Plant Growth Stages Research Articles

Genomic Organization and Expression Profiling of GOLDEN2-like Transcription Factor Genes in Eggplant and Their Role in Heat Stresses

GOLDEN2-like (GLK) transcription factor genes are involved in chloroplast biogenesis during all stages of plant growth and development, as well as in the response to biotic and abiotic stresses. However, little is known about this transcription factor family in eggplant. In this study, we identified 54 GLK genes in the eggplant genome (S. melongena L.) and classified them into seven groups (G1–G7). Structural analysis illustrated that the SmGLK proteins of specific groups are relatively conserved. Cis-acting elements indicated that these genes are likely to be involved in multiple responses stimulated by light, phytohormones, and abiotic stress. Collinear analysis indicated that expansion of the SmGLK gene family primarily occurred through segmental duplication. Tissue-specific expression analysis revealed that SmGLKs were preferentially expressed in leaves, fruits, and seeds. Further screening of SmGLK genes revealed their differential expression under various treatments. Notably, SmGLK18 was significantly responsive to multiple phytohormones and stress treatments, whereas SmGLK3 and SmGLK12 were highly induced by ABA, IAA, SA, and drought treatments. Our study provides new information on the eggplant GLK family systematically and comprehensively. For the first time, we propose that SmGLK18 may play a key role in improving heat resistance. This study provides valuable candidate gene resources for further functional research and will benefit eggplant molecular breeding.

Cadmium distribution in rice: Understanding the role of plant nodes and growth stages

Cadmium (Cd) contamination in farmland poses a significant threat to food security in staple crops, especially rice. Using a mix of hydroponic and soil culture methods, stable isotope tracers, and advanced analytical techniques, this study elucidated the mechanisms of Cd uptake, translocation, and accumulation in rice throughout different growth stages. Despite a notable linear correlation between soil DTPA (diethylene-triaminepentaacetic acid)-Cd and the total Cd concentration of rice, our findings showed that the influence of soil Cd level on the proportion of Cd in grain was negligible. The study highlighted the dynamic response of Cd distribution within plant nodes to changes in DTPA-extractable Cd. Heading stage (HS) and mature stage (MS) were critical for Cd uptake and upward transport in rice, and the contribution of Cd absorption in brown rice was 28.61% and 40.16%, respectively. Moreover, the distribution of Cd in nodes showed how important nodes are for controlling and redistributing Cd in rice. In the HS, the lower node had a function in re-transporting, whereas in the MS, there was a considerable redistribution of Cd in the upper node.These insights can help us understand rice Cd dynamics and develop agronomic techniques and rice cultivars that minimize Cd accumulation.

The Impact of Rhizospheric and Endophytic Bacteria on the Germination of Carajasia cangae: A Threatened Rubiaceae of the Amazon Cangas.

Carajasia cangae (Rubiaceae) is a narrow endemic species from the canga ecosystems of the Carajás National Forest that is facing extinction due to a limited range and habitat disturbance from hydroclimatological changes and mining activities. This study examines the influence of rhizospheric and endophytic bacteria on C. cangae seed germination to support conservation efforts. Soil samples, both rhizospheric and non-rhizospheric, as well as plant root tissues, were collected. Bacteria from these samples were subsequently isolated, cultured, and identified. DNA sequencing revealed the presence of 16 isolates (9 rhizospheric and 7 endophytic), representing 19 genera and 6 phyla: Proteobacteria, Actinobacteria, Acidobacteria, Firmicutes, Bacteroidetes, and Chloroflexi. The endophytic isolates of Bacillus and the rhizospheric isolates of Planococcus and Lysinibacillus reduced the median germination time and initiation time, while the rhizospheric isolates Serratia and Comamonas increased the germination time and decreased the germination percentage in comparison to the control sample. These findings emphasize the crucial role of endophytic bacteria in the germination of C. cangae and highlight isolates that could have beneficial effects in the following stages of plant growth. Understanding the impact of endophytic and rhizospheric bacterial isolates on seed germination can enhance conservation efforts by shortening the germination period of this species and thereby improving seedling production. Additionally, this knowledge will pave the way for future research on the role of bacteria in the establishment of C. cangae.

