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The United Nations (UN) adopted Sustainable Development Goals (SDGs); agenda 2030 focuses on Climate Action (goal 13), targeting climate adaptability, as well as resilience, awareness and improving policy mechanisms on climate change. In order to enhance climate adaptability, climate-smart agricultural practices (CSAP) is a necessary step. CSAP is a sustainable agriculture approach with a strong focus on climate dimensions. The three pillars of climate-smart agriculture (CSA) are ‘Adaptation’: adapting to climate change; ‘Resilience’: building resilience against it and ‘Remove’: reducing carbon emissions. The new world economy uses Industry 4.0 technologies for sustainable advancement, including blockchain technology, big data analytics, artificial intelligence (AI), augmented and virtual reality, industrial Internet of Things (IoT) and services. Hence, technology plays a significant role in climate sustainable agriculture practices. This chapter shall consider three technologies consisting of IoT, AI and blockchain technology which contribute to CSAP in pre-harvesting (monitoring climate as well as fertility status, soil testing, etc.), harvesting (tilling, fertilisation, seed operations, etc.) and post-harvesting (predicting weather factors, seed varieties, etc.) periods of agriculture. All these three technologies work like the human nervous system; IoT helps in converting various information regarding demography, climate change, local agricultural needs, etc. into world data; AI works like a brain in combination with IoT, helps predict the use of climate-smart technology and blockchain, the memory part of the nervous system which deals with supply-side and ensures traceability as well as transparency for consumers as well as farmers. Hence, this chapter shall contribute to the importance of these three technologies in adopting CSAP in three stages of agriculture.

The Internet of Things (IoT) is becoming increasingly popular in agribusiness to help increase food production capacity for the ever-expanding global population. This chapter provides a holistic overview of the latest trends around the applications of IoT in agriculture. We begin by giving an overview of IoT and its capabilities, followed by a deep dive into the practical and realistic aspects of leveraging IoT into the agroecosystem. IoT is already being used for many intelligent agriculture applications, such as open-field agriculture, controlled environment agriculture (greenhouse), livestock breeding, agricultural machinery, and more. This chapter examines those applications and ventures beyond the farm into several other aspects of the ecosystem, including storage, warehouse ambiance control, agri-data analytics and decision control, logistics, environmental safety, etc. The contents of the chapter would be based on extensive studies and empirical analysis of the latest research papers on this subject from around the globe, accurately interpreted and transformed by the authors in light of their academic background and professional experience in the digital transformation arena.

Supply of food from the early stages of societies was one of the most important needs of humans. Sustainable supply of food is a demand in modern societies. The agri-food market grows as the population rises every year. The increase in the demand side of the market is more than the growth of the supply side. The rate of using technology in the supply side is increasing rapidly. By using technology in some parts, the efficiency of production improved and caused more production while resources are the same. Availability of resources in different areas causes different ways of production and nurturing innovative technologies to maintain food security. Water, soil, climate change, and growth of population are drivers of using technology in food security. To depict the role of different technologies in the food industry, the authors have reviewed the role of the most important technologies in this field. Knowing the trends of changes in the industry will help to focus on the most important questions and solutions. Having a share in the global food market requires the major use of technology in production processes. In this chapter, the authors will review the most important trends of technology absorption in the food industry.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

The Internet of Things (IoT) is a cloud system that saves energy by being involved in the decision-making process of machines. By this means, machines create an environment of direct interaction without the need for explicit instructions. In this chapter, answers have been sought to the questions of what kind of research has been done on IoT-based technological devices, and how IoT-based technologies are effective in sustainable food production. The systematic literature review examined the scientific research on IoT and Food in the range of 2010–2022 in Scopus. The general framework of the research has been carried out in the context of 6Ws (who, when, where, what, why, and how), which is one of the systematic literature reviews. The results obtained have been analyzed and interpreted in the MAXQDA 2020 qualitative data analysis program. In the findings obtained, it has been determined that IoT and food have gained importance worldwide, especially in England, India, and China. Furthermore, it has been determined that most of the studies on IoT are based on case studies, and all the articles examined are collected in three main focus points. Subcodes have been created under the main codes ‘Food Supply Chain, Smart and Sustainable Agriculture ve Waste Management’, and problem points have been tried to be customized.

