Farm Management Information Systems (FMISs) are being expanded to improve operation efficiency, reduce inputs, and ensure compliance with standards and regulations. However, this goal is difficult to attain in the vegetable sector, where data acquisition is time-consuming and data at different stages is fragmented by the potential diversity of crops and multiple batches cultivated at any given farm. This applies, in particular, to farms in China, which have small areas and low degrees of mechanization. This study presents an integrated approach to track and trace production efficiently through our Digital Farm Management System (DFMS), which adopts the cloud framework and utilizes Quick Response (QR) codes and Radio Frequency Identification (RFID) technology. Specifically, a data acquisition system is proposed that runs on a smartphone for the efficient gathering of planting information in the field. Moreover, DFMS generates statistics and analyses of planting areas, costs, and yields. DFMS meets the FMIS requirements and provides the accurate tracking and tracing of the production for each batch in an efficient manner. The system has been applied in a large-scale vegetable production enterprise, consisting of 12 farms distributed throughout China. This application shows that DFMS is a highly efficient solution for precise vegetable farm management.

Combining ability analysis can be used to preliminarily identify the advantages and disadvantages of combinations and parents in earlier generations, enabling breeders to reduce the range of material, save breeding time, and improve breeding efficiency. An approach for combining ability analysis through the hybrid crop breeding information management system is presented. The general combining ability prediction effect of parents and the specific combining ability prediction effect of combinations are calculated to analyze hybrid combinations using the hybrid crop breeding information management system. The results provide the basis for parent selection and combination selection. The plant breeding trial management function of the system can provide convenient diallel crossing trial design, field planting plan, and combining ability analysis. In the system, the genealogy of breeding materials is traced with the combining ability test crosses. The selection of high-generation breeding materials can be performed in accordance with the combining ability test results of early generation materials. The system has been successfully applied to a large Chinese seed company. The combining ability test function automates data analysis and eliminates days in the decision-making process. The efficiency of the combining ability test analysis and test report generation has improved to more than double by using the system.

Heavy metal pollution in farmlands is a serious threat to sustainable agricultural development and has become a major agro-ecological problem that has attracted public concern in China. This study proposes a soil–crop collaborative risk assessment model that aims to assess the potential safety risks of heavy metal pollution in farmland soils by considering the concentrations of heavy metals in soils and the accumulation effects of heavy metals in crops. Based on these effects, a decision support system for risk assessment of heavy metal pollution in farmland soil is established, in which technologies such as web-based geographic information system, quick response code, radio frequency identification, and web service are introduced as the bases. The proposed system is composed of a mobile data acquisition terminal (MDAT) and a web-based information system (WIS). The MDAT, which is a portable computerized device running on the Android platform, is used for data acquisition or query, and the WIS is used for risk assessment, data management, and information visualization. The system is employed in some county-level cities in China for risk assessment and supervision of heavy metal pollution in farmlands. The practical application results show that the system provides highly efficient decision support for risk assessment of heavy metal pollution in farmland soils.

Genomic selection plays a crucial role in breeding programs designed to improve quantitative traits, particularly considering the limitations of traditional methods in terms of accuracy and efficiency. Through the integration of genomic data, breeders are able to obtain more accurate predictions of breeding values. In this study, we proposed and evaluated four deep learning architectures—CNN-LSTM, CNN-ResNet, LSTM-ResNet, and CNN-ResNet-LSTM—that are specifically designed for genomic prediction in crops. After conducting a comprehensive evaluation across multiple datasets, including those for wheat, corn, and rice, the LSTM-ResNet model exhibited superior performance by achieving the highest prediction accuracy in 10 out of 18 traits across four datasets. Additionally, the CNN-ResNet-LSTM model demonstrated notable results, showcasing the best predictive performance for four traits. These findings underscore the efficacy of hybrid models in identifying complex patterns, as they integrate skip connections to mitigate the vanishing gradient problem and enable the extraction of hierarchical features while elucidating intricate relationships among genetic markers. Our analysis of SNP sampling indicated that maintaining SNP counts within the range of 1000 to the full set significantly influences prediction efficiency. Furthermore, we conducted a comprehensive comparative analysis of predictive performance among random selection, marker-assisted selection, and genomic selection utilizing wheat datasets. Collectively, these results provide significant insights into crop genetics, enhancing breeding predictions and advancing global food security and sustainability.

The rapid increase in the number of new maize varieties and the intensification of market competition have raised the need to precisely promote new maize varieties to suitable planting areas and fully exploit the variety potential and win the market competition. This paper proposes a precise recommendation method for suitable planting areas of maize varieties based on a knowledge graph. The meteorology knowledge graph of maize ecological regions is constructed at county-scale and a RippleNet recommendation model is used to mine the potential spatial correlation of maize variety suitability in different meteorological environments. The county-scale precise recommendation for suitable planting areas is then realized. In total, 331 maize varieties and agricultural meteorological data of 59 experimental areas in the Huang-Huai-Hai ecological region are used for model training and testing (accuracy 76.3%). Through experimental comparison, the recommendation accuracy of this method is 24.3% higher than that of six traditional machine learning methods, 11.2% higher than that of graph attention networks, and 5.8% higher than that of graph convolution neural networks. This study provides a data-driven solution for the precise recommendation and market positioning of maize varieties, enhances the scientificity of variety recommendation and helps to fully exploit their planting potential.

The phenotype (P) of a crop is determined by the genotype (G), environment (E), and genotype-by-environment (G × E) interaction, expressed as P = G + E + G × E. Thus, studying G × E interactions is essential for phenotypic research. Traditional methods of crop phenotypes and adaptability based on G × E interaction analysis, based on large ecological regions, fail to account for year-to-year environmental changes and the blurring of region boundaries, leading to inaccurate insights into the relationship between genotypes and environmental factors. To address these issues, this study divided the research area into small ecological regions through the clustering of meteorological data, providing a more accurate framework for studying G × E interactions in maize. To ascertain the optimal method for ecological region delineation, the yield variance (SYV), the Davies–Bouldin Index (DBI), and the Silhouette Index (SI) were used to evaluate and compare the performance of the K-Means, Autoencoder K-Means (Ae-KM), and Deep K-Means Clustering Neural Network (DKMCNN) methodologies. The DKMCNN surpassed other methodologies and was selected for delineation. Based on this delineation result, the interactions between genotypes and the environment on maize were investigated and clarified using genome-wide association analysis (GWAS) and analysis of variance (ANOVA). Ultimately, through the analysis of maize field trial data from 2020 to 2021, we identified up to 108 single-nucleotide polymorphisms (SNPs) in 2020 and 153 SNPs in 2021 that exerted significant effects on maize yield and exhibited strong correlations with environmental factors, including temperature, cumulative precipitation, and cumulative sunshine duration.