The Future of Agriculture: IoT, AI and Blockchain Technology for Sustainable Farming

This chapter endeavors to develop a robust digital model for farm optimization with the primary objectives of enhancing resource utilization, minimizing waste, and increasing productivity while mitigating environmental impact. The proposed digital twin will leverage data from diverse sources, including sensors, weather data, soil moisture levels, and crop yields. Methodologically, the integration and processing of this varied data will be achieved through advanced algorithms, ensuring a comprehensive and accurate representation of the farm. The simulation aspect of the digital twin will explore different scenarios, allowing for a nuanced understanding of the impact of interventions on farm productivity and sustainability. Specific scenarios, such as testing the effects of varied irrigation strategies on crop yields or optimizing fertilizer inputs, will be explored. Methodological considerations will be discussed, addressing challenges related to data integration, format disparities, and accuracy variations across different data sources. Crucially, collaboration with farmers and stakeholders will be a cornerstone of this research. Their insights and realworld experiences will be actively incorporated throughout the development process, ensuring that the digital twin is tailored to the practical needs and challenges faced in agricultural operations. In tandem with this, the development of user-friendly interfaces will be emphasized, providing farmers and stakeholders with accessible tools for interacting with the digital twin. Specific functionalities, tailored to inform periodic decisions and processes, will be integrated into the interfaces, fostering usability and adoption. The chapter will examine the assessment of environmental impact. A detailed examination of the criteria and indicators used to measure and minimize the farm's environmental footprint will be discussed. By addressing these methodological considerations comprehensively, this research aims to not only optimize resource use and reduce waste but also contribute to the transformative advancement of sustainable and efficient farming practices.

The future of farming and farmers in India is smart farming, which uses intelligence to integrate information technology and communication tools equipped with sensors and actuators for embedded farm management. This involves using emerging technologies like AI, IoT, and blockchain to employ robots, drones, and artificial intelligence in the agricultural sector, which is modifying traditional farming practices and simultaneously posing a variety of difficulties. The aim is to explore the various tools and equipment used. Pesticides are an essential material used in agricultural land to eliminate insects or other harmful organisms that affect crop yields. However, excessive use of pesticides can result in problems such as decreased soil fertility and an increase in insect species' immunity. To overcome these challenges, a land-specific variable-rate spraying and directional spraying method can be employed, which offers an accurate and flexible alternative strategy. Soil moisture is a crucial parameter in agriculture as it affects plant growth and survival. Various factors like air content, salinity, toxic substances, soil structure, temperature, and heat capacity of the ground can affect soil moisture. Agriculture resource management can be enhanced by designing various technologies like AI, IoT, and blockchain by using IoT sensors, drones and satellites, AI-powered cameras, weather stations, crop yield predictions, disease and pest detection, crop optimization, supply chain transparency, resource optimization, online communities, and data sharing networks. AI optimizes resource allocation and predicts outcomes. IoT provides real-time data for precision farming and livestock monitoring, while blockchain ensures transparency and security in supply chains and transactions, revolutionizing agricultural resource management. Agriculture resource management using technologies like AI, IoT, and blockchain comes with ample potential results like increasing efficiency in agricultural operations, enhancing productivity, improving crop and livestock health, and facilitating knowledge sharing and collaboration.

'Wireless Sensor Networks and 'The Internet of Things' are the two imminent commonalities in agricultural science that allow the development of less exclusive systems to install, control, and maintain low-power standard protocols. The work makes use of low-cost sensors and IoT platforms to help farmers improve agricultural systems with better yield and reduce insufficient usage of water. Precision agriculture helps in terms of quality of yield, efficiency of product, decrease in the environmental harness, and minimal usage of natural assets. The proposed precision model obtains raw properties of the given soil and achieves an overall accuracy of 93.33% in predicting the ideal crop that can be cultivated for the given soil sample using the KNN algorithm and develops a continuous crop monitoring system for the expected crop based on the predefined crop properties.

