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106 lines
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106 lines
15 KiB
\section{Problem Definition and Identification of Need}
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Farmers are struggling to monitor the condition of their fields in real-time due to the high failure rate of IoT farming projects. Challenges such as network connectivity, sensor malfunctions, and integration issues have not yet been fully overcome. Reports also suggest a 30 percent setback due to the reliability of vendors' hardware and software in IoT projects.
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Farmers primarily want a real-time data collection system that meets their needs and provides an interface where they can interact with the data. However, a 'real-time' data-driven system introduces complexity due to technical debt and the cost of deploying many devices or sensors in the field. Due to software failures and connectivity issues, having equipment deployed and running apps via mobile devices is not always practical in many use cases. Mobile apps that access real-time data and perform assessments require a connection to the cloud and compatibility with current mobile operating systems. Without ignoring this limitation of current system, new approaches have to be introduced to satisfy the need of farmers.
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Recently, many new sensors and network protocols have been introduced that achieve longer ranges, improving the ability to gather data over wider areas. In addition, these new sensors offer improvements in range and power consumption, creating opportunities to build new systems at lower costs with long-term community support. Investing in such a system that supports real-time data gathering and extended range has become more feasible. However, most IoT farming vendors are reluctant to upgrade old systems to support new wireless communication interfaces without vendors releasing a new model, which requires costly new installations. Vendors' profits tend to rely on selling sensors, IoT devices, and software support, making it less feasible for them to develop systems with maximized range. Another way in which vendors sustain operations is through software support platforms that rely on the Internet to provide services to customers. This approach is called "Data-Driven Agriculture," which uses data analytics and digital tools to improve farming techniques. While these platforms maximize the utilization of data gathered from the field, they introduce a new technology stack, increasing complexity and impacting system functionality. One critical aspect of "Data-Driven Agriculture" is the increase in the number of devices and their up-time. However, relying on a large pool of devices forces farmers to rely on multiple vendors and services to meet their needs. Another factor to consider is the life time of these devices is that it tends to be shorter when vendors or companies realize a slow growth in the market. According to the Register journal, Cisco planned to Abaddon support for long range IoT devices by 2026. This will leave their customer having no support after 2026 in both software and hardware. The company also quoted that it is infeasible to stay in this long range technology that can't help the growth of the company due to slow sell of devices. The company also disclosed that long range technology lead to smaller number of gateway deployed compared to wifi which slow down the sells of the devices. In short, even though new long range wireless technology is introduced, for profit companies would not have incentives to develop this technology.
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As mentioned earlier, a system that depends on multiple vendors, with their short lifespans, introduces the risk of having a non-functional or outdated system. According to a report by the National Institute of Standards and Technology (NIST), “many IoT systems are closed systems, each with its own proprietary applications, which makes it difficult to integrate data from multiple providers” \cite{nist2023iot}. This research considers the needs of farmers, including how data is accessed and presented, how often data should be updated, and the level of reliance farmers place on the system. Most stakeholders for this project are homesteaders or farmers who prefer a certain level of self-reliance. If technology is introduced to their environment, they prefer it to offer the highest level of autonomy, which is one of the most important factors driving this project. These farmers are responsible for installing, maintaining, and operating the system. We aim to assemble or acquire a design for hardware requirements and create a basic software version to help farmers quickly start their system and reach the acquisition phase. Additionally, one of the key motivators for farmers to adopt smart IoT technology is to react to environmental changes that may harm or benefit their crops. At the same time, we focus on estimating water resources for farming activities, which in turn determines whether farmers need to invest further in their fields, such as installing a new irrigation system.
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To address farmers' needs, this project aims to provide a system capable of gathering information from multiple sources and offering remote control of certain devices in the field without the external system such as Cloud Technology and the 5G Network. More importantly, the system must be reliable for long-term use and feasible enough for farmers to acquire or build.
