A research team leverages chlorophyll fluorescence (ChlF) imaging, a cutting-edge technique, to identify reliable pre-symptomatic diagnostic indicators for rice blast and brown spot.

A research team has developed an innovative remote sensing model that significantly improves the accuracy of monitoring nitrogen accumulation in wheat crops.

A research team now proposes a novel Multi-Granularity Alignment (MGA) domain adaptation framework that dramatically improves cross-domain detection accuracy, enabling deep learning models to maintain high performance across datasets collected in different countries and conditions.

A research team pioneers an automated, high-throughput pipeline using open-source tools to quantify over 50 root traits in diverse maize genotypes, enabling researchers to investigate how genetics, hormones, and developmental stages influence root growth.