Today, sweet corn is considered an important vegetable due to its high sugar content and low starch content. Cluster analysis and variance analysis showed that hybrids had variations in yield indices. GB, DE and GS hybrids had similar performance on indices. SE hybrid that has significant performance on zeaxanthin. Biplot showed that fructose, glucose, sucrose and potassium had stability value on hybrids. All the hybrids had the best performance on fructose, glucose, sucrose and potassium factors. Factor biplot positively correlated with yield indices, including calcium, iron, zinc, magnesium, α-Carotene, 9Z-β-Carotene, phosphorus, and β-carotene. On the other hand, there is a positive correlation with fructose, glucose, potassium, lutein, sucrose, β-Cryptoxanthin, and zeaxanthin. So, to evaluate or increase lutein and zeaxanthin, the other parameters like sugar content (fructose, glucose, and sucrose) are important factors and have an effect together. Factor analysis and biplot showed that ME hybrid had a maximum performance on the first factor of yield indices. Also, the second factor of yield indices had a maxi-mum effect on NO hybrids. SE hybrids had maximum performance in zeaxanthin and GS hybrid had maximum performance in zinc, phosphorus, and iron. The dry matter had stability on DB hybrid.

Faba bean (Vicia faba L.) is an important grain legume crop in many countries and is severely constrained by infection by the weedy root parasite Orobanche crenata (crenate broomrape). The present work was carried out to evaluate the potential of arbuscular mycorrhiza fungi (AMF) and bacterial strains to suppress Orobanche crenata on faba bean. Orobanche infestation was influenced by the bacteria, AM fungi and the time the observation was made. Faba bean inoculated with the combination between bacterial strains (B2) {TAL 1399 plus A. brasilense}, B3 (TAL 1399 plus BMP{ Bacllius megathirium var phosphaticum }) alone or in combination with mycorrhiza fungi (AM) were completely inhibited Orobanche plant emergence. Time at which highest rate of Orobanche emergence occurred on faba bean was significantly delayed with AM fungi incorporated with each of the bacterial strains (B1, B2 and B3). The highest increment of faba bean shoot was obtained when AM fungi was incorporated with bacterial strains B3 as compared to control. Mycorrhizal (AM) fungi plus bacterial B2 root colonization were positively correlated with total dry matter of faba bean. With respect to nodule numbers results displayed that faba bean inoculated with bacterial B2 alone or in combination with AM fungi sustained the highest nodule numbers as compared to both controls. Adoption of an integrated approach encompassing AM fungi and bacteria inoculation may provide a novel, cheap and easy method to apply for O. crenata control under subsistence low-input farming systems.

Water stress is a major challenge that limits the growth, development, and yield of maize (Zea mays L.) worldwide, especially under climate change, particularly abiotic stresses. This review presents a comprehensive bibliometric and literature-based analysis of research on maize’s response to drought and water scarcity from 1975 to 2025, using VOS viewer1.6.20 software, facilitating the detection of co-authorship networks, thematic groupings, and patterns of keyword co-occurrence within the selected publications. Data from the Web of Science were examined to assess publication trends, keyword networks, and international collaborations. A literature search was conducted by combining the keywords ((“maize”) OR (“corn”) AND (“drought”) OR (“water stress”) AND (“yield”)). Relevant studies were retrieved from the Web of Science (WoS) database using this search string. The Mann–Kendall test revealed a significant positive trend (p = 0.001) in publications on water scarcity (R² = 0.8526), with 396 relevant studies identified globally, regardless of language. The analysis of publication trends demonstrated a statistically significant increase in the volume of publications over the examined period, featuring major contributions from Kenya, Switzerland, Mexico, China, and the United States. The most influential publication focuses on a biotic stressor that significantly reduces maize grain yield. These results emphasise the need for integrated strategies that combine genetic improvement and sustainable irrigation to mitigate the impacts of water stress. This comprehensive analysis provides a foundation for guiding future research and policy development to improve maize resilience against the effects of water stress under changing climatic conditions.

Artificial Intelligence (AI) and the Internet of Things (IoT) are transforming modern agriculture by enabling precision farming through advanced sensors, real-time data analytics, and satellite imagery. These technologies allow precise monitoring and management of soil health, nutrients, water, crop diseases, and yields, supporting site-specific interventions that optimize resources, increase productivity, and promote sustainability. Integrating AI analytics with IoT sensors provides farmers with detailed, real-time field insights beyond traditional methods. Controlled environments like greenhouses and vertical farms further enhance efficiency by regulating climate and nutrients, reducing food waste, and improving market access. Adoption challenges include high costs, technical complexity, data privacy, and sociocultural barriers, especially for smallholders in developing areas. Overcoming these through policy, education, and infrastructure is vital to unlock AI and IoT's full potential in sustainable food production and global food security amid growing pressures.

Precision agriculture (PA) has emerged as a data-driven approach for improving maize (Zea mays L.) production through the integration of remote sensing, Geographic Information Systems (GISs), Global Navigation Satellite Systems (GNSSs), the Internet of Things (IoT), and machine learning (ML). This systematic review evaluates the application of PA for yield optimisation and resource-use efficiency in maize production between 2020 and 2026. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, 464 records were identified from Scopus and Web of Science, of which 129 studies met the inclusion criteria. The reviewed literature comprised field experiments (28.6%), remote sensing and PA integration studies (25.5%), machine learning applications (19.4%), climate-informed PA strategies (23.4%), and soil degradation studies (3.1%). Remote sensing and integrated multi-technology systems were the most extensively investigated approaches, followed by ML-based models for yield prediction and crop monitoring. Overall, PA technologies enhanced maize productivity, nutrient-use efficiency, water-use efficiency, and yield prediction accuracy through site-specific management and data-driven decision support. Despite its considerable potential, the adoption of PA remains constrained by high implementation costs, technical complexity, and limited data availability. Collectively, these findings demonstrate that precision agriculture provides an effective framework for sustainable maize intensification by improving productivity, optimising resource-use efficiency, and strengthening resilience to climate variability.