Grey leaf spot disease, caused by Pestalotiopsis spp., poses a significant threat to coconut trees, impacting both yield and quality. This study evaluates the potential of indigenous beneficial microorganisms (IBMs) as biological control agents against grey leaf spot disease. Various IBMs were isolated from the rhizosphere and phyllosphere of healthy coconut trees and screened for their antagonistic activity against Pestalotiopsis spp. in vitro. Promising candidates were further assessed in greenhouse and field trials to determine their efficacy in reducing disease severity and enhancing plant health. The results demonstrated that specific IBMs significantly inhibited the growth of Pestalotiopsis spp., reduced lesion formation, and promoted overall plant vigor. These findings suggest that indigenous beneficial microorganisms hold great potential as an eco-friendly alternative to chemical fungicides for managing grey leaf spot disease in coconut trees.

This study investigates the vibratory dynamics and their implications on sugar beet roots when subjected to various digging tools. Vibrations generated by digging tools can affect root integrity and yield, influencing agricultural practices significantly. Understanding these dynamics is crucial for optimizing tool design and operational practices in sugar beet cultivation.

The global population, now at 8 billion and projected to reach 9.7 billion by 2050, necessitates a significant increase in food production. This escalating demand underscores the importance of artificial intelligence (AI) technologies in agriculture, which enhance resource optimization and productivity amid supply chain pressures and more frequent extreme weather events. A systematic literature review (SLR), conducted using the PRISMA methodology, examined AI applications in agriculture, encompassing 906 relevant studies from five electronic databases. From these, 176 studies were selected for bibliometric analysis, with a quality appraisal further refining the selection to 17 key studies. The review highlighted a notable rise in publications over the past five years, identifying over 20 AI techniques, including machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision, as predominant. The research emphasized significant contributions from India, China, and the USA, focusing on sectors like crop management, prediction, and disease and pest management. The study concluded with an analysis of current challenges and future trends, pointing to promising directions for AI in agriculture to meet global food production demands.