Arbuscular mycorrhizal fungi improve the disease resistance of Lycium barbarum to root rot by activating phenylpropane metabolism.

Root rot is one of the common diseases of Lycium barbarum. Pathogens can cause devastating disasters to plants after infecting host plants. This study investigated the effect of arbuscular mycorrhizal fungi (AMF) Rhizophagus intraradices inoculation on phenylpropane metabolism in L. barbarum and evaluated its resistance to root rot. The experiment was set up with AMF inoculation treatments (inoculated or not) and root rot pathogen-Fusarium solani inoculation treatments (inoculated or not). The results showed that AMF was able to form a symbiosis with the root system of L. barbarum, thereby promoting plant growth significantly and increasing plants' resistance to disease stress. The plant height of AMF-colonized L. barbarum increased by 24.83% compared to non-inoculated diseased plants. After inoculation with AMF, the plant defense response induced by pathogen infection was stronger. When the enzyme activity of the leaves reached the maximum after the onset of mycorrhizal L. barbarum, phenylalanine ammonia-lyase, cinnamic acid-4-hydroxylase, and 4-coumaric acid-CoA ligase increased by 3.67%, 31.47%, and 13.61%, respectively, compared with the non-inoculated diseased plants. The products related to the lignin pathway and flavonoid pathway downstream of phenylpropane metabolism such as lignin and flavonoids were also significantly increased by 141.65% and 44.61% compared to nonmycorrhizal diseased plants. The activities of chitinase and β-1,3-glucanase increased by 36.00% and 57.96%, respectively. The contents of salicylic acid and jasmonic acid were also 17.7% and 31.63% higher than those of nonmycorrhizal plants in the early stage of plant growth, respectively. The results indicated that AMF significantly promoted plant growth and enhanced disease resistance by increasing enzyme activities and the production of lignin and flavonoids.

Identification of Rust Resistance Genes in Wheat (Triticum aestivum L.) Using Molecular Markers and Host–Pathogen Interaction Tests

ABSTRACTThe leaf rust (Puccinia triticina f. sp. tritici), stripe rust (Puccinia striiformis f. sp. tritici), and stem rust (Puccinia graminis f. sp. tritici) are major fungal constraints affecting wheat production worldwide. Identifying and deploying wheat varieties with diverse resistance are the best ways to manage all the rusts. Therefore, a continuous search goes on to identify diverse germplasm with effective rust resistance that expresses at different stages of plant growth (seedling and adult plant). A set of 22 rust resistant wheat genotypes and 4 checks (controls), viz., Avocet‐Yr10, Avocet ‐Yr15, Agra Local, and respective positive checks were studied for characterising rust resistance genes using host–pathogen interactions complemented by molecular markers. Among 22 elite genotypes, 05 genotypes amplified 191 bp fragment with marker PSY1E1, confirmed the presence of gene Lr19/Sr25. These genotypes also expressed resistance to most virulent leaf rust pathotypes, 77‐5 and 77‐9 in host–pathogen interaction test (HPI). Seven genotypes showed the presence of Lr34/Yr18/Sr57/Pm38/Ltn1 in homozygous state, whereas G4 showed its presence in heterozygous condition. Among 22 genotypes, 16 genotypes possessed Yr10. Five genotypes (22.7%) exhibited two gene combinations, Lr19/Sr25, and Yr10 as revealed through the detection of 191 bp fragment with marker PSY1E1 and 260 bp fragment with co‐dominantly inherited microsatellite marker Xpsp3000, respectively. All five genotypes (G2, G3, G8, G9, and G18) also expressed brown glumes controlled by the gene Rg1 tightly linked to Yr10 on the 1BS chromosome. Broad spectrum rust resistance present in these lines in good agronomic backgrounds could be used as potent genetic donors for diverse and durable rust resistance breeding programmes in wheat.

