The agriculture industry needs to adopt the latest technologies to manage and market crops in a more efficient manner. By using IOT and other digital solutions, farmers can increase their yield and profitability by streamlining the process of controlling and marketing their crops. Digital farming aims to provide customers and farmers with a range of solutions to address current issues such as resource management and food security. With a complex set of variables to consider, such as climate change, digital techniques can significantly improve decisionmaking support and efficiency. Digitalization can also help prevent crop hoarding and establish optimal prices for crops throughout the year. By using digital twins, all crop availability can be displayed in one location, making contract farming possible without needing a large number of fields in one area. This approach can encourage corporate investment in agriculture and increase the export of crops between countries. Additionally, the use of digital twins allows us to keep track of all services, agreements, and transactions between farmers and merchants on a transparent platform. Using IOT devices that utilize machine learning algorithms, we can automatically determine relevant prices and check the quality of crops. This will enable farmers to get the best value for their crops while providing consumers with high-quality produce. Overall, digitalization can revolutionize the agriculture industry and help us address current and future challenges.
The use of digital twin technology in combination with IoT systems can significantly improve crop management and sales. Digital twin technology involves creating a virtual replica of a physical object or system, which can be used to simulate and analyze different scenarios and outcomes. In the context of crop management, a digital twin can be used to model a specific farm, taking into account factors such as soil quality, weather conditions, irrigation systems, and crop types.
Real-time data on various aspects of the farm, such as soil moisture levels, temperature, and humidity, can be collected using IoT systems. This data can be used to update the digital twin and make predictions about future crop yields, potential pest outbreaks, and other factors that could affect the success of the farm.
Digital twin technology and IoT systems can also be utilized for crop selling. Data on crop quality, quantity, and market prices can be collected and used by the digital twin to predict the best time to sell the crops for maximum profit. In addition, IoT sensors and digital twins can assist farmers in optimizing their crop management practices by identifying areas for improvement, such as modifying irrigation schedules or utilizing different fertilizers. This can result in increased crop yields and a more efficient and environmentally friendly farming operation. The combination of digital twin technology and IoT EMAIL: (A.3);
2020 Copyright for this paper by its authors. systems has the potential to transform crop management and sales, empowering farmers to make decisions based on data that leads to increased efficiency, profitability, and sustainability.
The paper is divided into 5 sections. Section 1 gives the overview of the digital twin technology in crop management. The remainder of the paper is structured as follows, Section 2 briefly describes the Systematic Literature Review (SLR) and digital twin terminologies, its uses in data analyzing and integration levels. The concepts of contract farming and “one nation- one market” is also briefed in this section. Section 3 looks to conceptualizes the methodology using NoIR IOT based camera used in crop recognizing. Results are discussed in section 5. Finally, the paper concludes in Section 6.
In many countries, the increasing population results in a shortage of agricultural land and food
supply. Reports suggest that hunger-related factors cause the death of 20 million people every year. The
Food and Agriculture Organization (FAO) estimates that there are currently 435 million severely
malnourished people in the world [
Crops are categorized based on several factors such as growth and maturation, root depth, climate,
season, and carbon dioxide absorption [
Many farmers across the country are facing losses as they have to purchase all their inputs, such as
seeds, fertilizer, and other supplies, at retail prices and sell their produce at wholesale costs [
Digitizing the agriculture sector can help farmers who struggle to sell their crops at a profitable price
by providing them with access to technologies. Additionally, small-scale and marginal farmers often
cannot afford high-quality seeds due to their high cost and limited availability in nearby stores.
However, the quality of the seed used in farming is crucial for achieving higher crop yields and
sustained agricultural productivity. Ensuring high-quality seed distribution is just as critical as seed
production [
Farmers in certain regions may not be familiar with the latest technologies available for agriculture. The lack of proper equipment is a major challenge faced by farmers, making it difficult for them to adapt to modern agricultural practices. However, with adequate training, farmers can significantly improve their lives and the productivity of their farms. The use of modern equipment is crucial for this purpose. Through digitization, we can provide farmers with access to new technologies that can greatly benefit them.
Farmers face numerous daily challenges, resulting in their status as price takers rather than price makers. Additionally, the global population is growing exponentially. In this context, ensuring food security for the world's expanding population while ensuring long-term sustainable growth is a crucial goal. To achieve this, we must implement the "One Nation, One Market" principle by leveraging digital twins and cutting-edge technologies such as IoT and other advanced technologies.
