The development of modern agriculture has prompted the greater input of herbicides, insecticides, and fertilizers. However, precision release and targeted delivery of these agrochemicals still remain a challenge. Here, a pesticide-fertilizer all-in-one combination (PFAC) strategy and deep learning are employed to form a system for controlled and targeted delivery of agrochemicals. This system mainly consists of three components: (1) hollow mesoporous silica (HMS), to encapsulate herbicides and phase-change material; (2) polydopamine (PDA) coating, to provide a photothermal effect; and (3) a zeolitic imidazolate framework (ZIF8), to provide micronutrient Zn2+ and encapsulate insecticides. Results show that the PFAC at concentration of 5 mg mL–1 reaches the phase transition temperature of 1-tetradecanol (37.5 °C) after 5 min of near-infrared (NIR) irradiation (800 nm, 0.5 W cm–2). The data of corn and weed are collected and relayed to deep learning algorithms for model building to realize object detection and further targeted weeding. In-field treatment results indicated that the growth of chicory herb was significantly inhibited when treated with the PFAC compared with the blank group after 24 h under NIR irradiation for 2 h. This system combines agrochemical innovation and artificial intelligence technology, achieves synergistic effects of weeding and insecticide and nutrient supply, and will potentially achieve precision and sustainable agriculture.

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Network embedding has attracted considerable attention in recent years. It represents nodes in a network into a low-dimensional vector space while keeping the properties of the network. Some methods (e.g. ComE, MNMF, and CARE) have been proposed to preserve the community property in network embedding, and they have obtained good results in some downstream network analysis tasks. However, there still exists a significant challenge because nodes may lose important structural information following embedding. To address this problem, we propose a community structure enhancement framework for network embedding, based on edge reweighting. Through edge reweighting, the weight of intra-community edges is increased while the weight of inter-community edges is decreased. Therefore, after embedding, nodes in the same community are closer to each other than nodes in different communities in the embedding space. We apply the edge reweighting as a preprocessing stage in network embedding, and construct an enhanced network by incorporating enhanced community structures into the original network. By doing this, the embedded vectors from the enhanced network can better perform all downstream network analysis tasks. Extensive experiments are conducted on two network analysis tasks (community detection and node classification) with synthetic and real-world datasets. The results show that our method outperforms state-of-the-art network embedding methods.

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Mobile crowd sensing (MCS) is considered as a powerful paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data. However, MCS assumes all workers always are trusted, and thus offering opportunities for malicious workers to conduct the crowd sensing data falsification (CSDF) attack. To suppress such threat, recent efforts have been made to trust mechanism. Currently, some malicious workers can collude with each other to form a collusive clique, and thus not only increasing the power of CSDF attack but also avoiding the detection of trust mechanism. To ensure honest data collection in MCS, we must fight against such collusive CSDF attack. Noting that the duality of sensing data, we propose a defense scheme called BMCA from the design idea of binary-minmaxs clustering analysis to suppress collusive CSDF attack. In the BMCA scheme, the logic AND operation corresponding to the type of ''1'' and ''0'' historical sensing data is used to measure the similarity between any two workers. Based on this, we find the feature that collusive CSDF attackers usually hold high trust value and a low variance in their similarity vector. To detect collusive CSDF attackers, the min and max variance analysis is introduced to design a new binary-minmaxs clustering algorithm. Moreover, the BMCA scheme can perfect trust evaluation to prevent the trust value growth of collusive CSDF attackers. Simulation results show that the BMCA scheme can enhance the accuracy of trust evaluation, and thus successfully reducing the power of collusive CSDF attack against data collection in MCS. INDEX TERMS Mobile crowd sensing, trust mechanism, clustering analysis, collusive attack.