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.
Abstract. Disease spot segmentation from crop leaf images is a key prerequisite for disease early warning and diagnosis. To improve the accuracy and stability of disease spot segmentation, an adaptive segmentation method for crop disease images based on K-means clustering is proposed. The approach is based on three stages. First, the excess green feature and the a* component of the CIE (L*a*b*) color space were combined to adaptively learn the initial cluster centers. Second, iterative color clustering of two clusters was conducted using the squared Euclidian distance as the similarity distance. Finally, the distance of a* components between two clusters as the clustering criterion function was used to correct the clustering results. To verify the effectiveness of the proposed method, segmentation experiments were performed on images of three kinds of cucumber diseases and one kind of soybean disease. The results of the experiments were compared with the results obtained using a fixed threshold method, the Otsu method, the traditional K-means clustering method, and the Renyi entropy method, which showed that our adaptive segmentation method was accurate and robust for segmentation of crop disease images. Keywords: Adaptive, CIE L*a*b*, Disease spot, Image segmentation, K-means clustering.
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.
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