Agriculture is vital to human survival and has a significant impact on the economy of any nation. Crop protection costs millions of dollars per year. Insects and other pests pose a serious threat to the health of a harvest. Excessive use of chemical fertilizers and pesticides negatively affects the crop and soil quality. Therefore, one way to safeguard the harvest and mitigate potential losses is through early identification of the pests. Examining the crop at the right moment is the best technique to determine its overall health. While manual inspection is the standard way of conducting field inspection, it becomes challenging for large fields. In addition, manual inspection would be exceedingly expensive and tedious. To address this, an automated system is needed to detect pests, identify them, and recommend appropriate fertilizers using an IoT system. Therefore, automated pest detection has become a major focus for researchers globally, as it offers a more efficient and cost-effective alternative to manual inspection. In this work, a smart agriculture system has been proposed that monitors crops, identifies pests, and allows remote control. The dataset comprises over 4000 images of corn leaves, categorized into rust, blight, grey spots, and healthy leaves. By employing Convolutional Neural Networks (CNN), the system has achieved a remarkable 99% accuracy in pest detection.

Agriculture is one of the major sources of economy in India. Quality of crops and optimal yield are possible amidst several challenges, such as climate change, water scarcity, and weeds, by means of sustainable agriculture. Modern science and technological advancements can be used to address these challenges and, hence, maximize agricultural productivity.

This chapter focuses on weed control using a combination of computer vision and IoT. Weed is an unwanted crop that affects the growth of the actual crop by absorbing soil nutrients, water, and sunlight. It is extremely important to remove the weeds. Targeted spraying to kill the weed has been the most effective solution. Such a system is costeffective and time-saving for large farms. The major concern in this method is the identification of the weed. There are several types of weeds, and identifying them among the actual crop is critical. False identification may lead to large economic losses. An efficient product for weed removal can be designed by combining the knowledge of IoT, image processing, and artificial intelligence (AI). AI and image processing aid in identifying and classifying the weeds. AI also helps in analyzing the risk of weed and the ineffective usage of weed killer, along with the amount of pesticide to be sprayed based on the type of weed. This chapter discusses sustainable agriculture, works carried out in the field of smart farming, the significance of technologies such as IoT and AI, and the design of a weed killer bot, which mainly uses image processing.

In a world where hunger and malnourishment are still a matter of concern, food loss, wastage, and its timely availability must be addressed immediately. Based on an extensive literature review and secondary data, this manuscript has tried to come up with holistic, contemporary solutions for food supply chain management. Against this backdrop, tools viz., IoT, AI, and blockchain will not only manage food loss, wastage, and timely availability but also revamp transparency, food quality and safety. IoT architecture offers tracking solutions through measurement, network, service, and application layers using various sensors and WiFi-connected devices. Artificial Intelligence (AI) and the Internet of Things are becoming significant facilitators of supply chain management (SCM) optimization. The adoption of AI technology has been associated with a 15% reduction in logistics costs, a 35% reduction in inventory levels, and a 65% increase in customer service levels. It assists in demand forecasting, enhanced safety, cost saving, boosting revenue, and chiefly on-time delivery. Block Chain Technology (BCT) system can be programmed to record and track food supply chain transactions. Chhattisgarh in India has developed the Centralized Online RealTime Electronic Public Distribution System (CORE-PDS) and has become a model state in the ‘Public Distribution System (PDS)’. Unfortunately, Chhattisgarh’s model fell prey to scams viz., bogus ration cards, irregularities in rice stock, and poor quality rice and salt samples collected, which were unsuitable for human consumption because of no mid-term audit and monitoring. The chapter concludes that all these anomalies would not have happened to such an extent or have been addressed timely if the technological trinity viz., IoT, AI, and Blockchain, had been incorporated judiciously into the PDS system. 

