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An orchard precision management system plays an important role in improvement at the management level and the enhancement of decision abilities. A single orchard tree or an orchard tree microcommunity is the basic management unit, and bidirectional information on the environment and plants is the important content for precision management. A type of RFID label was applied with a UHF chip in the core and a QR code in the surface for single tree identification. A bidirectional acquisition system for orchard production, which included farming information collection for the forward direction and environmental information acquisition for the backward direction, was designed with smart phones. In the farming information collection part, information collection flow that included QR code image acquisition, image preprocessing, barcode decoding and farming information collection was established. An improved local threshold method was adopted to improve the QR code identification rate in the smart phone platform. In the environment information acquisition part, a sensor search rule on the single tree position and a multi-point environment value model were designed. The orchard information bidirectional acquisition system was developed on an Android platform with the Java language, which has the function of QR decoding, farm record information collection, environment information acquisition, data uploading and statistical analysis. The system was tested in an apple orchard. A total of 144 trees were chosen to decode the QR codes in the tree label. The success rate was approximately 96.52%. The identification time of 85% of the trees was less than 4 s for the 20 chosen trees. In taking the temperature, for example, the difference between the computed temperature value and the measured temperature value around each tree was small. The system could decrease the cost of the professional equipment, such as portable RFID readers and writers, which was a low-cost and high-efficiency solution for orchard production information collection. [ABSTRACT FROM AUTHOR]

Abstract: Wheat flour undergoes several processing steps in its transformation from raw wheat in the mill, which differentiates wheat flour from other farm products. At each step, various wheat sources are combined into one batch of wheat flour. This study primarily aimed to develop a Wheat Flour Milling Traceability System (WFMTS), incorporating 2D barcode and radio frequency identification (RFID) technology, and to validate the system in a wheat flour mill in China. We designed the encoding rules for the raw material, processing and traceability batches. Labels with a Quick Response Code (QR Code) were attached to small packages of wheat flour to link them to their processing information, and RFID tags were affixed to the storage bins to record logistics information. A traceability system was developed based on batch identification and record keeping. The system was applied and supported in a wheat flour mill for one year. The WFMTS management and traceability capacity was evaluated using a contrast experiment. The experiment was divided into five parts, including raw material data recording, processing data recording, package data recording, logistics data recording and traceability query. The results show that although time consumption using WFMTS in package data recording was more than that with paper recording, WFMTS was dominant in its total time consumption: five parts were reduced by 113%, and the mean accuracy of the five parts increased by 8%. The QR Code and RFID recognition accuracy was evaluated using experiments with different reading distances. The cost and income variations in application WFMTS were analyzed based on the survey. The results show that the total cost increased by 17.2% to apply the system. Compared to the cost, the sales income increase was obvious, and it reached 32.5%. Considering the good evaluation results, the system has good application potential in medium or large wheat mill enterprises. [Copyright &y& Elsevier]

Abstract: For the small-scale and scattered fresh cucumber production in China, the result that production record-keeping and its transfer are inefficient have prevented the wide application of traceability systems in China. With the mobility and computability, Personal Digital Assistant (PDA) provides a new way for agricultural information collection to solve the above problems. Thus a PDA-based Record-keeping and Decision-support System (PRDS) for traceability in cucumber production was developed on Windows Mobile platform invoking a Geographic Information System (GIS) control. For improving the decision making feasibility of PRDS, the fertilization recommendation model and pesticide usage early warning model were developed by using the Technical Specification of Balanced Fertilization by Soil Testing and the Guideline for Safety Application of Pesticides in China. The architecture of PRDS was provided. With Unified Modeling Language (UML), a requirement model including two types of users and 17 use cases was described, and a static class model was also designed, which consisted of table class, table operation class, algorithm class and interface class. Based on these models, the functions of system setup, map management, data management, production record-keeping and decision-support and query, etc., were implemented by adopting Hosting MapInfo MapX Mobile Controls on the .NET Compact Framework 2.0, and the data synchronization was realized by Remote Data Access (RDA). Two agricultural production enterprises were chosen as case study to evaluate the system by questionnaires. The results show that the efficiency of production record-keeping and decision-support is improved by the simple and friendly system. [Copyright &y& Elsevier]

Abstract: Treatment during the primary infection phase is essential for controlling cucumber downy mildew in solar greenhouses. An early warning model applicable to this phase would represent a foundation for early warning systems for managing the disease and reducing pesticide usage. Based on the input parameters that were both readily available and appropriately limited in number, EWMPICDW (early warning model for primary infection of cucumber downy mildew in solar greenhouses) was developed based on monitoring data, early warning theory and plant disease epidemiology. The elaboration of this model included clarification of the meaning of warning, monitoring the warning indicators, forecasting the warning situation, tracing the warning sources and controlling the warning situation. The definition of warning included disease occurrence (yes or no) and probability. Because the leaf wetness duration (LWD) played an important role in disease warning systems for crops in solar greenhouses and was difficult to monitor, the leaf wetness sensor and RH threshold model were investigated and combined to form a practical estimation solution for LWD. Within the warning situation forecasting model system, the infection condition and incubation early warning submodels received the most attention. The infection condition early warning submodel was developed by using a threshold method based on the combination of LWD and mean temperature in LWD. The temperature was chosen as the warning indicator for incubation, and the incubation early warning submodel was defined using nonlinear regression methods. The warning sources traceability algorithm was developed in relation to expert knowledge and in terms of a mode of disaster mitigation that involved cutting the disaster chain from the headstream. The method for controlling the warning situation was based on good agricultural practices (GAP). The early warning model was implemented as a system and was evaluated using data for 4 years at two sites in Beijing, China. The warnings can be provided more than 2 d before symptoms appear. Using EWMPICDW, a positive early warning is associated with a change in the chance of disease occurrence from 0.68 to 0.96. Accordingly, the probability of disease occurrence calculated for the early warning model was 96%. These results demonstrate that the data-driven model will support the development of early warning systems for primary infection by cucumber downy mildew in solar greenhouses. [Copyright &y& Elsevier]