Bioassessment of Cd and Pb at Multiple Growth Stages of Wheat Grown in Texturally Different Soils Using Diffusive Gradients in Thin Films and Traditional Extractants: A Comparative Study.

The bioavailability of heavy metals in soil is a crucial factor in determining their potential uptake by plants and their subsequent entry into the food chain. Various methods, including traditional chemical extractants and the diffusive gradients in thin films (DGT) technique, are employed to assess this bioavailability. The bioavailability of heavy metals, particularly cadmium (Cd) and lead (Pb), is also influenced by soil texture and their concentrations in the soil solution. The primary objectives of this experiment were to compare and correlate the assessment of the Cd and Pb bioavailability using the DGT technique and traditional extractants across two soil textural classes: sandy clay loam (SCL) and clay loam (CL) at two contamination levels: aged contaminated (NC) and artificially contaminated (AC). The specific objectives included assessing the bioavailability of Cd and Pb at different growth stages of the wheat plant and correlating the DGT-based bioassessments of Cd and Pb with their concentrations in various plant parts at different growth stages. This study also compared the effectiveness of the DGT method and traditional extraction techniques in assessing the bioavailable fractions of Cd and Pb in soil. The regression analysis demonstrated strong positive correlations between the DGT method and various extraction methods. The results showed that the wheat plants grown in the AC soils exhibited lower root, shoot, and grain weights compared to those grown in the NC soils, indicating that metal contamination negatively impacts plant performance. The concentrations of Cd and Pb in the wheat tissues varied across different growth stages, with the highest levels observed during the grain filling (S3) and maturity (S4) stages. It is concluded that the in situ assessment of Cd and Pb though DGT was strongly and positively correlated with the Cd and Pb concentration in wheat plant parts at the maturity stage. A correlation and regression analysis of the DGT assessment and traditional extractants showed that the DGT method provides a reliable tool for assessing the bioavailability of Cd and Pb in soils and helped in developing sustainable soil management strategies to ensure the safety of agricultural products for human consumption.

Probiotics in Eco-efficient Plant Growing Systems

Abstract: The understanding of plant-associated bacterial communities has attracted a lot of attention because of their crucial function in improving plant physiology and productivity. These benefits include bioremediation, nutrient solubilization, and stress tolerance. Notably, the use of bacteria as probiotics in eco-efficient plant growing systems (EPGS), including hydroponics and aquaponics, is emerging. These systems are ecologically friendly, productive, resource-efficient, and hold excellent promises for a more sustainable agriculture. This review explores the use of probiotics in the unique stages of plant growth in EPGS, highlighting the significance of managing nutrients and conditions for the plant growth. Moreover, it clarifies how important bacteria are to the nitrogen cycle in EPGS, particularly nitrifying and nitrogen-fixing species. Furthermore, it discusses the methods for bioprospecting new probiotics, highlighting the importance of the combination of metagenomics and conventional microbiology methods. It also highlights how crucial it is to understand the dynamics of probiotics in EPGS for environmentally friendly plant production and preservation in the face of climate change.

Data-driven determination of plant growth stages for improved weather index insurance design

PurposeThis paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.Design/methodology/approachUsing the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.FindingsOur results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.Originality/valueThis research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

Biosynthesis of Ascorbic Acid and Its Metabolism across Plant Growth Stages, Fruit Maturation, and Postharvest Physiology: A Review

Ascorbic acid (AsA), generally known as vitamin C, is essential for horticulture crops to grow, develop, and maintain themselves after harvest. It is essential for plant health, fruit quality, and human nutrition because of its role in enzymatic processes, hormone production, and antioxidant activity. Important plant hormones, including auxins and gibberellins, which control the growth and development of fruit, are involved in its production. The maintenance of optimal AsA levels in horticultural crops can help ensure their nutritional value and quality for consumers. This is done by utilizing suitable postharvest practices and by understanding the variables like temperature, light exposure, and oxygen that influence AsA content. To fully comprehend the function of ascorbic acid in horticulture crops throughout plant growth, fruit development and ripening, and postharvest physiology, more study is required. This information may be used to increase the ascorbic acid content of crops and improve their nutritional value. Key intermediates in the AsA biosynthesis pathway have been shown in a simplified schematic diagram. The control of plant AsA production and subsequent responses to horticulture crops throughout plant growth, fruit development and ripening, and postharvest physiology may now be better understood through future study.

