The rapid development of agricultural cyber-physical systems sheds new light on facilitating agricultural production. The grazing behavior recognition of herded sheep is a paramount issue in animal husbandry. Triaxial accelerometers of agricultural cyber-physical systems provide fine-grained observations of herded sheep but also generate temporal-spatial correlated acceleration data with inherently large-scale dimensions and massive volumes. These inherent characteristics of the data constrain the direct application of existing recognition algorithms. Motivated by the unique features of triaxial accelerometers of agricultural cyber-physical systems, we design a hybrid temporal-spatial fuzzy deep neural network (TSFDNN) approach for predicting the grazing behaviors of herded sheep. We first extract temporal-spatial features and reduce data dimensionality using bidirectional long short-term memory network (Bi-LSTM) and convolutional neural network (CNN) in parallel, then control feature dimensions through principal component analysis (PCA), and finally use fuzzy neural network (FNN) to achieve feature enhancement and category mapping. The superiority of the designed TSFDNN is demonstrated through its empirical comparison with other state-of-the-art machine learning algorithms by using two datasets from sheep pastures. Furthermore, we analyze the rationale of each component in the designed TSFDNN by performing several ablation studies. We also conduct robustness experiments with heterogeneous dimension reduction and optimization algorithms to explore the generalization capabilities of TSFDNN. The managerial implications of precisely identifying herded sheep behaviors for production decision-making, agricultural management, animal welfare, and ecological protection are discussed.

Deep neural networks (DNNs) have achieved remarkable progress in remote sensing image (RSI) analysis, yet their vulnerability to subtle adversarial perturbations poses a critical threat to safety-critical applications such as environmental monitoring. While black-box attacks have garnered attention for their practicality, existing methods face a dilemma in RSI scenarios: restricted attacks often result in compromised image quality and limited stealthiness, whereas unrestricted attacks risk degrading transferability. To address this challenge, this paper proposes the latent-space adversarial diffusion attack framework (LADA), which focuses on balancing stealthiness and transferability in adversarial attacks against RSI models. LADA employs a pre-trained diffusion model to map high-resolution RSIs into a low-dimensional latent space, enabling semantic-level perturbation optimization while avoiding pixel-wise explicit noise. Additionally, text prompts are automatically generated using a large multimodal model to guide adversarial sample synthesis, ensuring semantic consistency. To enhance perturbation search efficiency in the latent space, a hybrid strategy combining multi-scale sampling and covariance matrix adaptation evolution strategy is introduced. Extensive experiments demonstrate that LADA achieves superior performance across multiple RSI datasets, model architectures, and defense mechanisms. This paper establishes a benchmark for high-stealthiness and high-transferability adversarial attacks, advancing the secure deployment of DNNs in remote sensing applications.

This article addresses the optimization problem of zeroth-order in a distributed setting, where the gradient information is not available in the edge Internet of Things (IoT) clients. The high communication costs and poorer convergence hinder the use of zeroth-order optimization methods in distributed IoT. This article proposes a communication-efficient Distributed adaptive Zeroth-order optimization method (DaZoo). DaZoo is applied to optimize a class of nonconvex optimization problems, where each client can only access zeroth-order information of local functions. To estimate the global gradient, each client uses a population-based feedback strategy to approximate the first-order gradient, which are then aggregated through a central server. A novel global adaptive optimization scheme is devised for DaZoo, making it with the flexibility to adapt to any landscape without the need for manual parameter tuning. Furthermore, sparsification techniques are incorporated into the local model differences to substantially reduce communication overhead. The theoretical findings suggest that DaZoo can reduce iteration complexity compared to the baselines. Case studies on distributed closed-box attacks and large-scale IoT attack detection demonstrate that DaZoo can outperform state-of-the-art methods.

The platform-based agricultural service is receiving popularity in small-scale farming and shows significant advantages in gathering dispersed service requests and matching supply and demand. However, it also generates new challenges, including service traceability, denial and fraud, information security, and privacy issues. Blockchain is an emerging technology that provides a secure and trusted environment to track and manage the service process. In this study, we propose a blockchain-based service platform for efficient agricultural service operations with the support of Internet of Things technology. Following the smart platform, we use the drone plant protection service as an example and develop a new execution procedure for smart contract-based agricultural services. In the proposed procedure, we focus on integrating optimization methods to deal with multiple service requests as well as potential disruption events and establishing detailed interactions among service plans, smart contracts, and physical services. Moreover, we formulate the drone plant protection issue using a mixed-integer linear programming model to obtain the optimal service plan and develop a recovery model to deal with potential disruptions of new order arrival. Finally, we design detailed smart contract terms for drone plant protection services. Results of numerical experiments demonstrate the effectiveness of the developed optimization model in obtaining the optimal service plan before and after disruptions. Also, we verify the applicability and security of the smart contract on the Ethereum platform based on a three-phase functional test and a comprehensive security test.

