IoT Intelligent Farming System
Intelligent Farming System - Introduction
Australian agriculture and animal husbandry industry are highly developed today; farms can range from small pastures and fields to several thousand hectares of large areas. Various motorized machines cultivate the land and care for the cattle. In recent years, the number of mechanical equipment used on farms has grown across the board. Consequently, a new problem has arisen: how can a farmer effectively manage all their machinery and mechanical equipment, which is primarily offline, track which fields contain which vegetation, and create a plan for cultivating the land? Furthermore, farm owners also employed farming contractors each year for a multitude of jobs ranging from sowing to harvesting, so it was arduous and complicated to remain on top of all tasks and pieces of relevant information.
- Monitor and report weather conditions
- Be very responsive to all immediate systemic changes
- Connect all the data points and extract data in real-time for reporting and benchmarking purposes
- Triangulate and track each machinery and mechanical equipment used in the cultivation processes in real-time, thereby increasing overall safety on the farm
Our client already created a hardware prototype based on the Internet of Things approach and would address the requirements and solve the problem farmers encountered. They wanted to extend their software development capacity and were looking for a long-term software development partnership. They found that Shinetech experts’ experience in mobile app development, custom website development, extensive IoT experience, as well as system interconnectedness suited them perfectly, so we started a long-term collaboration.
The Solution – bring IoT-based intelligent farming system into the industry
- Developing a fully functional website used by the client and their customers
- Developing a custom web app with a detailed user interface
- Fully integrating the client’s custom-built hardware into the intelligent farming system
Shinetech, being the remote development solutions provider, assembled a four-person team who undertook all the software development and hardware integration tasks. The team then established communication channels, arranged multiple onsite visits, and created a development plan.
Through our unique approach to how we work with the clients and develop the software, Shinetech managed to send the product to the client in just three months.
• Strengthen Communication by Transparent Progress
How did we complete all of the client’s requirements in three months and develop a working solution? The overall development process was entirely transparent for the client, and they knew exactly where the software stood during its development phases. Through the use of daily email updates, instant messaging platforms, and project management tools, the customer understood the development progress directly and, based on it, provided adequate feedback on time. Our team was very keen on receiving the said feedback as it helped tremendously improve the submitted functions and quickly solve the issues our team encountered. It also provided the appropriate support for coordinating the resources.
We also established weekly video conferences to further strengthen the customer and the Shinetech team’s contact. Not only did constant and consistent communication build trust and provide support for both sides, but it was also the key to delivering great working software at a steady pace. Our client relied on receiving updates regularly as it helped them with their daily operations.
• Continuous integration by Two-week Iteration Process
We needed to ensure that the intelligent farming system consistently met the client’s needs. To achieve this goal, we adopted the ‘continuous integration, two weeks iteration’ practice so that we could enable the client to use, evaluate, and voice their thoughts in all software development stages. That’s established the development phase that consisted of several bi-weekly sprints. After completing each development sprint, we received extensive feedback and then promptly implemented it into our software. The received feedback, in turn, allowed our team to find and solve problems effectively, thus achieving higher development efficiency and mitigating the potential risks.
Finally, in order to understand the client’s users better, our experts visited the farms on multiple occasions. It was essential for us to comprehend how well the system Shinetech built performed in real-world circumstances, so our team members spent four weeks onsite on their first visit. That is why our experts accompanied machine operators in the cabins and experienced first-hand how the hardware was used. While being in the cabin with the drivers, they thoroughly tested the system, discussed all the details with the end-users, and examined how the system and all its parts performed under poor network connectivity. We then established frequent visits to retest the system and ensure all the components worked as intended.
The Results - a fully functional intelligent farming system
For the duration of this project’s development phase, the Shinetech team and the client encountered numerous barriers and difficulties. Building such an extensive IoT farming intelligent system meant that every small component needed to perform its function and synchronize well with the other parts of the system. Furthermore, integrating the hardware itself into the IoT system brought its own set of issues we needed to resolve. And lastly, the system required security and stability since IoT systems generally had a bad reputation of lacking security measurements. Regardless, our team successfully overcame these difficulties by applying our professional knowledge, problem-solving skills, and diligent and proactive attitude. Some examples where this collaboration came to its full effect were:
- Data extraction and processing: Connecting various devices to be used as one coherent system
- Amazon Web Services: Employing AWS was the right choice in terms of security, versatility, and ease of use
- Google Maps: the Shinetech team successfully implemented the required functions and complex operations such as marking, drawing and presenting data from Google Maps due to each team member’s sufficient experience and strong technical capabilities
- Live GPS data capturing: Accurately capturing large volumes of GPS data is always challenging. After rigorous testing and numerous algorithm revisions, the system was finally able to meet the end customers’ demands and requirements
The software Shinetech built supports more than 20 farms today; the system connects over 150 cultivation machines and has processed more than 700 Gb of data. The project is still active, and our experts are working on expanding it further, so we expect these numbers to increase in the near future.
Lance Nuttall CEO, NuPoint
The team behind the intelligent farming system
We at Shinetech understand how essential it is for the client to receive working software at regular intervals, incorporate the client’s feedback into the development processes, and have a consistent development pace. The team that worked with NuPoint did their best to answer all questions, demands, and expectations the client had in order to develop great software for them and help them with their business goals. They are the true Shinetech champions.
Coco was deeply integrated with both the team and the client for this project. Because of this, she developed a deep understanding of the client's business and helped implement new ideas while delivering software of the highest quality. Coco's ability has grown with Shinetech; she continuously learns new skills and is always eager to help others.
Django has been an essential member of the NuPoint team for over four years; he always designs excellent architecture and helps with team mobilization. The client has given him a perfect rating and has used Django to help improve other areas of their business. Django’s knowledge of several programming languages is impeccable, some of which are PHP, NodeJS, VUE, and Python.