent chemical mutagens is EMS (ethyl-methane sulfonate), which produces point mutations in plants, but to a lesser degree can also cause the loss or deletion of a chromosomal region. This study used inter-primer binding site (iPBS) and coupled restriction enzyme digestion inter-primer binding site (CRED-iPBS) technique analysis to determine the effect of EMS mutagens on methylation rates in wheat genotypes at seedling growth stage. Treatments with five different EMS concentrations (0%; control, 0.1%, 0.2%, 0.3%, and 0.4%) at four different times (0; control, 3, 6, and 9 h) were used. Inter-primer binding site (iPBS) markers were employed to assess genomic instability and cytosine methylation in treated wheat. In seeds treated with EMS at different concentrations and times, the disappearance of regular bands and the formation of new bands due to the effects of the EMS mutagen revealed that genetic diversity exists. The CRED-iPBS analysis revealed that the 3 h + 0.1% EMS treatment produced the highest MspI polymorphism value (19.60%), while the 9 h + 0.1% EMS treatment produced the lowest value (10.90%). The mutagenic effects of EMS treatments had considerable polymorphism on a variety of impacts on the cytosine methylation and genomic instability of wheat. According to the current research, EMS mutagenesis may be a practical method for accelerating breeding programs to produce enough genetic diversity in wheat populations. Mutation-assisted breeding and the subsequent selection of desirable mutants using genetic markers may also be carried out in wheat utilizing an integrated strategy.
Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications
Modelling plays an important role in identifying and solving problems that arise in a number of scientific issues including agriculture. Research in the natural environment is often costly, labour demanding, and, in some cases, impossible to carry out. Hence, there is a need to create and use specific “substitutes” for originals, known in a broad sense as models. Owing to the dynamic development of computer techniques, simulation models, in the form of information technology (IT) systems that support cognitive processes (of various types), are acquiring significant importance. Models primarily serve to provide a better understanding of studied empirical systems, and for efficient design of new systems as well as their rapid (and also inexpensive) improvement. Empirical mathematical models that are based on artificial neural networks and mathematical statistical methods have many similarities. In practice, scientific methodologies all use different terminology, which is mainly due to historical factors. Unfortunately, this distorts an overview of their mutual correlations, and therefore, fundamentally hinders an adequate comparative analysis of the methods. Using neural modelling terminology, statisticians are primarily concerned with the process of generalisation that involves analysing previously acquired noisy empirical data. Indeed, the objects of analyses, whether statistical or neural, are generally the results of experiments that, by their nature, are subject to various types of errors, including measurement errors. In this overview, we identify and highlight areas of correlation and interfacing between several selected neural network models and relevant, commonly used statistical methods that are frequently applied in agriculture. Examples are provided on the assessment of the quality of plant and animal production, pest risks, and the quality of agricultural environments.
Evaluation of the Effectiveness of NBPT and NPPT Application as a Urease Carrier in Fertilization of Maize (Zea mays L.) for Ensiling
The study presents the results of a 3-year field trial aimed at assessing the yield and quality of raw material for ensiling in the cultivation of three maize varieties differing in their agronomic and genetic profile, conditioned by the selection of nitrogen fertilizer. Maize cultivar ES Metronom showed a significant advantage over other cultivars when fertilized with UltraGrain stabile, or alternatively Super N-46. The application of nitrogen-stabilized fertilizers or urea + N-Lock significantly increased the yield of maize green fodder for ensiling. The “stay-green” maize cultivars were characterized by a higher content of non-structural carbohydrates, including starch and water-soluble sugars, and a lower content of structural carbohydrates, compared to the conventional cultivar, which increased their suitability for ensiling. The negative effect of maize fertilization with ammonium nitrate and ammonium nitrate + N-Lock on the chemical composition of green fodder was demonstrated by a reduced starch content and increased structural carbohydrate contents, including crude fiber and NDF. In turn, the positive effect of maize fertilization with urea and urea + N-Lock on the chemical composition of maize fodder was shown by increased starch content and reduced structural carbohydrate contents, including crude fiber and its NDF and ADF fractions. The analysis of the number and weight of leaves may indicate a highly effective utilization of nitrogen (“stay-green” maize hybrids), leading to the faster formation of leaves with a larger assimilation surface, which is the basis for the efficient absorption of solar radiation. The results obtained clearly show that only the correct choice of maize variety for silage cultivation, combined with nitrogen fertilizer guaranteeing access to N during the growing season, can guarantee a high yield for ensiling.