The flourish of the Internet of Things (IoT) and data-driven techniques provide new ideas for enhancing agricultural production, where evapotranspiration estimation is a crucial issue in crop irrigation systems. However, tremendous and unsynchronized data from agricultural cyber-physical systems bring large computational costs as well as complicate performing conventional machine learning methods. To precisely estimate evapotranspiration with acceptable computational costs under the background of IoT, we combine time granulation computing techniques and gradient boosting decision tree (GBDT) with Bayesian optimization (BO) to propose a hybrid machine learning approach. In the combination, a fuzzy granulation method and a time calibration technique are introduced to break voluminous and unsynchronized data into small-scale and synchronized granules with high representativeness. Subsequently, GBDT is implemented to predict evapotranspiration, and BO is utilized to find the optimal hyperparameter values from the reduced granules. IoT data from Xi'an Fruit Technology Promotion Center in Shaanxi Province, China, verify that the proposed granular-GBDT-BO is effective for cherry tree evapotranspiration estimation with reduced computational time, and acceptable and robust predictive accuracy. Consequently, the precise estimation of crop evapotranspiration could provide operational guidance for plant irrigation, plant conservations, and pest control in the agricultural greenhouse.

Since the outbreak of the COVID-19 pandemic in 2020, China has adopted a zero-clearing policy under closed control. It is rather common for residents who are quarantined at home to buy fresh agricultural products online, when COVID-19 spread in big cities. Many e-commerce platforms are trying to develop online shopping channels for fresh agricultural products. However, negative comments and news about those platforms have been increasing because of several reasons, such as the difference in the quality of fresh products, inadequate categories of commodity and inefficient delivery caused by the shortage of personnel and so on. The smooth daily supply of online fresh agricultural products is conducive to soothing the pessimistic emotions and to encouraging their active obedience to epidemic prevention and control policy. Therefore, it is of great importance to explore the preference characteristics of consumers' online purchase of fresh agricultural products under this critical situation. In this paper, firstly, Pycharm software is used to collect online comment texts of fresh agricultural products on the online platforms with a total of 34,546 pieces of evaluation data. Secondly, the collected data is preformed into the text preprocessing. To be specific, the obtained online comments are processed by Python, including the process of text duplication between sentences, text duplication within sentences and short sentence filtering. After that, processed texts are subjected to Jieba Text Segmentation to form the final word frequency ranking, involving two procedures, part-of-speech tagging and stop-words removal. Lastly, the results of the LDA model indicate the factors that influence consumers' preferences when they purchase fresh agricultural products online. This study could not only identify the typical features of residents' online shopping preference in the context of the spread of COVID-19, but also provide pragmatic suggestions for the local government to appease the residents' negative emotions for the prevention of widespread complaints at the social level.

The production and distribution planning of fresh produce is a complex optimization problem, which is affected by many factors, including its perishable characteristics. Farmers cannot guarantee the efficiency and accuracy of production and distribution decisions. Given the close relationship between the production and distribution of annual fresh produce, the intention of our research is to solve the two-stage joint planning problem and maximize the revenue of farmers ultimately. The internal relationship matrix between the two links of production and distribution is established. On this basis, we propose a mixed-integer programming (MIP) model, which covers the constraints of labor and capital. The decisions obtained are not only based on price estimation and resource availability but also on the impact of the agricultural Internet-of-Things technology and the special requirements of each distribution channel. Numerical experiments demonstrate that when the planting area is 1, 4, and 6 ha, the proposed joint planning model can improve the distribution revenue of farmers by 7.92%, 4.15%, and 4.94%, respectively, compared with the traditional separate decision-making approach of distribution. According to different decision scenarios, management insights have been obtained. For example, farmers should carefully sort and package products as well as choose a timely and safe third-party express delivery company. Additionally, the proposed strategy can evaluate the impact of distribution channels on farmers' revenue.