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\subsection{Feasibility Analysis}
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\subsubsection{Operating and Deploying Farming Drones}
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With this solution, farmers only need to focus on specific fields and their crops. Purchasing drones can assist with multiple farming activities such as remote imaging, crop watering, and seeding. In short, the drone would use aerial imaging to capture crop health and monitor water usage. When acquiring a farming drone, users are often expected to use the images to identify unhealthy spots, such as areas with water shortages or fertilization issues. This is part of precision farming, which involves using images and mapping to create a farm's topology, crop types, and drainage patterns. A management platform supported by the vendor's software is typically included at no extra charge, along with training via software simulations and an analytics platform. However, maintaining and repairing the drone requires vendor experts, leaving farmers unable to maintain it themselves. The risk of system downtime is high due to crashes or hardware and software malfunctions. According to some drone experts, drones require stable weather conditions to safely take off and land. Additionally, operating a drone may require farmers to obtain permits in some regions. Finally, the initial investment costs are high, which may be prohibitive for many farmers.
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\subsubsection{Control-Based Station}
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This solution connects users to control irrigation systems and notifies them when crops need care. Farmers can verify the situation by logging into a monitoring system and predicting if water is needed in the field. Manual labor is still required to work in parallel with the system. Using time series metrics along with image snapshot to observe changes in the field. Tasks such as installing sensors, updating the system for new sensor types, and self-diagnosis in case of malfunction will be needed. The skills required to operate the system can vary depending on the system's complexity as designed. The result would be a central hub where data is processed, and the system should accept user inputs to send actions to the field.
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\subsubsection{Image Capturing with AI Integration}
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Using AI to monitor field conditions is being developed alongside IoT solutions. Two approaches are available: deploying a robot to scan the programmed field or installing a camera station pole. With the first approach, users need to acquire a robotic product from a company to deploy in the field. Although this solution is meant to reduce manual labor, features such as scanning the soil and crops via computer vision can determine necessary actions and provide live updates for farmers. However, the mechanical parts of the robot depend heavily on the vendor. Most robot parts are custom-made for improved productivity and efficiency, making them expensive and difficult to replace. Skill gaps would pose a significant challenge, as farmers would require specialized training to operate the robots, which involves knowledge from a different field.
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The second approach, the Remote Image Capture System (RICS), uses a camera station where images are captured and stored over time. Data is then sent and transformed to fit into a data pipeline—a process known as "ETL" (Extract, Transform, Load). The issue here is that although data extraction can happen on-site, the transformation and loading for analytics often require cloud services, as they are CPU-intensive and energy-demanding. AI tasks such as computer vision are also typically processed in the cloud rather than on-site. If cloud services are utilized, users should expect monthly operational costs. Worse still, this system cannot achieve bidirectional communication. While this system helps predict field conditions, it lacks an interface for remote environmental control. Research suggests that AI-capable IoT devices are not yet widespread due to the lack of popularity of AI hardware for such devices. Therefore, the term "EDGE AI" could be misleading, as it suggests that AI can be easily deployed in a farming environment. In reality, devices on the farm primarily collect data, which is processed at remote locations where more energy is available and communication latency is lower.
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\par When comparing alternatives, key evaluation criteria could include \textbf{initial acquisition cost, required skills, maintenance, and the ability to modify}. The next section will be using the multi-criteria decision-making method to find a feasible solution of the alternatives.
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\subsection{Multi Criteria Decision making (AHP method).}
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\includepdf[pages=-]{AHP.pdf} % Include all pages of the PDF
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\newpage
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\par In order to begin the analysis, criteria have to be defined. Then, evaluate each alternative based on the selected criteria. Then using a self defined scoring metric to weight each criteria. Considering normalizing the data prioritizing to finalizing data in for score weight in the end.
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\subsection{Operational Requirement}
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The design of the control base system should follow the following requirements to meet the customer's needs.
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\textbf{Mission Definition:} Deliver a system that is capable of providing an observatory and accepting control input from users (farmers, homesteaders).
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\subsection*{Functional Requirements}
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\begin{itemize}
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\item \textbf{Req}: System must provide coverage of 1,000 square feet.
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\item \textbf{Req}: The system should be able to turn on or turn off irrigation pumps.
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\item \textbf{Req}: System must be able to support the following wireless communication protocols: LORA, WiFi 2.4GHz, WiFi 5.0GHz.
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\item \textbf{Req}: Farmers should receive updated data every 3-5 minutes.
