Design of a Smart IoT-Based Control System for Remotely Managing Cold Storage Facilities

A comprehensive review of current approaches on food waste reduction strategies

Food waste is a serious worldwide issue that has an impact on the environment, society, and economy. This comprehensive review provides a detailed description of methods and approaches for reducing food waste, emphasizing the necessity of comprehensive strategies to tackle its intricate relationship with environmental sustainability, social equity, and economic prosperity. By scrutinizing the extent and impact of food waste, from initial production stages to final disposal, this comprehensive review underlines the urgent need for integrated solutions that include technological advancements, behavioral interventions, regulatory frameworks, and collaborative endeavors. Environmental assessments highlight the significant contribution of food waste to greenhouse gas emissions, land degradation, water scarcity, and energy inefficiency, thereby emphasizing the importance of curtailing its environmental impact. Concurrently, the social and economic consequences of food waste, such as food insecurity, economic losses, and disparities in food access, underscore the imperative for coordinated action across multiple sectors. Food waste can also be effectively reduced by various innovative approaches, such as technological waste reduction solutions, supply chain optimization strategies, consumer behavior-focused initiatives, and waste recovery and recycling techniques. Furthermore, in order to foster an environment that encourages the reduction of food waste and facilitates the transition to a circular economy, legislative changes and regulatory actions are essential. By embracing these multifaceted strategies and approaches, stakeholders can unite to confront the global food waste crisis, thereby fostering resilience, sustainability, and social equity within our food systems.

Analysis of Internet of things implementation barriers in the cold supply chain: An integrated ISM-MICMAC and DEMATEL approach

Integrating Internet of Things (IoT) technology inside the cold supply chain can enhance transparency, efficiency, and quality, optimize operating procedures, and increase productivity. The integration of the IoT in this complicated setting is hindered by specific barriers that require thorough examination. Prominent barriers to IoT implementation in a cold supply chain, which is the main objective, are identified using a two-stage model. After reviewing the available literature on IoT implementation, 13 barriers were identified. The survey data were cross-validated for quality, and Cronbach's alpha test was employed to ensure validity. This study applies the interpretative structural modeling technique in the first phase to identify the main barriers. Among these barriers, "regulatory compliance" and "cold chain networks" are the key drivers of IoT adoption strategies. MICMAC's driving and dependence power element categorization helps evaluate barrier interactions. In the second phase of this study, a decision-making trial and evaluation laboratory methodology was employed to identify causal relationships between barriers and evaluate them according to their relative importance. Each cause is a potential drive, and if its efficiency can be enhanced, the system benefits as a whole. The findings provide industry stakeholders, governments, and organizations with significant drivers of IoT adoption to overcome these barriers and optimize the utilization of IoT technology to improve the effectiveness and reliability of the cold supply chain.

An Intelligent Water Monitoring IoT System for Ecological Environment and Smart Cities

Global precipitation is becoming increasingly intense due to the extreme climate. Therefore, creating new technology to manage water resources is crucial. To create a sustainable urban and ecological environment, a water level and water quality control system implementing artificial intelligence is presented in this research. The proposed smart monitoring system consists of four sensors (two different liquid level sensors, a turbidity and pH sensor, and a water oxygen sensor), a control module (an MCU, a motor, a pump, and a drain), and a power and communication system (a solar panel, a battery, and a wireless communication module). The system focuses on low-cost Internet of Things (IoT) devices along with low power consumption and high precision. This proposal collects rainfall from the preceding 10 years in the application region as well as the region's meteorological bureau's weekly weather report and uses artificial intelligence to compute the appropriate water level. More importantly, the adoption of dynamic adjustment systems can reserve and modify water resources in the application region more efficiently. Compared to existing technologies, the measurement approach utilized in this study not only achieves cost savings exceeding 60% but also enhances water level measurement accuracy by over 15% through the successful implementation of water level calibration decisions utilizing multiple distinct sensors. Of greater significance, the dynamic adjustment systems proposed in this research offer the potential for conserving water resources by more than 15% in an effective manner. As a result, the adoption of this technology may efficiently reserve and distribute water resources for smart cities as well as reduce substantial losses caused by anomalous water resources, such as floods, droughts, and ecological concerns.