Insights into Drought Tolerance of Tetraploid Wheat Genotypes in the Germination Stage Using Machine Learning Algorithms
Throughout germination, which represents the initial and crucial phase of the wheat life cycle, the plant is notably susceptible to the adverse effects of drought. The identification and selection of genotypes exhibiting heightened drought tolerance stand as pivotal strategies aimed at mitigating these effects. For the stated objective, this study sought to evaluate the responses of distinct wheat genotypes to diverse levels of drought stress encountered during the germination stage. The induction of drought stress was achieved using polyethylene glycol at varying concentrations, and the assessment was conducted through the application of multivariate analysis and machine learning algorithms. Statistical significance (p < 0.01) was observed in the differences among genotypes, stress levels, and their interaction. The ranking of genotypes based on tolerance indicators was evident through a principal component analysis and biplot graphs utilizing germination traits and stress tolerance indices. The drought responses of wheat genotypes were modeled using germination data. Predictions were then generated using four distinct machine learning techniques. An evaluation based on R-square, mean square error, and mean absolute deviation metrics indicated the superior performance of the elastic-net model in estimating germination speed, germination power, and water absorption capacity. Additionally, in assessing the criterion metrics, it was determined that the Gaussian processes classifier exhibited a better performance in estimating root length, while the extreme gradient boosting model demonstrated superior performance in estimating shoot length, fresh weight, and dry weight. The study’s findings underscore that drought tolerance, susceptibility levels, and parameter estimation for durum wheat and similar plants can be reliably and efficiently determined through the applied methods and analyses, offering a fast and cost-effective approach.
Zinc Oxide Nanoparticles: An Influential Element in Alleviating Salt Stress in Quinoa (Chenopodium quinoa L. Cv Atlas)
Climate change has intensified abiotic stresses, notably salinity, detrimentally affecting crop yield. To counter these effects, nanomaterials have emerged as a promising tool to mitigate the adverse impacts on plant growth and development. Specifically, zinc oxide nanoparticles (ZnO-NPs) have demonstrated efficacy in facilitating a gradual release of zinc, thus enhancing its bioavailability to plants. With the goal of ensuring sustainable plant production, our aim was to examine how green-synthesized ZnO-NPs influence the seedling growth of quinoa (Chenopodium quinoa L. Cv Atlas) under conditions of salinity stress. To induce salt stress, solutions with three different NaCl concentrations (0, 100, and 200 mM) were prepared. Additionally, Zn and ZnO-NPs were administered at four different concentrations (0, 50, 100, and 200 ppm). In this study, plant height (cm), plant weight (g), plant diameter (mm), chlorophyll content (SPAD), K/Na value, Ca/Na value, antioxidant enzyme activities (SOD: EU g−1 leaf; CAT: EU g−1 leaf; POD: EU g−1 leaf), H2O2 (mmol kg−1), MDA (nmol g−1 DW), proline (µg g−1 FW), and sucrose (g L−1), content parameters were measured. XRD analysis confirmed the crystalline structure of ZnO nanoparticles with identified planes. Salinity stress significantly reduced plant metrics and altered ion ratios, while increasing oxidative stress indicators and osmolytes. Conversely, Zn and ZnO-NPs mitigated these effects, reducing oxidative damage and enhancing enzyme activities. This supports Zn’s role in limiting salinity uptake and improving physiological responses in quinoa seedlings, suggesting a promising strategy for enhancing crop resilience. Overall, this study underscores nanomaterials’ potential in sustainable agriculture and stress management.