Hybrid Adaptive Multiple Intelligence System (HybridAMIS) for classifying cannabis leaf diseases using deep learning ensembles

Optimizing cannabis crop yield and quality necessitates accurate, automated leaf disease classi-fication systems for timely detection and intervention. Existing automated solutions, however, are insufficiently tailored to the specific challenges of cannabis disease identification, struggling with robustness across varied environmental conditions and plant growth stages. This paper introduces a novel Hybrid Adaptive Multi-Intelligence System for Deep Learning Ensembles (HyAMIS-DLE), utilizing a comprehensive dataset reflective of the diversity in cannabis leaf diseases and their progression. Our approach combines non-population-based decision fusion in image prepro-cessing with population-based decision fusion in classification, employing multiple CNN archi-tectures. This integration facilitates a significant improvement in performance metrics: Hy-AMIS-DLE achieves an accuracy of 99.58 %, outperforming conventional models by up to 4.16 %, and exhibits superior robustness and an enhanced Area Under the Curve (AUC) score, effectively distinguishing between healthy and diseased leaves. The successful deployment of HyAMIS-DLE within our Automated Cannabis Leaf Disease Classification System (A-CLDC-S) demonstrates its practical value, contributing to increased crop yields, reduced losses, and the promotion of sus-tainable agricultural practices.

Effects of organic amendments on nutrient and quality characteristics of Salvia fruticosa Mill at different plant growth stages

ABSTRACT Salvia (Salvia fruticosa Mill) is used in traditional medicine and in food and pharmacological industries, and as organically grown plants are preferred it is important to understand how organic soil fertilisation regimes affect the nutritional status and quality of the plant. To address this, an experiment was conducted testing the direct (in the first year) and residual (in the second year) effects of different organic nutrient inputs; control (C: untreated plants), farmyard manure (FYM), chicken manure (CM), vermicompost (VC), and spent mushroom compost (SMC). In the first year, N concentrations in the salvia were increased by 15%–46% and 8%–16%, at beginning of flowering and at full flowering, respectively, indicating a direct effect of the organic amendments. In the second year, the residual effects of the organic amendments increased N concentrations by 8%–37% at beginning of flowering. The organic amendments also affected concentrations of P at full flowering and caused increases of 6%–23% in the first year and by 6%–18% in the second year. Soil organic matter increased by 13%–16% as direct effects of the organic amendments and by 14%–24% as residual effects. Antioxidant activity, total flavonoid and phenolic concentrations of the salvia increased with applications of organic amendments. Total phenolic concentration and antioxidant activity were positively correlated with N, Mn and Mg and negatively correlated with K, Ca and P in the plant leaves. VC and SMC were the most effective amendments for enhancing total flavonoid content, antioxidant activity and plant nutrient concentrations.

