A dataset of the quality of soybean harvested by mechanization for deep-learning-based monitoring and analysis.

Deep learning and machine vision technology are widely applied to detect the quality of mechanized soybean harvesting. A clean dataset is the foundation for constructing an online detection learning model for the quality of mechanized harvested soybeans. In pursuit of this objective, we established an image dataset for mechanized harvesting of soybeans. The photos were taken on October 9, 2018, at a soybean experimental field of Liangfeng Grain and Cotton Planting Professional Cooperative in Guanyi District, Liangshan, Shandong, China. The dataset contains 40 soybean images of different qualities. By scaling, rotating, flipping, filtering, and adding noise to enhance the data, we expanded the dataset to 800 frames. The dataset consists of three folders, which store images, label maps, and record files for partitioning the dataset into training, validation, and testing sets. In the initial stages, the author devised an online detection model for soybean crushing rate and impurity rate based on machine vision, and research outcomes affirm the efficacy of this dataset. The dataset can help researchers construct a quality prediction model for mechanized harvested soybeans using deep learning techniques.

OsGRF4 controls grain shape, panicle length and seed shattering in rice.

Traits such as grain shape, panicle length and seed shattering, play important roles in grain yield and harvest. In this study, the cloning and functional analysis of PANICLE TRAITS 2 (PT2), a novel gene from the Indica rice Chuandali (CDL), is reported. PT2 is synonymous with Growth-Regulating Factor 4 (OsGRF4), which encodes a growth-regulating factor that positively regulates grain shape and panicle length and negatively regulates seed shattering. Higher expression of OsGRF4 is correlated with larger grain, longer panicle and lower seed shattering. A unique OsGRF4 mutation, which occurs at the OsmiRNA396 target site of OsGRF4, seems to be associated with high levels of OsGRF4 expression, and results in phenotypic difference. Further research showed that OsGRF4 regulated two cytokinin dehydrogenase precursor genes (CKX5 and CKX1) resulting in increased cytokinin levels, which might affect the panicle traits. High storage capacity and moderate seed shattering of OsGRF4 may be useful in high-yield breeding and mechanized harvesting of rice. Our findings provide additional insight into the molecular basis of panicle growth.