The integration of machine learning with intelligent sensing tools enables high-accuracy state perception. This allows machines to interpret data on seedling health, soil condition, and crop readiness ...

By leveraging a vision foundation model called Depth Anything V2, the method can accurately segment crops across diverse environments—field, lab, and aerial—reducing both time and cost in agricultural ...

A crop-protection startup is using AI and machine learning to identify and develop new active ingredients it says will help ...

These results strongly validate MGA’s superiority in cross-domain adaptability, significantly improving detection accuracy across varied crop disease datasets and real-world conditions.

Early stress detection via precision agriculture just got a serious upgrade, according to new research out of the Hebrew University of Jerusalem. Led by Dr. Ittai Herrmann, the team developed a ...

Scientists have revealed that Convolutional Neural Networks (CNNs), a type of deep learning algorithm, demonstrate superior performance compared to conventional non-machine learning approaches when ...

About An AI-driven platform offering crop recommendations, fertilizer suggestions, and disease detection for optimal farming - prokriti11/AgroShield-AI-Powered-Crop-Disease-Detection ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes ...

Artificial intelligence (AI) refers to machine-based systems that analyze input data to generate predictions, recommendations, or decisions, 1 AI-based and machine learning (ML)-based technologies ...

Deep learning breakthrough enhances crop disease detection across lab and field Plant diseases remain a major threat to food security and agricultural productivity, especially in resource-constrained ...