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\item \textbf{Req}: Farmers should be able to view weather data daily.
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\item \textbf{Req}: The system must be exposed to UV sunlight.
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\item \textbf{Req}: The system can operate in outdoor environments below 100°F.
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\item \textbf{Req}: The system can withstand 40 mph - 50 mph wind speed.
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\item \textbf{Req}: System must support being connected over-the-air.
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\item Enable remote access protocol.
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\item \textbf{Req}: System should take less than 30 minutes to mount.
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\item \textbf{Req}: System only allows 90 minutes downtime.
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\item \textbf{Req}: System should take 90 minutes to install software for the system.
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\item \textbf{Req}: System must take two hours to deploy.
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\item \textbf{Req}: System must be able to operate without grid electricity.
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\item \textbf{Req}: The system must measure factors such as UV radiation, wind speed, wind direction, rainfall, and soil moisture.
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\item \textbf{Req}: System must support mobile devices.
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\item \textbf{Req}: The system must automatically restart when a malfunction is detected.
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\item \textbf{Req}: The chance of the system going down must be less than 10 percent.
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\end{itemize}
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\subsection*{Economic Factors}
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\begin{itemize}
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\item \textbf{Req}: Annual operation cost should not exceed \$200.
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\item \textbf{Req}: System parts or modules must be available on the market for the next 5 years.
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\item \textbf{Req}: About 80 percent of the parts should be recyclable or reused.
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\item \textbf{Req}: The system should last more than a 5-year lifespan.
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\item \textbf{Req}: The parts can be salvaged with a price of 30 percent of the initial purchasing cost.
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\end{itemize}
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\subsection*{Design Constraints}
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\begin{itemize}
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\item The system’s weight must be below 50 pounds.
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\item \textbf{Req}: It should be built within a \$1000 budget.
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\end{itemize}
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\subsection*{Operational Life Cycle}
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\begin{itemize}
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\item \textbf{Req}: System parts or modules must be available on the market for replacement.
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\item \textbf{Req}: About 80 percent of the parts should be recyclable or reused.
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\end{itemize}
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\subsection*{Non-Functional Requirements}
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\begin{itemize}
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\item Latency for farmers to access the system is 0-30 seconds.
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\item System must provide data redundancy.
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\item Data will be collected for a 3-year period.
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\item Data must be transmitted securely over the air.
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\end{itemize}
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\item Effectiveness Requirement: Because the system potentially uses sensors with WiFi communication, it allows the system to be built and assembled easily, as farmers can purchase these parts at a low price. Additionally, the availability of various models is not a concern, as WiFi-based sensor systems are among the most popular on the market. This off-the-shelf solution helps reduce the cost of specialized weather-related sensors and the pool of devices is large to pick. Since this is an off-grid system, a portable power station should be provided to perform installation and troubleshooting while in the field. This should act as a temporary data source to assist the installation and maintenance process. In addition, the costs of upgrading should only be tied to new parts without modification of the current design. Therefore, it is acceptable to replace other than repair.
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\newpage
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\subsection{House of Quality}
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To translate and communicate with stakeholders (farmers), a House of Quality (HoQ) is recommended to help the design process. This often completed by system analysis and system engineering. To complete this step, a requirement documents have to be a affirmed to conduct the chart. Based on the requirement documents, quantified information will be used to set up standard requirement of the system, and later used as a blueprint to drive the design process. For the assignment stage, this tends to address the customers and stakeholders need more than information shared within the engineer team. Therefore, spending times with stakeholder often prioritized more than system development.
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\begin{figure}[H] % 'H' ensures the figure stays where you place it
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\centering
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\includegraphics[trim=0 0 0 0, clip, width=\textwidth]{QFA-House of Quality.pdf} % Adjust the width (50% of text width here)
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\caption{House of Quality}
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\label{fig:hoq}
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\end{figure}
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As mentions in figure \ref{fig:hoq}, we can view competitor such as Azure IoT Platform, The Things Network, Cisco IoT have offering same set of qualities and functions. Theses qualities have to match with the demand of customers. The demands of customers are deprived from the requirement documents collected earlier.
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