Impact of Modified Atmosphere Packaging Conditions on Quality of Dates: Experimental Study and Predictive Analysis Using Artificial Neural Networks

Dates are highly perishable fruits, and maintaining their quality during storage is crucial. The current study aims to investigate the impact of storage conditions on the quality of dates (Khalas and Sukary cultivars) at the Tamer stage and predict their quality attributes during storage using artificial neural networks (ANN). The studied storage conditions were the modified atmosphere packing (MAP) gases (CO2, O2, and N), packaging materials, storage temperature, and storage time, and the evaluated quality attributes were moisture content, firmness, color parameters (L*, a*, b*, and ∆E), pH, water activity, total soluble solids, and microbial contamination. The findings demonstrated that the storage conditions significantly impacted (p < 0.05) the quality of the two stored date cultivars. The use of MAP with 20% CO2 + 80% N had a high potential to decrease the rate of color transformation and microbial growth of dates stored at 4 °C for both stored date cultivars. The developed ANN models efficiently predicted the quality changes of stored dates closely aligned with observed values under the different storage conditions, as evidenced by low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. In addition, the reliability of the developed ANN models was further affirmed by the linear regression between predicted and measured values, which closely follow the 1:1 line, with R2 values ranging from 0.766 to 0.980, the ANN models demonstrate accurate estimating of fruit quality attributes. The study's findings contribute to food quality and supply chain management through the identification of optimal storage conditions and predicting the fruit quality during storage under different atmosphere conditions, thereby minimizing food waste and enhancing food safety.

Special Issue on the Latest Research on Flavor Components and Sensory Properties of Food during Processing and Storage

Due to their chemical composition and physico-chemical properties, most food products are susceptible to biochemical, microbiological, physical and chemical deterioration [...].

TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits

Fresh dates have a limited shelf life and are susceptible to spoilage, which can lead to economic losses for producers and suppliers. The problem of accurate shelf life estimation for fresh dates is essential for various stakeholders involved in the production, supply, and consumption of dates. Modified atmosphere packaging (MAP) is one of the essential methods that improves the quality and increases the shelf life of fresh dates by reducing the rate of ripening. Therefore, this study aims to apply fast and cost-effective non-destructive techniques based on machine learning (ML) to predict and estimate the shelf life of stored fresh date fruits under different conditions. Predicting and estimating the shelf life of stored date fruits is essential for scheduling them for consumption at the right time in the supply chain to benefit from the nutritional advantages of fresh dates. The study observed the physicochemical attributes of fresh date fruits, including moisture content, total soluble solids, sugar content, tannin content, pH, and firmness, during storage in a vacuum and MAP at 5 and 24 ∘C every 7 days to determine the shelf life using a non-destructive approach. TinyML-compatible regression models were employed to predict the stages of fruit development during the storage period. The decrease in the shelf life of the fruits begins when they transition from the Khalal stage to the Rutab stage, and the shelf life ends when they start to spoil or ripen to the Tamr stage. Low-cost Visible-Near-Infrared (VisNIR) spectral sensors (AS7265x-multi-spectral) were used to capture the internal physicochemical attributes of the fresh fruit. Regression models were employed for shelf life estimation. The findings indicated that vacuum and modified atmosphere packaging with 20% CO2 and N balance efficiently increased the shelf life of the stored fresh fruit to 53 days and 44 days, respectively, when maintained at 5 ∘C. However, the shelf life decreased to 44 and 23 days when the vacuum and modified atmosphere packaging with 20% CO2 and N balance were maintained at room temperature (24 ∘C). Edge Impulse supports the training and deployment of models on low-cost microcontrollers, which can be used to predict real-time estimations of the shelf life of fresh dates using TinyML sensors.

Sustainable Nonfarm Approaches to Achieve Zero Hunger and Its Unveiled Reality

Millions of people worldwide are deprived of sufficient, safe, and nutritious food required for an everyday and healthy life. The hunger crisis is worsening over time, even though many attempts have been made to minimize it. Increasing world population and competition for natural resources, climate change, natural disasters, urbanization, poverty, and illiteracy are the main causes that need to be addressed to reduce the hunger crisis. Various nonfarm technologies are being used to eradicate hunger but their long-term impact on the environment should also be considered. The real sustainability of several novel technologies being implemented to deal with hunger is an issue to tackle. This paper discusses the potential applications of storage facilities, underutilized crops, waste valorization, food preservation, nutritionally enriched novel food products, and technological advancement in food processing to achieve zero hunger. An attempt has also been made to address the sustainability of various nonfarm technology utilized to minimize the global hunger crisis.