Agri-Food 4.0

Innovations, Challenges and Strategies

Rahul S. Mor, Dinesh Kumar, Anupama Singh

• Publisher: Emerald Publishing Limited
• Year: 2022
• ISBN:
  9781801175005
• Topic: Business
• Subtopic: Management
• Index: Business

The Agri-Food industry currently faces a range of challenges, from food safety and security, demand, and supply gaps to the maintenance of product quality and issues in product traceability. Smart digital technologies have opened a complete paradigm shift, providing solutions to these challenges, and helping move towards safe, resilient, responsive, and eventually profit-driven supply chains.

The use of these digital technologies by Agri-Food supply chains can bring significant changes in food supply chain management by reducing food wastage, real-time product monitoring, and reducing scalability issues. Agri-Food 4.0: Innovations, Challenges and Strategies addresses new research on digital technologies in the industry, including smart packaging, smart warehousing, effective inventory control, blockchain technology, artificial intelligence, and other Industry 4.0 concepts.

Agri-Food 4.0 explores the challenges and complexities in implementing such technologies, helping policymakers in the agri-food sector with impact assessment and digital transformation towards creating a sustainable industry.

Digital Transformation: Artificial Intelligence Based Product Benefits and Problems of Agritech Industry

C. Ganeshkumar, Arokiaraj David and D. Raja Jebasingh

Abstract

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.

Keywords: Agriculture sector; artificial intelligence; agritech; digital transformation; ecosystem; food loss and wastage; empirical survey

1 Introduction

Today, agriculture faces several grave concerns such as stagnating/declining productivity, precarious livelihoods for millions of small and marginal farmers, regional imbalances in agricultural productivity, a general lack of qualified manpower in the frontier areas to deliver at the grassroots level, rising input costs, changing food habits and quality concerns, fragmented processing industry, high postharvest losses, lack of value addition and processing, emerging climate change, and poor access to credits, etc., are dragging the sector into distress conditions. To address these challenges, Artificial Intelligence (AI)-based agritech ecosystem in India is a key enabler to address these issues, though still at an evolutionary stage, played an important role in many ways working at both consumer end and farmer end, to ensure prosperity in agriculture value chains (Balamurugan et al., 2016; Pratheepkumar, Sharmila, & Arokiaraj, 2017; Ravi, David, & Imaduddin, 2018). Governments at the central level and state level have also responded positively with multiple measures and reforms to ease farmer and other stakeholders of value chain access to AI-based agritech industry products and services. AI-based agritech in the Indian agri-value chain is a complex subject, which is one of the important determinants of success or failure of the food and agriculture sector (Srivastava, Singh, David, & Rai, 2022). Policymakers need to be thoroughly aware of all the essential components of AI-based agritech in the Indian agri-value chain and understand the impact that it might exert on the overall efficiency of the sector and the performance of the organization. This knowledge will enable them to focus on those factors which add value to the sector and organization. Considering the significance of AI-based agritech in the Indian agri-value chain, especially in the Indian context, the researcher has made a sincere attempt to find a solution to the research problem of “What are the benefits and problems of the agritech industry-based products?” “Agriculture is an art and science of cultivating plants and other crops and raising animals for food, other human needs, or economic gain.” With the rise of civilized human society, the crucial advent of agriculture through which the cultivation of domesticated species produced surplus food that encouraged people to live in towns (David, Kumar, & Paul 2022). The agricultural sector is the mainstay of the economy, and its significance can be understood from the following: contribution to GDP, a major source of livelihood, large-scale employment, industrial growth, and foreign trade (Dlodlo & Kalezhi, 2015; Kumar, Rajan, Venkatesan, & Lecinski, 2019; Pandolfi et al., 2009; Sengottuvel & Ganeshkumar, 2018).

Indian Agriculture is currently in the shift from traditional to modern farming. AI in agriculture should play a crucial role in facilitating this transition to modern agriculture. AI comes as a great blessing to the agriculture field, which is largely reliant on the sometimes-unpredictable climatic conditions. Currently, following traditional practices of farming, there is a need for implementing technology in Indian agriculture. Using AI products/services can boost yield quality and efficiency in the management system known as “Precision Agriculture.” The benefits of precision farming are improved productivity in agriculture, decreased chemical use in crop production, less soil degradation, and efficient use of water. In addition, the dissemination of modern farming practices to increase the quality, quantity, and minimize production costs, and precision agriculture is changing the producers' socioeconomic status (Ganeshkumar & Khan, 2021; Mehta & Mungarwal, 2019). AI is essential for a better future in agriculture, and hence there is a need to switch over to smart farming. Smart farming includes software applications, sensing technologies, communication systems, etc. (Abdel-Rahman, Ahmed, & Ismail, 2013). Future farms will be small and automated with the implementation of technology such as survey drones, a fleet of Ag robots, smart tractors, texting cows, farming data, etc. Traditionally, to get increased production, more agricultural inputs like (pesticides, herbicides, and fertilizers) were applied, which consequently have more impact on the environment (Alexander, 2019; Li, Nie, Qiu, & He, 2011; Miranda, Gerardo, & Tanguilig, 2014; Sarita Nayyar, De Cleene, & Dreier, 2018; Tschentscher et al., 2013; Wamba et al., 2017). In this respect, AI with the site-specific application of agricultural inputs can lead to a reduction of agricultural inputs, without affecting agricultural production (Christensen, Suarez, & Utterback, 1998; Lee, Mendelson, Rammohan, & Srivastava, 2017; Porter & Heppelmann, 2015; Puertas & Vázquez, 2019). In his study “Technology in Agribusiness: opportunities to drive value” stated that about 33% of the total, every year food raised for consumption gets wasted. Developing countries are experiencing more wastage at the postharvest and processing level, and developed countries are experiencing more wastage in retail and utilization. Later argued that technology would help reduce waste, enable more significant investment, increase productivity, and meet rising demand (Cortés, Barat, Talens, Blasco, & Lerma-García, 2018; Kassim, Mat, & Harun, 2014; Marchand & Peppard, 2013; Panigrahi & Ting, 2012).

Fig. 1 represents the percentage of primary production loss at various levels of the food value chain for vegetables and fruits in different regions of the world. “Agriculture” indicates losses that occur during harvest operation and subsequent sorting and grading (Ganeshkumar, Prabhu, Reddy, & David, 2020; Karimi, Prasher, Madani, & Kim, 2008; Liqiang, Shouyi, Leibo, Zhen, & Sha...

Table of contents

• Cover
• Series Title
• Title
• Copyright
• Contents
• List of Figures
• List of Tables
• About the Editors
• List of Contributors
• Foreword
• Preface
• Acknowledgments
• Research Trends and Innovation Perspectives about Sustainability and Agri-Food 4.0
• Digital Technology: Implementation Challenges and Strategies in Agri-Food Supply Chain
• Modeling the Challenges for Agri-Food Supply Chain 4.0: TISM Approach
• An Overview of Reducing Food Loss and Food Waste in Supply Chains
• Technological Advancement and Circular Economy Practices in Food Supply Chain
• Blockchain-Based Smart Wheat Supply Chain Model in Indian Context
• Drone-Based Crop Product Quality Monitoring System: An Application of Smart Agriculture
• Overview of IoT in the Agroecosystem
• Smart Irrigation System Using IoT
• Digital Transformation: Artificial Intelligence Based Product Benefits and Problems of Agritech Industry
• Advancement in Packaging Technologies for Agri-Food Sector
• Industry 4.0 Technologies in Agri-Food Supply Chains: Key Performance Indicators
• Robotics and Automation for Agri-Food 4.0: Innovation and Challenges
• Index