Sodium Azide as a Chemical Mutagen in Wheat (Triticum aestivum L.): Patterns of the Genetic and Epigenetic Effects with iPBS and CRED-iPBS Techniques
Wheat, which is scientifically known as Triticum aestivum L., is a very nutritious grain that serves as a key component of the human diet. The use of mutation breeding as a tool for crop improvement is a reasonably rapid procedure, and it generates a variety that may be used in selective breeding programs as well as functional gene investigations. The present experiment was used to evaluate the potential application of a conventional chemical mutagenesis technique via sodium azide (NaN3) for the germination and seedling growth stage in wheat. Experiments with NaN3 mutagenesis were conducted using four different treatment periods (0, 1, 2, and 3 h) and five different concentrations (0, 0.5, 1, 1.5, and 2 mM). The genomic instability and cytosine methylation of wheat using its seeds were investigated after they were treated. In order to evaluate the genomic instability and cytosine methylation in wheat that had been treated, interprimer binding site (iPBS) markers were used. The mutagenic effects of NaN3 treatments had considerable polymorphism on a variety of impacts on the cytosine methylation and genomic instability of wheat plants. The results of the experiment showed considerable changes in the iPBS profiles produced by the administration of the same treatments at different dosages and at different times. Coupled restriction enzyme digestion interprimer binding site (CRED-iPBS) assays identified changes in gDNA cytosine methylation. The highest polymorphism value was obtained during 1 h + 2 mM NaN3, while the lowest (20.7%) was obtained during 1 h + 1.5 mM NaN3. Results showed that treatments with NaN3 had an effect on the level of cytosine methylation and the stability of the genomic template in wheat plants in the germination stage. Additionally, an integrated method can be used to for mutation-assisted breeding using a molecular marker system in wheat followed by the selection of desired mutants.
GT Biplot and Cluster Analysis of Barley (Hordeum vulgare L.) Germplasm from Various Geographical Regions Based on Agro-Morphological Traits
Barley, an ancient crop, was vital for early civilizations and has historically been served as food and beverage. Today, it plays a major role as feed for livestock. Breeding modern barley varieties for high yield and quality has created significant genetic erosion. This highlights the importance of tapping into genetic and genomic resources to develop new improved varieties that can overcome agricultural bottlenecks and increase barley yield. In the current study, 75 barley genotypes were evaluated for agro-morphological traits. The relationships among these traits were determined based on genotype by trait (GT) biplot analysis for two cropping years (2021 and 2022). This study was designed as a randomized complete block experiment with four replications. The variation among genotypes was found to be significant for all traits. The correlation coefficient and GT biplot revealed that grain yield (GY) was positively correlated with the number of grains per spike (NGS), the grain weight per spike (GW), and the thousand kernel weight (1000 KW). However, the test weight (TW) was negatively correlated with the heading date (HD). Hierarchical analysis produced five groups in the first year, four groups in the second year, and four groups over the average of two years. Genotypes by trait biplot analysis highlighted G25, G28, G61, G73, and G74 as promising high-yielding barley genotypes. This study demonstrated the effectiveness of the GT biplot as a valuable approach for identifying superior genotypes with contrasting traits. It is considered that this approach could be used to evaluate the barley genetic material in breeding programs.
Evaluation of the Effect of Conventional and Stabilized Nitrogen Fertilizers on the Nutritional Status of Several Maize Cultivars (Zea mays L.) in Critical Growth Stages Using Plant Analysis
The study presents the results of a three year field trial aimed at assessing the nutritional status of maize in critical growth stages by means of a plant analysis in the cultivation of three maize cultivars differing in their agronomic and genetic profile. The main research problem was to demonstrate whether the availability of nitrogen from stabilized fertilizers for “stay-green” maize varieties is consistent with the dynamics of the demand for this component. This is very important from both the economic and agronomic aspect of maize cultivation. The research showed a significant response of the maize cultivars to different nitrogen fertilizer formulations, which was observed in the period from the five-leaf stage to the full flowering stage. The advantage of the fertilizer, UltraGran stabilo, over other nitrogen fertilizers in the BBCH 15 stage was demonstrated only for the cultivar, ES Metronom, which produced a greater aerial mass while maintaining the nitrogen concentration at the level of the other two maize cultivars. The nitrogen and potassium content shaped the kernel weight in the ear in the flowering stage, confirming the importance of the interaction of these two elements in forming this feature of maize as the main predictor of the grain yield. This trait (expressed by the R2 coefficient) manifested each year of the study, but especially in the years with optimal weather patterns (i.e., the first year). The response of the maize cultivars to nitrogen fertilizers, especially the cultivar, ES Metronom, was manifested by an increase in the content of nutrients and chlorophyll in the ear leaf, that is considered a predictive organ for grain yield. The fertilizers, Super N-46 and UltraGran stabilo, had a positive effect on the chlorophyll content (CCI parameter) and increased its efficiency of excitation energy transfer (the F0 parameter).