Plant phenology modulates and undersown cover crops mitigate N2O emissions

Mitigation of N2O emissions, a potent greenhouse gas, remain challenging due to knowledge gaps in plant-mediated nitrogen (N) transformation pathways, which limits ability to identify optimal approaches for efficient N utilization. We set up mesocosms with barley, Italian ryegrass, and barley in combination with Italian ryegrass to assess role of cover crop in N2O emission mitigation. Soil emitted N2O was collected simultaneously from the pots with plants at three growth stages: namely, vegetative, canopy expansion, and grain filling. The gas sample N2O contents, N in microbial biomass (MBN), mineral N content, and phospholipid fatty acid (PLFA) analysis in soils were determined at the three growth stages. Cumulatively, highest N2O was emitted from soil under Italian ryegrass (0.056 mg N g−1 soil) followed by barley (0.0051 mg N g−1 soil) and the least under barley and Italian ryegrass combination (0.0014 mg N g−1 soil). The high emissions under Italian ryegrass occurred at vegetative stage due to high reactive N availability. Strong emissions were observed at canopy expansion stage under barley and were linked to access to the large mineral N proportion redistributed to the lower depth as depicted by highest MBN (0.025 mg N g−1 soil) and decreased extractable N (0.0068 mg N g−1 soil). The high emissions under barley correlated with high fungal/bacterial ratio, pointing towards a fungal role in the emissions. The least soil N2O emissions under barley and Italian ryegrass combination were accompanied by elimination of variations induced by the plant growth stages. Absence of 18:2ω6,9 fungal PLFA biomarker under barley and Italian ryegrass combination indicates a potential inhibition and corresponds with reduced N2O emissions. Together, these results broaden our understanding on how plant-soil interactions drives N2O emissions processes and improves our ability to identify optimal plant-based emission mitigation approaches.

From Wasteland to Bloom: Exploring the Organizational Profiles of Occupational Health and Well-Being Strategies and Their Effects on Employees' Health and Well-Being.

Based on the signaling and conservation of resources theories, this study aims to identify different strategic organizational profiles related to occupational health and well-being (OHWB). Additionally, this study explores how these various organizational profiles impact employees' well-being, specifically in relation to absenteeism, emotional exhaustion, work overload, intention to quit, and job satisfaction. Data were collected from 59 organizations and 2828 employees. The first phase of this study presents the latent profile analysis carried out to identify OHWB organizational profiles. This analysis reveals four organizational profiles that are metaphorically named according to the growth stages of plants (i.e., wasteland, sprouting, budding, and blooming OHWB profiles). The second phase of this study investigates the associations between the latent profiles assigned to the organizations with absenteeism, intention to quit, emotional exhaustion, feelings of work overload, and job satisfaction among their employees using MANOVA. The results show that organizational profiles influence employees' health and well-being. Employees working in organizations with a low OHWB profile, known as the "wasteland profile", tend to report more days of absenteeism, higher levels of emotional exhaustion, greater work overload, and lower job satisfaction. Employees are also more likely to express a greater intention to quit their jobs than those working in organizations with a higher OHWB profile (a "blooming profile"). This study is useful for organizations and practitioners seeking to understand how investing in a health and well-being strategy can benefit their employees.

Predicting Plant Growth Stages Using Random Forest Classifier: A Machine Learning Approach

The optimization of plant growth through predictive modelling is a crucial aspect of modern agricultural practices. This study investigates the application of a Random Forest Classifier to predict plant growth stages based on various environmental and management factors. The dataset, sourced from Kaggle, includes variables such as soil type, sunlight hours, water frequency, fertilizer type, temperature, and humidity. The research involves extensive data pre-processing, including encoding categorical variables, scaling data, and splitting it into training (80%) and testing (20%) sets. The Random Forest Classifier is implemented with 5-fold cross-validation, and its performance is evaluated using accuracy, precision, recall, and F1-score metrics. The model exhibits robust performance with an average accuracy of 84.27%, precision of 85.59%, recall of 84.27%, and F1-score of 83.98%. Visualization techniques such as correlation heatmaps, PCA plots, t-SNE plots, and violin plots are used to provide insights into the data structure and feature relationships. The results confirm the hypothesis that machine learning can effectively predict plant growth stages, offering significant implications for precision agriculture. By accurately identifying growth stages, farmers and greenhouse managers can optimize resource allocation and management practices, leading to enhanced crop yields and sustainability. The study's limitations include the specificity of the dataset and the sole use of the Random Forest Classifier. Future research should explore additional machine learning models and incorporate more diverse datasets to improve generalizability. The findings contribute to the growing body of knowledge on the application of machine learning in agriculture and suggest practical applications for improving agricultural productivity

Effect of Different Zinc Sources and Application Methods on Content and Uptake of Micronutrients of Basmati Rice