Cyber-physical systems and data-driven techniques have potentials to facilitate the prediction and control of product quality, which is one of the two most important issues in modern industries. In this article, we integrate random forest (RF) with Bayesian optimization for quality prediction with large-scale dimensions data, selecting crucial production elements by information gain, and then utilizing sensitivity analysis to maintain product quality. Horizontal empirical experiments are performed to verify the superiorities of RF embedded within Bayesian optimization over classical RF, support vector machine, logistic regression, decision tree, and even background propagation neural network. Besides, we find fewer but critical features handled by RF-Bayesian optimization can realize satisfactory forecast accuracy as well as cost-effective computing time, where we interpret it with Herbert A. Simon's management decision theory and Pareto principle. Consequently, the results could provide managerial insights and operational guidance for product quality prediction and control at the real-life process industry.

In the era of Internet of people and things, big data are merging. Conventional computation algorithms including correlation measures become inefficient to deal with big data problems. Motivated by this observation, we present three fuzzy correlation measurement algorithms, that is, the centroid-based measure, the integral-based measure, and the α-cut-based measure using fuzzy techniques. Data of Shanghai stock price index (SSI) and exchange rates of main foreign currencies over China Yuan from 22 January 2013 to 17 May 2018 are used to check the effectiveness of our algorithms, and, more importantly, to observe the causality relationship between SSI and these main exchange rates. We have observed some findings as follows. First, the usage of the highest, lowest, or closing values in daily exchange rates and stock prices has impact on the significant Granger causes of exchange rates over SSI, but does not produce any opposite cause from SSI to exchange rates. Second, no matter which of our fuzzy measurement algorithms is used, Hongkong Dollar over China Yuan and U.S. Dollar over China Yuan are positively related with SSI, and Euro over China Yuan negatively correlated with SSI is always recognized as a Granger cause to SSI with the significance level being 1%. Finally, both the optimism level and the uncertainty level are observed having impact on the correlation coefficients, but the later brings more significant changes to results of the Granger causality tests.

The connection of physical agriculture with corresponding cyber systems is helpful to achieve precision agriculture. Real-time data from agriculture sensors can provide decision supports to improve the yields and quality of agricultural products, but also bring about challenges one of which is how to mine useful information from these vast amounts of data at acceptable computation costs. To deal with the dimension disaster problem faced by most conventional mining algorithms, in this paper we combine granulation techniques and genetic algorithm (GA) with a support vector machine (SVM) to propose a granular GA-SVM. In the integrated predictor, three granulation methods, that is, Min-Median-Max granulation, Quartile-Median granulation, and fuzzy granulation, are introduced to break down big data in agricultural cyber-physical systems into small-scale granules, and GA is used to find the optimal values of SVM penalty parameter and kernel parameter from the reduced granules. Internet of Things (IoT) data from Luochuan Apple Experimental Demonstration Station in Shaanxi Province, China, verified that the proposed granular GA-SVM predictor is effective to make big data prediction with reduced computation time and equivalent accuracy. Moreover, the predicted environment information could provide guidance for growers achieving precise management of apple planting.

Capturing specific interrelationship among input arguments has great importance in the process of aggregation as they may change the aggregation result significantly, which can lead viable changes in the overall decision outcome. In this study, we attempt to aggregate a set of inputs with certain heterogeneous interrelationship pattern among them. To do this, we introduce a new aggregation operator, which we call the extended geometric Bonferroni mean. We investigate its properties and develop an algorithm to learn its associated parameters based on decision maker's perceived view toward the aggregation process. Moreover, to learn such heterogeneous relationship among the inputs from the data set, we provide a learning algorithm. Examples are given to illustrate the realization of algorithm and to show certain advantages over the existing aggregation operators.

For perishable food products, customer satisfaction mainly reflects on the freshness. Due to the highly value lost in the distribution process, the complexity of perishable food vehicle routing problem increases. So it is important to design an effective distribution route that can minimize the total costs and maximize the freshness state of the delivered products. We propose a multi-objective vehicle routing problem with time windows dealing with Perishability (MO-VRPTW-P). A two-phase heuristic algorithm based on Pareto variable neighborhood search- genetic algorithm considering temporal-spatial distance (STVNS-GA) is applied to solve the problem. Several numerical examples are presented. The results illustrate that the algorithm is effective and efficient.

Prompt medical service and supplies are very important to reduce the life loss in response to disasters. In this work, we focus on how to allocate the limited medical supplies to affected areas in different situations with fuzzy triangular values. Using the a-cut method and Giove's acceptability index, we first propose a method of comparing fuzzy triangular numbers. Then, based on our previous work, we develop a situation-based approach for allocating medical supplies with fuzzy triangular values. A simple example shows the effectiveness of the developed approach.