Investigation of the Influence of Polyamines on Mature Embryo Culture and DNA Methylation of Wheat (Triticum aestivum L.) Using the Machine Learning Algorithm Method
Numerous factors can impact the efficiency of callus formation and in vitro regeneration in wheat cultures through the introduction of exogenous polyamines (PAs). The present study aimed to investigate in vitro plant regeneration and DNA methylation patterns utilizing the inter-primer binding site (iPBS) retrotransposon and coupled restriction enzyme digestion–iPBS (CRED–iPBS) methods in wheat. This investigation involved the application of distinct types of PAs (Put: putrescine, Spd: spermidine, and Spm: spermine) at varying concentrations (0, 0.5, 1, and 1.5 mM). The subsequent outcomes were subjected to predictive modeling using diverse machine learning (ML) algorithms. Based on the specific polyamine type and concentration utilized, the results indicated that 1 mM Put and Spd were the most favorable PAs for supporting endosperm-associated mature embryos. Employing an epigenetic approach, Put at concentrations of 0.5 and 1.5 mM exhibited the highest levels of genomic template stability (GTS) (73.9%). Elevated Spd levels correlated with DNA hypermethylation while reduced Spm levels were linked to DNA hypomethylation. The in vitro and epigenetic characteristics were predicted using ML techniques such as the support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF) models. These models were employed to establish relationships between input variables (PAs, concentration, GTS rates, Msp I polymorphism, and Hpa II polymorphism) and output parameters (in vitro measurements). This comparative analysis aimed to evaluate the performance of the models and interpret the generated data. The outcomes demonstrated that the XGBoost method exhibited the highest performance scores for callus induction (CI%), regeneration efficiency (RE), and the number of plantlets (NP), with R2 scores explaining 38.3%, 73.8%, and 85.3% of the variances, respectively. Additionally, the RF algorithm explained 41.5% of the total variance and showcased superior efficacy in terms of embryogenic callus induction (ECI%). Furthermore, the SVM model, which provided the most robust statistics for responding embryogenic calluses (RECs%), yielded an R2 value of 84.1%, signifying its ability to account for a substantial portion of the total variance present in the data. In summary, this study exemplifies the application of diverse ML models to the cultivation of mature wheat embryos in the presence of various exogenous PAs and concentrations. Additionally, it explores the impact of polymorphic variations in the CRED–iPBS profile and DNA methylation on epigenetic changes, thereby contributing to a comprehensive understanding of these regulatory mechanisms.
Exploring Digital Innovations in Agriculture: A Pathway to Sustainable Food Production and Resource Management
Today’s agriculture faces numerous challenges due to climate change, a growing population and the need to increase food productivity [...]
Genotype-Trait (GT) Biplot Analysis for Yield and Quality Stability in Some Sweet Corn (Zea mays L. saccharata Sturt.) Genotypes
A strong statistical method for investigating the correlations between traits, assessing genotypes based on numerous traits, and finding individuals who excel in particular traits is genotype–trait (GT) biplot analysis. The current study was applied to evaluate 11 sweet corn (Zea mays L. saccharata) genotypes and correlate them based on genotype–trait (GT) biplot analysis for two cropping seasons in Erzurum, Türkiye using the RCBD experimental design with three reputations. The results showed that the genotypes were significantly different for the majority of the examined variables according to the combined analysis of variance findings at 0.01 probability level. An ecological analysis was performed to evaluate sweet corn varieties and environmental conditions and interactions between them (genotype × environmental conditions). Our results showed that the summation of the first two and second main components was responsible for 73.51% of the combined cropping years of the sweet corn growth and development variance, demonstrating the biplot graph’s optimum relative validity, which was obtained. In this study, the Khan F1 (G6) genotype was found to be the stablest genotype, and the Kompozit Seker (G7) genotype was the non-stable genotype, moreover based on the first cropping year, second cropping year, and the average mean of the two cropping years. As a conclusion, the Khan F1 (G6) genotype is the highest-yielding genotype, and the Kompozit Seker (G7) is the lowest. Based on the heat map dendrogram, the context of the differential extent of trait association of all genotypes into two clusters is indicated. The highest genetic distance was shown between the BATEM Tatlı (G3) and Febris (G5) genotypes. Our results provide helpful information about the sweet corn genotypes and environments for future breeding programs.