By eliminating intermediaries and facilitating direct communication between farmers and retailers through digitalization, agricultural prices can benefit both parties. Various advanced technologies are available in the market that can be utilized to establish a system that enables seamless connection between farmers and retailers.
In many regions, farmers lack literacy and are not familiar with modern technologies, making it
difficult for them to adopt digitalization in agriculture [
A Digital Twin is a digital representation of a physical object or system, which is used to simulate and analyze its behavior in real-world conditions. The following are some common modeling terminologies used in Digital Twin development:
Model: The Digital Twin model represents a physical object or system through a mathematical model that includes data on its design, construction, operation, and maintenance.
Inputs: Inputs for the Digital Twin model include various types of data, such as sensor readings, environmental conditions, and user inputs, used to simulate the behavior of the physical object or system.
Outputs: Outputs generated by the Digital Twin model can provide performance metrics, predictive analytics, visualizations, and other data-driven insights, based on the inputs fed into the model.
Simulation: Simulation involves running the Digital Twin model to predict how the physical object or system will behave under different conditions, allowing potential issues to be identified and performance to be optimized.
Validation: Validation involves verifying that the Digital Twin model accurately represents the physical object or system by comparing the model outputs to real-world data.
Optimization: Optimization is the process of using the Digital Twin model to identify ways to improve the performance of the physical object or system, including changes to design, maintenance, or operational processes.
Machine learning: Machine learning is a type of artificial intelligence that enables the Digital Twin model to learn from data and improve its predictions over time, helping to identify patterns and optimize performance.
Analytics: Analytics involves using data to gain insights into the performance of the physical object or system, which can include descriptive, predictive, and prescriptive analytics.
The use of digital technology can enable farmers to engage in contract farming, which involves an
agreement between farmers and processing and/or marketing firms for the production and supply of
agricultural products at predetermined prices [
There are various types of crops available, and the price of each crop is determined by its quality. However, assessing the quality of crops can be challenging for humans to do accurately with the naked eye. Hence, utilizing machine learning (ML) and Internet of Things (IoT) technology can help verify the quality of crops and establish prices based on their respective quality levels. 2.2.
A digital twin is a virtual representation of a physical system or process that enables real-time monitoring, analysis, and optimization. In the realm of data integration, a digital twin can be employed to create a unified view of data from multiple systems or sources.
To generate a digital twin for data integration, you must first identify the various systems or sources that you want to integrate. These sources may comprise databases, APIs, file systems, and other data repositories. You must then develop a data model that represents the structure and relationships of the data in each of these sources.
Subsequently, you can employ this data model to create a digital twin of the integrated data. This could involve creating a virtual database or data warehouse that consolidates all of the data from different sources, or it could involve generating a real-time data stream that aggregates data from multiple sources as it is made available. The digital twin can be utilized for a range of purposes, such as real-time data monitoring and analysis, forecasting future trends and outcomes, and optimizing processes based on data insights. With the aid of advanced analytics techniques, including machine learning, the digital twin can even be used to automate decision-making processes and enable autonomous operations. 2.3.
b.
Contract farming (CF) involves an agreement made in advance between farmers (producers)
and buyers for the production and distribution of agricultural products, where the terms and
conditions are agreed upon by both parties. These terms often include the price to be paid to
the farmer, the quantity and quality of the product required by the buyer, and the delivery date
of the product to the customer. The contract may also specify details on how production will
be carried out or whether the buyer will supply inputs like seeds, fertilizer, and technical
guidance [
CF has been used for many years, but its use has increased in recent times, especially in developing nations, due to the growing demand for food and agricultural products brought on by globalization. As more people live in cities and seek food that is safe to consume and produced in an environmentally friendly and socially responsible manner, food markets have become more competitive. In this new environment, agricultural product buyers must collaborate more closely with their supply chain partners to obtain high-quality raw materials directly from farmers and satisfy the demand for food products from their clients, including supermarkets, eateries, hotels, schools, and hospitals.
Contracting with farmers can help businesses that process agricultural goods ensure a steady supply of raw materials that match their quality and quantity requirements.
The main challenge associated with contract farming is that small-scale farmers with limited land holdings are often unable to participate because the firms require a minimum quantity of crops from a single location. However, advancements in technology, such as the digital twin technique, have made it possible for individual farmers to engage in contract farming. By creating a virtual community of farmers interested in contracting with the same business, we can connect and facilitate their participation digitally. While implementing this solution is complex and poses real-world challenges, it is feasible with the latest technologies and commitment. It is important to consider that many farmers around the world may be illiterate and lack the necessary infrastructure to use such advanced methods. Therefore, a user-friendly system with simplified processes and a one-click service can be created to make it accessible to everyone. In the digital realm, the combination of technology and human effort can make anything possible. Consequently, it is feasible to quantify crop production by utilizing digital tools, and we can also assess the quality of crops through specialized machine learning algorithms. 2.4.