In this chapter, drone and vision camera technology have been combined for monitoring the crop product quality. Three vegetable crops such as tomato, cauliflower, and eggplant are considered for quality monitoring; hence, image datasets are collected for those vegetables only. The proposed method classified the vegetables into two classes as rotten and nonrotten products so the images were collected for rotten and nonrotten products. Three different features information such as chromatic features, contour features, and texture features have been extracted from the dataset and further used to train a Gaussian kernel support vector machine algorithm for identifying the product quality. The system utilized multiple features such as chromatic, contour, and texture features in classifier training which enhances the accuracy and robustness of the system. Chromatic features were utilized for detecting the crop while other features such as contour and texture features were utilized for further classifier building to identify the crop product quality. The performance of the system is evaluated based on the true positive rate, false discovery rate, positive predictive value, and accuracy. The proposed system identified good and bad products with a 97.9% of true positive rate, 2.43 % of false discovery rate, 97.73% positive predictive value, and 95.4% of accuracy. The achieved results concluded that the results are lucrative and the proposed system is efficient in agriculture product quality monitoring.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

The objective of this chapter is to identify the key characteristics of Global Services businesses that will thrive and achieve success in the future. These factors are integrated into three main pillars, which we refer to as the Triple-Win. The first and most obvious pillar is technology as a tool. The second pillar is the design and sustainability of the business model, without which the previous factor would be merely a cost and not an investment. And last but not the least, there is the purpose which gives meaning to the proposal, focusing on the human being and their environment. The DIDPAGA business model sits at the intersection of these three elements.

Food fraud as intentional deception for economic gain relies on a low-tech food value chain, that applies a ‘paper-and-pencil approach’, unable to provide reliable and trusted data about food products, accompanied processes/activities and actors involved. Such approach has created the information asymmetry that leads to erosion of stakeholders and consumers trust, which in turn discourages cooperation within the food chain by damaging its ability to decrease uncertainty and capability to provide authentic, nutritional, accessible and affordable food for all. Lack of holistic approach, focus on stand-alone measures, lack of proactive measures and undermined role of customers have been major factors behind weaknesses of current anti-fraud measures system. Thus, the process of strong and fast digitalisation enabled by the new emerging technology called Industry 4.0 is a way to provide a shift from food fraud detection to efficient prevention. Therefore, the objective of this chapter is to shed light on current challenges and opportunities associated with Industry 4.0 technology enablers' guardian role in food fraud prevention with the hope to inform future researchers, experts and decision-makers about opportunities opened up by transforming to new cyber-physical-social ecosystem, or better to say ‘self-thinking’ food value chain whose foundations are already under development. The systematic literature network analysis is applied to fulfil the stated objective. Digitalisation and Industry 4.0 can be used to develop a system that is cost effective and ensures data integrity and prevents tampering and single point failure through offering fault tolerance, immutability, trust, transparency and full traceability of the stored transaction records to all agri-food value chain partners. In addition, such approach lays a foundation for adopting new business models, strengthening food chain resilience, sustainability and innovation capacity.

This chapter presents the Smart Irrigation system using the Internet of Things (IoT). IoT Technology is a network of physical objects that are connected with sensors, software, etc. This chapter concludes the project based on the agriculture field that automates the irrigation process and on the agriculture field that automates the irrigation process and solves the challenge of water consumption in those areas. We have developed the system using different sensors like (1) Soil Moisture sensor, which measures the moisture present in the soil, (2) Humidity and Temperature Sensor (DHT11), which traces the temperature change. All these sensors are connected to the Node MCU ESP8266 microcontroller, which is also a Wi-Fi module. It uploads the data to the cloud and displays it in the form of readings detected by the Blynk Application. This sensor's reading values control the pump for emergency purposes, such as stopping the pump for irrigation. Thus, this project can automate the irrigation process by analyzing soil moisture and climatic conditions, covering essential aspects like less labor, power consumption, reliability, and cost.