Methodology for Assessing Tractor Traction Properties with Instability of Coupling Weight
The purpose of the study is to increase the efficiency of using the tractor hitch weight in traction mode by reducing the uneven distribution of vertical reactions between the wheels. This work is grounded on a methodology that involves summarizing and analyzing established scientific findings related to the theory of tractors operating in traction mode. The analytical method and comparative analysis were employed to establish a scientific problem, define research objectives, and achieve the goal. The key principles of probability theory were applied in developing the empirical models of the tractor. The main provisions of the methodology for evaluating the traction properties of the tractor with the instability of the coupling weight were formulated. The method of evaluating the vertical reactions on the wheels of the tractor is substantiated, which is based on the measurement of the vertical reaction on one of the four wheels. It was proven that tractors with a center of mass offset to the front or rear axles have the greatest probability of equal distribution of vertical reactions between the wheels of one axle, and tractors with a center of mass in the middle between the axles have the lowest probability. It is theoretically substantiated and experimentally confirmed that when the tractor performs plowing work with uneven distribution of loads on the sides, its operation with maximum traction efficiency is ensured by blocking the front and rear axle drivers.
Genetic Diversity and Population Structure in Bread Wheat Germplasm from Türkiye Using iPBS-Retrotransposons-Based Markers
This study investigated the genetic diversity and population structure of 63 genotypes from Turkish bread wheat germplasm using iPBS-retrotransposons primers. The thirty-four iPBS primers produced a total of 1231 polymorphic bands, ranging from 8 (iPBS-2375) to 60 (iPBS-2381) alleles per marker, with an average number of 36.00 alleles. The polymorphism information content (PIC) per marker varied between 0.048 (iPBS 2087) and 0.303 (iPBS 2382), with an average of 0.175. The numbers of effective alleles (ne), genetic diversity of Nei (h), and Shannon’s information index (I) value were calculated as 1.157, 0.95, and 0.144, respectively. The greatest genetic distance (0.164) was between Eastern Anatolia Agricultural Research Institute genotypes and Çukurova Agricultural Research Institute genotypes. The unweighted pair-group method with arithmetic mean (UPGMA) dendrogram placed the 63 wheat genotypes into three clusters. The percentage of genetic diversity explained by each of the three main coordinates of the basic coordinate analysis was determined to be 44.58, 12.08, and 3.44, respectively. AMOVA (Analysis of Molecular Variance) showed that the variation within populations was 99% and that between populations was 1%. The result of genetic structure analysis suggests that the greatest value of K was calculated as 3. The F-statistic (Fst) value was determined as 0.4005, 0.2374, and 0.3773 in the first to third subpopulations, respectively. Likewise, the expected heterozygosity values (He) were determined as 0.2203, 0.2599, and 0.2155 in the first, second, and third subpopulations, respectively. According to the information obtained in the study, the most genetically distant genotypes were the G1 (Aksel 2000) and G63 (Karasu 90) genotypes. This study provided a deep insight into genetic variations in Turkish bread wheat germplasm using the iPBS-retrotransposons marker system.
Planting Geometry May Be Used to Optimize Plant Density and Yields without Changing Yield Potential per Plant in Sweet Corn
Planting geometry is one of the most important management practices that determine plant growth and yield of corn. The effects of eight planting geometries (35 × 23 cm, 40 × 21 cm, 45 × 19 cm, 50 × 18 cm, 55 × 17 cm, 60 × 16 cm, 65 × 15 cm, 70 × 15 cm) on plant growth and yields of three sweet corn hybrids (Argos F1, Challenger F1, Khan F1) were investigated under Erzurum, Türkiye conditions in 2022 and 2023 years. Variance analysis of the main factors shows a highly significant effect on whole traits but in two-way interactions some of the traits were significant and in the three-way interactions, it was insignificant. As an average of years, the number of plants per hectare at the harvest varied between 92,307 (35 × 23 cm) and 120,444 (70 × 15 cm) according to the planting geometries. The highest marketable ear number per hectare (107,456), marketable ear yield (24,887 kg ha−1), and fresh kernel yield (19,493 kg ha−1) were obtained from the 40 × 21 cm planting geometry. The results showed that the variety Khan F1 grown at 40 × 21 cm planting geometry obtained the highest marketable ear number (112,472), marketable ear yield (29,788 kg ha−1), and fresh kernel yield (22,432 kg ha−1). The plant density was positively correlated with marketable ear number (r = 0.904 **), marketable ear yield (r = 0.853 **), and fresh kernel yield (r = 0.801 **). The differences among the varieties were significant for the studied traits, except for plant density and kernel number per ear. In conclusion, the variety Khan F1 should be grown at the 40 × 21 cm planting geometry to maximize yields under study area conditions without water and nutrient limitations.
Digital Innovations in Agriculture
Digital agriculture, defined as the analysis and collection of various farm data, is constantly evolving [...]