Traceability is an important component of food supply chain management because it allows businesses to track food products from their origin to the point of consumption. However, the current food supply chain system frequently lacks proper traceability methods, making it difficult to pinpoint the source of food safety concerns, monitor product quality, and assure regulatory compliance. As a result, there is a growing need to rethink food supply chain management to assure traceability. Artificial intelligence (AI), the Internet of Things (IoT), and blockchain are contributing to the resolution of these issues by increasing accountability, traceability, and efficiency. The application of blockchain, IoT, and AI to food supply chain management is examined in this book chapter. It highlights the possible advantages of incorporating these technologies into the food industry, including improved food safety, real-time food quality monitoring, and increased supply chain visibility. It looks at other uses as well, such as decentralized food traceability systems, smart packaging, and predictive analytics. The chapter also discusses the obstacles and restrictions that come with implementing these technologies in the food supply chain, as well as potential solutions and effectiveness.

Agriculture plays an important part in most countries, such as India. A survey says that 54.6% of the total labor force of India is engaged in agriculture and its connected activities. The government is announcing many schemes to facilitate agriculture and support farmers. But most of the farmers are from poor families and are not able to reach the government schemes when they are really in need. Also, it is required to observe and measure the inter and intra-field variability in crops to enjoy the complete benefits of government schemes. This can be done with the advancements in the field of the Internet of Things. Information related to the impact of natural calamities on the agricultural field, malfunctions in the machinery used for cropping, yielding level, and health status of crops can be measured using the technology of IoT (Internet of Things) and analyzed using AI (Artificial Intelligence). Blockchain plays a critical role in replacing traditional means of data storage and exchanging agricultural data with a more trustworthy, immutable, transparent, and decentralized approach. By keeping all the transactions related to government schemes in blockchain, the possible crimes in the form of false data by the intermediate dealers acting between the farmers and the government can be addressed. This, in turn, allows useful government schemes to reach the farmer in time. We propose to develop a theoretical model using IoT, AI, and blockchain, which can assist the farmers in benefitting from the appropriate schemes announced by the government in time and achieving precise agriculture. 

The Internet of Things (IoT) technology is making a radical transition in the agricultural business, resulting in the creation of precision agriculture and sustainable crop management practices. This study inspects how Internet of Things (IoT) technology is revolutionizing agriculture, with a particular emphasis on sustainable crop management techniques and precision agriculture. The study explores the extent and significance of using sensors, IoT devices, and data analytics for improved crop monitoring and management, empowering farmers to make data-driven choices. Farmers are able to allocate resources more efficiently and produce less waste due to the real-time data collecting on soil moisture, temperature, humidity, and crop health. We go into great detail on the essential elements of IoT-based precision agriculture, such as decision support systems, data collecting, analytics, and sensor technology. The study also looks at the benefits of using IoT in agriculture, highlighting how technology might completely transform farming methods for more sustainability and efficiency. A thorough literature study adds to our understanding of the status of research in Internet of Things applications for sustainable crop management and precision agriculture.

The management of solid waste is a major issue everywhere in the world. It is an undesirable material produced from industrial and business activities andfrom residential areas in a given region, which leads to adverse impacts on agriculture. Decentralized municipal solid waste is one of the causes of the harmful environment in India and all over the world. Decentralized waste management causes significant problems, including hazardous diseases and environmental pollution. To tackle these problems, scientific management of waste disposal is needed. Few methods are used to find the solution to these problems, but they do not give precise and accurate results. Moreover, there is uncertainty in data, so there is an immediate need to establish a reliable way to find a place where solid waste can be disposed of. A scientific, integrated solid waste management system must be immediately designed to reduce the effects on agriculture. Geospatial tools like Remote Sensing and GIS, which can help in appropriate site choice for Municipal Solid Waste (MSW) disposal in an additional scientific manner, might result in economically supported concrete proof. The study provides the suitable places in the city and the best algorithms in the field of Site Suitability Analysis (SSA).