The range measurement is the premise for location, and the precise range measurement is the assurance of accurate location. Hence, it is essential to know the accurate internode distance. It is noted that the path loss model plays an important role in improving the quality and reliability of ranging accuracy. Therefore, it is necessary to investigate the path loss model in actual propagation environment. Through the analysis of experiments performed at the wheat field, we find that the best fitted parametric exponential decay model (OFPEDM) can achieve a higher distance estimation accuracy and adaptability to environment variations in comparison to the traditional path loss models. Based on the proposed OFPEDM, we perform the RSSI-based location experiments in wheat field. Through simulating the location characteristics in MATLAB, we find that for all the unknown nodes, the location errors range from 0.0004 m to 5.1739 m. The location error in this RSSI-based location algorithm is acceptable in the wide areas such as wheat field. The findings in this research may provide reference for location estimation in large-scale farmland.

Personalized recommendation technology is a valid way to solve the problem of “information overload”. In the face the complexity of agricultural field information and problems of farmers’ preference prediction accuracy which is not high, this paper proposes a kind of recommendation algorithm based on context-aware and collaborative filtering (CACF). The algorithm constructs the “user-item-context” 3D user interest model which contains the context information. Through calculating context similarity and adopting pre-filtering paradigm, the 3D model is reduced to “user-item” 2D model. By computing item similarity, it can predict the item rating and generate recommendations. The CACF was applied on the field information recommendation. The experimental results show that the CACF can accomplish higher recommendation precision and efficiency compared with the traditional User-based collaborative filtering algorithm (UBCF), Slope one algorithm (SLOA).

Limited by the resource characteristics of the agricultural information cloud platform, the existing resource allocation methods have higher energy consumption when the user density is high. Therefore, a resource allocation method based on cluster mapping is proposed for the agricultural information cloud platform. Based on the technology of cluster mapping, the resource allocation framework of agricultural information cloud platform is constructed, and then the dormancy allocation of cluster resources is proposed, and the coordination and optimization of resource allocation among clusters are carried out. The simulation results show that the resource allocation method of agricultural information cloud platform based on cluster mapping has good robustness and can reduce energy consumption.

Aiming at low management automation level, heavy workload and other problems that exist in traditional vineyard production, a vineyard production monitoring system is designed based on wireless sensor network technology. The system includes 54 sensor nodes, 3 cluster-head nodes, a sink node and monitoring center. It is free to customize automatic acquisition, processing, transmission, storage of soil moisture rate that from three soil layers separately buried apart from surface 0-20, 20-40, and 40-60 cm, the change of soil temperature and illumination intensity. Each node employs JenOS operation system, and the communication between nodes follows ZigBee protocol. Comprehensively considering the cost, energy consumption, usability and other factors, this system selects the TP-ST series sensors. Experiments of data package transmission rate in budding leafing stage and fruit rapid-expanding stage are taken. Six out of ten sensor nodes have the data package transmission rate above 90%, two sensor nodes below 35%. The reason is that the energy consumption of one solar energy power circuit is excessive, and the other node is deployed where the grape trellis blocking is serious. In addition, the data package transmission rate of fruit rapid-expanding stage is generally lower than that of grape budding leafing stage. This phenomenon shows that the density of branch and fruit has certain influence on the transmission of wireless signal.

The crop growth environmental information in farmland such as temperature and humidity has a gentle variation amplitude in a certain time or space domain, and is sparse in the discrete cosine transform domain. Based on compression sensing, we construct the approach to crop growth environmental information monitoring. The reconstruction error for crop growth environmental information is validated through the simulation experiment. By exploiting orthogonal matching pursuit method, it is found that the reconstructed environmental information data are consistent with the measured data.