Digital Twin in “One Nation One Market” Strategy Each country is comprised of multiple states or districts, and each district in turn is composed of several small villages. In some states, the production of certain crops is high, while in others, it is low. Consequently, the demand for specific crops can be higher in some regions than in others. If digital platforms are utilized to gather crop production data during the harvesting season, it would be possible to predict the crop production levels for each state based on the demand. This would allow for the calculation of crop prices for the entire year. If production exceeds demand, exporting crops becomes feasible, and farmers can reap more financial benefits, while consumers would be content with buying crops at a consistent price throughout the year. This approach can prevent crop hoarding in the country, help each state acquire crops at a reasonable price, and encourage private sector investment in agriculture. 2.5.
By utilizing Digital Twins as the primary tool for farm management, physical flows can be separated
from planning and control. This allows farmers to remotely manage operations using (almost)
realtime digital information, eliminating the need for on-site human labor and direct observation [
By automating analytics through the Internet of Things (IoT) and other machine-driven methods,
we can obtain various data from sensors and other sources without the need for human intervention
[
It is important to monitor the crop health and gather data consistently throughout the entire cycle. As more data is accumulated, the digital twin can be refined and crop management can be adapted accordingly.
The above presented methodology represents a transparent system, utilized by both literate and illiterate farmers. The registration is required for both farmers and shopkeepers for gathering information, assistance can easily be provided through nearby cybercafes and govt agencies. After successful registration, farmers can submit their crops' quantity and quality with a single-click based system in this application or server as shown in Figure 5 as discussed below: • A shopkeeper approaches the farmer after a farmer upload crops in order to buy those crops and sends a request to buy crop. • If farmer accepts the request, then portal automatically generates an agreement between the farmer and shopkeeper. • System is able to create automatic agreements, which can act as a proof of transactions done between farmers and shopkeepers. • When farmers are ready to sell their crop and shopkeeper are ready to buy crops then a physical verification is required to check the quality and quantity of crops to finalize the transaction. • Therefore, an agent (Human) goes to farmer house and check the crop quality and quantity and also check the quality of crop by using Al algorithm-based machine. The unit is comprising of a Pi NoIR infrared powered camera which captures the images of the crop and check the quality of crops.
Crops are ready to be sold when everything is in order. If everything is in order, farmers should find a way to transport their goods to storekeepers of storekeeper can also manage the transport and the transport changes can be deducted from either side (farmer or shopkeeper). Here, all farmers upload their crop output so that the system can determine the price of crops for the entire year based on the precise amount of crop output in a given area.
Digitization in agriculture is the process of enhancing and optimizing farming methods through the use of technologies like sensors, drones, and data analytics. The working of crop recognizing device used in our system are shown in figure 6 below.
This IoT-enabled device is designed for quality checks of crops and generates real-time receipts for farmers using AI techniques. In order to ensure the expected result, several major steps such as data collection, implementation, testing, and troubleshooting, need to be conducted. The prototype is built by combining the part of crop recognition and IoT together. This machine firstly scans the crop using the inbuilt camera (), and crop quality analysis is done as per the category. The details of the crops are shown in Table1. The quality and quantity of the crop are monitored by an on-ground agent. The above data is uploaded to the server for further processing taking place on the web portal. The real-time receipt is generated after successful verification of the above crop data and provided to the farmer. The buyer verification is done by an on-ground verification agent after submitting his records and token amount (minimum) for the receipt-generated amount. As a final step, the measured crop should be transported using the most economical transportation medium (suggested by the portal), to the buyer's place.
Digitization provides farmers with access to data and insights that can help them make better decisions and optimize their operations. By using data analytics and precision agriculture technologies, farmers can reduce waste, increase yields, and improve productivity on their farms. This is particularly important in the current economic climate, where profit margins are often slim. By gathering and analyzing data on their crops, farmers can make more informed decisions about when to plant, water, fertilize, and harvest their crops, potentially resulting in higher yields and higher-quality crops. Digitization can also help farmers implement more environmentally friendly farming practices, such as reducing pesticide use and conserving soil and water. This can improve long-term agricultural productivity and environmental sustainability. Ultimately, the digitization of agriculture has the potential to revolutionize how farmers conduct business by providing them with access to data and insights that can guide their decisions, increase productivity, and enhance profitability.