Zinc (Zn) is an essential trace element for plants, animals and human’s health. Zn deficiency is widely spread in paddy soils of India and has negative impact on national rice production. Field experiment on rice crop was conducted in sandy loam soil at crop research center Chirodi of SVBP University of Agriculture and Technology, Meerut., during Kharif season of 2011 and 2012 toevaluate the “Effect of different sources and application methods of zinc on content and uptake of micronutrients in basmati rice crop in sandy loam soil. The experiment was laid out in randomized block design with three replications. The experiment comprised of twelve treatments viz.; T1 (control), T2 (recommended NPK @120:60:60 kg ha-1) T3 (5 kg Zn through ZnSO4.7H2O+RDF), T4(5 kg Zn through mono ZnSO4.7H2O),T5 (0.1% Zn spray through ZnSO4.7H2O+RDF ),T6 (0.1% Zn spray through ZnSO4.7H2O + RDF),T7 (0.012% Zn spray through chelated Zn at tillering + RDF),T8 (0.05% Zn spray through ZnSO4.7H2O at tillering +0.05% at panicle initiation+ RDF), T9 (0.05% Zn spray through mono ZnSO4.7H2O at tillering + 0.05% at panicle initiation +RDF),T10 (0.006% Zn spray through chelated Zn at tillering + 0.006% at panicle initiation+ RDF ), T11 (micronutrient mixture@ 25kg ha-1 + RDF), T12 (vermicompost @ 3tha-1+ RDF).The experimental soil was low in organic carbon and available nitrogen and medium in phosphorus and higher in potassium with slightly alkaline in pH. The status of DTPA extractable Zn 1.23 mg Kg-1,Fe 14.85 mg Kg-1 Cu 2.43 mg Kg-1 Mn 10.91 mg Kg-1 in the surface soil. Nutrient assimilation at different stages by the rice crop varied significantly due to application of different treatments in the study. Maximum zinc content 85.78 and 93.57 ppm and uptake 288.60 and 341.85 g/ha at 30 DAT during 2011 and 2012 found in T12 which was significantly higher than the rest of the treatments while minimum zinc content recorded in T1 was significantly lower than the rest of the treatments during both the years. Similar trends were also recorded at 60 DAT, grain and straw growth stages of rice plant. The zinc content of plant sample in T11 and T3 was also higher and statistically at par to the level of zinc content recorded in T12. The Cu, Fe, and Mn content and uptake at different growth stages of rice plant sample in T11 and T3 was also higher and like the level in T12. Among the method of Zn application, soil application resulted in higher biomass, micronutrient content and uptake in the grain and straw. Foliar application caused greater effect on zinc content and uptake and as well as content and uptake of Cu, Fe and Mn in rice plant at different growth stages during both the years. Among the sources of Zinc, ZnSO4.7H2O proved to be the most efficient source of Zn for rice production.

Both Short-term and Long-term Ozone Pollution Alters the Chemical Composition of rice Grain.

The increasing ground-level ozone (O3) is threatening food security, especially in Asian areas, where rice is one of the most important staple crops. O3 impacts on rice could be exacerbated by its spatiotemporal heterogeneity. To improve evaluation accuracy and develop effective adaptations, direct data is urgently needed. Studies on the short-term effects of O3 on rice grain, however, are lacking. Which may lead to an underestimation of the O3 impacts. Through a field experiment, we studied the responses of grain nitrogen, grain carbon, and grain protein in rice cultivars to elevated concentrations of O3 (40 ppb plus that in background air, eO3), especially examining the effects of short-term eO3 during different plant growth stages. We found that long-term eO3 increased grain nitrogen by 29.29% in a sensitive rice cultivar, and short-term eO3 at the tillering and jointing stages increased grain nitrogen by 19.31%, and the grain carbon to nitrogen ratio was decreased by 14.70%, and 21.14% by short-term and long-term eO3. Here we demonstrate that short-term eO3 may significantly affect the chemical composition of rice grains. Previous evaluations of the effects of eO3 may be underestimated. Moreover, changes in the grain nitrogen and grain protein were greater when the short-term eO3 was added to rice plants during the tillering and jointing stage, compared to heading and ripening stage. These results suggest that to improve the tolerance of rice to eO3 to achieve food security, studies on cultivar screening, as well as developing growth-stage-specific adaptations are needed in future.

Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms

Vertical indoor farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use deep learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s-eye view and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering, and fruiting classes. All the trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

Role of Endophytic Entomopathogenic Fungi in Mediating Host Selection, Biology, Behavior, and Management of Tarnished Plant Bug, Lygus lineolaris (Hemiptera: Miridae).

Non-insecticidal control strategies using entomopathogens, nematodes, and endophytes provide sustainable and safer alternatives for managing crop pests. This study investigated the potential of different fungal endophytes, specifically Beauveria bassiana strains, in colonizing cotton plants and their efficacy against tarnished plant bug, Lygus lineolaris. The effect of endophytes on plant growth parameters and cotton yield were measured during different plant growth stages. The entomopathogenicity of these fungi was studied in diet cup bioassays using L. lineolaris adults. The behavior of adult males and females toward endophytic cotton squares was analyzed using olfactometer assays. The experiments showed that the fungal endophytes colonized the plant structures of cotton plants, which resulted in an increase in the number of cotton squares, plant height, and weight compared to control plants. B. bassiana strains/isolates such as GHA, NI-8, and JG-1 caused significant mortality in Lygus adults compared to controls. Also, male and female Lygus adults exhibited repellence behavior towards endophytic cotton squares containing JG-1 isolate of B. bassiana and to other B. bassiana strains such as NI-8, GHA, and SPE-120. No differences were observed in the survival and development of L. lineolaris second-instar nymphs on endophytic cotton, and no yield differences were observed in the field experiments.

Ontogenic Predisposition of Tomato Foliage to Early Blight Caused by Alternaria solani under Mountainous Conditions of Kashmir

This study explores the susceptibility of tomato plants to early blight caused by Alternaria solani concerning their chronological and physiological ages. While previous research has noted higher resistance in younger potato plants and an acropetal increase in resistance within plant leaves, such investigations are limited in tomatoes. Our study aimed to address this gap by investigating the influence of chronological and physiological ages on early blight resistance in tomato plants. Using the Keck Ruth Ageti cultivar, we conducted experiments at various plant growth stages. Chronological age variations were achieved by sowing seeds at 10-day intervals, with inoculation occurring when the youngest seedlings reached 20 days. Leaf sampling from different sections facilitated the evaluation of physiological age effects. Results showed a direct relationship between plant age and disease susceptibility, with older plants exhibiting higher incidence rates. Moreover, resistance increased from lower to upper leaves, with top leaves demonstrating the highest resistance.

Effects of Foliar Selenium Spraying on Yield and Quality Characteristics of Fenugreek (Trigonella foenum graecum L.) at Different Plant Growth Stages

ABSTRACT The study was conducted to determine the effects of foliar selenium spraying on the yield and quality characteristics of fenugreek in different plant growth stages. The experiment was established according to the split-split block experimental design. In the study, the main plots, sub-plots, and sub-sub plots consist of different plant growth stages (seedling, flowering, and seed formation periods), cultivars (Gürarslan, Çiftçi, and Berkem), and selenium doses (Control, 25, 50 and 75 g ha−1), respectively. Applying selenium at different plant growth stages was a statistically significant effect on the number of pods and protein content in the first year and on the number of branches in the second year. According to different plant growth stages, the highest results for the number of pods per plant, protein content and number of branches per plant were obtained during the flowering period. Applied selenium doses were ineffective on all investigated plant parameters except for plant height and number of seeds per pod in the first year. The highest results in the plant height and the number of seeds per pod were obtained from 75 g ha−1 selenium application and the control parcels, respectively. Biological yield and protein content were significant among cultivars only in the second year. The highest results in terms of biological yield and protein content were obtained in Gürarslan and Çiftçi cultivars. This study revealed that more studies are needed to determine the effects of selenium applications on medicinal and aromatic plants.