Estimation of soybean yield based on high-throughput phenotyping and machine learning.

Introduction: Soybeans are an important crop used for food, oil, and feed. However, China's soybean self-sufficiency is highly inadequate, with an annual import volume exceeding 80%. RGB cameras serve as powerful tools for estimating crop yield, and machine learning is a practical method based on various features, providing improved yield predictions.

Soybean leaf estimation based on RGB images and machine learning methods.

Background: RGB photographs are a powerful tool for dynamically estimating crop growth. Leaves are related to crop photosynthesis, transpiration, and nutrient uptake. Traditional blade parameter measurements were labor-intensive and time-consuming.

Natural variation at the DEP1 locus enhances grain yield in rice.

Grain yield is controlled by quantitative trait loci (QTLs) derived from natural variations in many crop plants. Here we report the molecular characterization of a major rice grain yield QTL that acts through the determination of panicle architecture. The dominant allele at the DEP1 locus is a gain-of-function mutation causing truncation of a phosphatidylethanolamine-binding protein-like domain protein.