The boom in the electronics industry has made a variety of credit card-sized computer systems and plenty of accompanying sensing and acting elements widely available, at continuously diminishing cost and size levels. The benefits of this situation for agriculture are not left unexploited and thus, more accurate, efficient and environmentally-friendly systems are making the scene. In this context, there is an increasing interest in affordable, small-scale agricultural robots. A key factor for success is the balanced selection of innovative hardware and software components, among the plethora being available. This work describes exactly the steps for designing, implementing and testing a small autonomous electric vehicle, able to follow the farmer during the harvesting activities and to carry the fruits/vegetables from the plant area to the truck location. Quite inexpensive GPS and IMU units, assisted by hardware-accelerated machine vision, speech recognition and networking techniques can assure the fluent operation of a prototype vehicle exhibiting elementary automatic control functionality. The whole approach also highlights the challenges for achieving a truly working solution and provides directions for future exploitation and improvements.

https://www.mdpi.com/2075-1702/9/4/82/pdf

A Synergy of Innovative Technologies towards Implementing an Autonomous DIY Electric Vehicle for Harvester-Assisting Purposes

Public health emergencies have brought great challenges to the stability of the e-commerce supply chain. Demand forecasting is a key driver for the sound development of e-commerce enterprises. To prevent the potential privacy leakage of e-commerce enterprises in the process of demand forecasting using multi-party data, and to improve the accuracy of demand forecasting models, we propose an e-commerce enterprise demand forecasting method based on Horizontal Federated Learning and ConvLSTM, from the perspective of sustainable development. First, in view of the shortcomings of traditional RNN and LSTM demand forecasting models, which cannot handle multi-dimensional time-series problems, we propose a demand forecasting model based on ConvLSTM. Secondly, to address the problem that data cannot be directly shared and exchanged between e-commerce enterprises of the same type, the goal of demand information sharing modeling is realized indirectly through Horizontal Federated Learning. Experimental results on a large number of real data sets show that, compared with benchmark experiments, our proposed method can improve the accuracy of e-commerce enterprise demand forecasting models while avoiding privacy data leakage, and the bullwhip effect value is closer to 1. Therefore, we effectively alleviate the bullwhip effect of the entire supply chain system in demand forecasting, and promote the sustainable development of e-commerce companies.

Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development

https://doi.org/10.1016/j.cie.2022.108859

Supply chain risk management with machine learning technology: A literature review and future research directions

Does Logistics Efficiency matters? -evidence from Green Economic Efficiency side

Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

/pdf/artificial-intelligence-in-food-safety-a-decade-review-and-1zgeu949.pdf

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

The sustainability and profitability of fresh produce supply chains are contingent upon several risk factors. This work, therefore, examines several risk indicators that affect the quality and safety of fresh produce in transit, including technological, biological, sustainability, environmental, and emergency risks. Then, we developed a risk assessment and monitoring model that employs a machine learning algorithm, a support vector machine, based on historical monitoring data. The proposed methodology was then applied to simulation and numerical analysis to assess the risks incurred in the strawberry cold chain. After training, the algorithm predicted the risks incurred during transportation with an average accuracy of 90.4%. Therefore, the developed methodology can effectively and accurately perform a risk assessment. Furthermore, the risk assessment model can be applied to other fresh produce due to comprehensive risk indicators. Decision-makers in fresh produce logistics companies can use the developed methodology to identify and mitigate risks incurred, thus improving food safety, reducing product loss, maximizing profits, and realizing sustainable development.

https://www.mdpi.com/2071-1050/12/18/7569/pdf

Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine

• Optimize Bundle Pricing Strategy by considering advance selling and consumer regret • Ignoring consumer inaction regret hurts profit more than ignoring the action regret • Bundle products of large cost difference can reduce negative effect of regret on profit • Sell high-profit products to high-level regret consumers can improve profit • Sell low-profit products to low-level regret consumers can mitigate negative effect of regret In making purchase decisions, consumers often anticipate regret and take their remorse into consideration. In this research, we examine the impact of consumer's anticipated regret (e.g., action regret and inaction regret ) on mixed bundle strategy in advance selling given uncertain consumer valuation. A consumer might experience action regret if she accepts a discount price associated with advance selling or bundling; or inaction regret if she rejects the discount and feels remorse. We propose an analytical model to optimize two-stage bundle pricing. The pricing model maximizes retailer's profit by taking into account consumer regret when facing advance selling. We find consumer regret has significant implications for retailers. When ignoring action regret , the retailer suffers a moderate loss in profit, but overlooking inaction regret would result a much higher loss. Retailers can mitigate the loss by employing advance selling. To maximize profit, retailers can bundle two products of big price difference to reduce the negative effect of consumer regret. Sensitivity analysis shows that the retailer can ignore consumer regret when bundling two products of moderate unit-profit contributions as the regret-induced profit loss is negligible. We also find for consumers of high regret level, retailers should sell high-profit products and bundles. In contrast, retailers should sell low-profit products and bundles to low-regret consumers to reduce the negative effect of regret behavior. 

Optimizing Mixed Bundle Pricing Strategy: Advance Selling and Consumer Regret

The rebound effect of decarbonization in China’s power sector under the carbon trading scheme

The energy rebound effect in households: Evidence from urban and rural areas in Beijing

The electric vehicle industry faces intense competition and the sustainability problem. In order to obtain a differential competitive advantage, enterprises actively promote the battery swapping mode (BSM) to respond to cost pressures caused by the mismatch between demand and supply. Considering cascade utilization, the Stackelberg game models of electric vehicle supply chain under three different scenarios, in the secondary supply chain consisting of a battery manufacturer (BM) and a vehicle manufacturer (VM), were constructed, respectively. Additionally, then, through the contrastive analysis of differential power structures, the influence of power structures and related parameters on the optimal pricing strategy and enterprise profits of both parties in the supply chain were studied and compared. The conclusions show that the dominance of the supply chain determines the profit level of enterprises in BSM. Compared with VMs, the adoption of the BSM has provided BM greater profit growth. Secondly, the number of reserve batteries in the battery swapping stations and the revenue of cascade utilization are essential factors affecting the profits of battery swapping service (BSS), especially for VMs. In addition, setting a reasonable range for the pricing of BSS can achieve a win–win situation for both manufacturers.

/pdf/long-term-leases-vs-one-off-purchases-game-analysis-on-3nelog3z.pdf

Guohao Li

Papers

Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine

Optimizing Mixed Bundle Pricing Strategy: Advance Selling and Consumer Regret

The rebound effect of decarbonization in China’s power sector under the carbon trading scheme

The energy rebound effect in households: Evidence from urban and rural areas in Beijing

Long-Term Leases vs. One-Off Purchases: Game Analysis on Battery Swapping Mode Considering Cascade Utilization and Power Structure