Artificial intelligence (AI) and machine learning (ML) have emerged as transformative forces in the field of cardiovascular medicine. The increasing ...

The field of bioinformatics has become a cornerstone of the One Health approach, which recognizes the interconnectedness of human, animal, and environmental ...

Illinois farmers are increasingly using drones for crop scouting, pest detection, and NDVI imagery to monitor plant health, while AI-driven analytics expand from agriculture to turf and lawn care.

AI technologies are increasingly being integrated into agriculture and gardening, from advanced irrigation and pest detection systems to consumer-grade smart plant care devices. Researchers and ...

A review published in Agriculture outlines how integrating these tools into farming practices could play a decisive role in ...

A new satellite-based analytical framework enables accurate estimation of crop sowing and emergence dates at the field scale. By integrating daily ...

A new satellite-based analytical framework enables accurate estimation of crop sowing and emergence dates at the field scale.

Crop Disease Detection using Machine Learning is a CNN-based system that identifies crop diseases from leaf images and provides preventive measures, helping farmers detect diseases early and reduce ...

ABSTRACT: Accurate prediction of water travel time in drip irrigation systems is essential for efficient water and nutrient delivery. This study develops a predictive model for travel time by ...

Abstract: Crop diseases remain a major threat to global agricultural productivity, particularly in resource-constrained regions, where early intervention is critical to ensuring food security.

A new study shows that machine-learning models can accurately predict daily crop transpiration using direct plant measurements and environmental data. By training models on seven years of ...