The agriculture and animal husbandry in Australia is highly developed, its farm scale can range from small fields up to even several thousand hectares. High mechanization in the farming brings convenience, but also management difficulties with the large number of mechanical equipment and farm lands. Farmer's use of IOT (Internet of Things) could further upgrade the mechanized planting to intelligent planting, with the help of farm management Informatization.
Our client is a software provider of farming solutions in Australia, whose users are large farmers located in Australia and New Zealand. The client is familiar with the process of farm operations and the problems faced by farmers: farmers outsource farmland-related work such as cultivating, spoiling and harvesting to professional farming contractors each year, and farmers themselves needed to manage cultivated land and track the farming process, they needed a system to help track each tractor, monitor the weather conditions in order to take the best planting program to avoid losses, and to achieve intelligent farm cultivation.
IOT technology is fully used in this project. The first version of the system consisted of three parts - Web, App and Hardware. Shinetech assembled a team of our five developers who undertook all the software and hardware implementation work. The first version went live in three months.
• Continuous integration by Two-week Iteration Process
How did we complete all the customer's requirements in three months and make sure that the submitted software help solve our customer's business problems? It was key to promptly understand whether the software under development was meeting their business needs, we then addressed their feedback to improve our software as soon as possible. In order to achieve this goal, we adopted the 'continuous integration, two weeks iteration' process to enable customer use, to evaluate and to develop the software together. This allowed us to find and solve the problem with the Shinetech team effectively, thus reducing risk and time and achieving higher development efficiency.
• Strengthen Communication by Transparent Progress
The overall development progress was 100% transparent to the customer. Through the use of daily mail feeds, instant messaging tools and project management tools, customers could directly understand the development progress in order to timely provide adequate feedback to the submitted functions, keeping abreast of the difficulties and problems the team encountered, giving the appropriate support of the resources coordination. There were also weekly voice conferences to strengthen the communication between the customer and the team.
In the past two years, Shinetech team and customers encountered numerous barriers and difficulties. We collaborated and overcame the difficulties by applying our professional knowledge, coupled with diligent and proactive attitude from both parties. Some good examples include:
• Google Maps: to achieve the required functions, complex operations such as the need to marking, drawing and calculating based on Google Maps. Shinetech team handled these well based on their sufficient experiences and strong technical capabilities.
• Live GPS data capturing: handling the accuracy and large volume of the live GPS data are always challenging. After rigorous testing and revising of the algorithm, the system managed to meet the demand of the challenging requirements from the end customers.
• Understanding of Farming Operations: customers invited our developers to visit and work on the fields with the drivers directly to see how they actually carry out their work.
The V1 has been put into use by the end of 2015. The farming contractor, after receiving the positioning equipment, is assigned to a corresponding vehicle or tractor. During farming, the farmer can locate the vehicle in real-time via Google Maps, manage driver's task area and track the progress of agricultural operations. The driver can view the tasks and the farm map by a tablet installed in the tractor. At the same time, local weather data collected through a small weather station, such as temperature, wind direction and humidity, are relayed to inform the farmers via the system, helping users to deal with sudden weather conditions.
From early 2016, aside from the optimization work of the first version of the system, we have been working on the development work of the second version to enhance the system functionality. The "second generation farm breeding system" allows the users that have limited or even no network signal to still use the system, to achieve farm intelligent management and crop breeding despite network constraints in the field.