Irrigation is a highly important practice in agriculture. The productivity of irrigated areas is substantially more than the un-irrigated land. However, most farmers rely on empirical methods in order to decide when and how much water to apply to each field.
This leads mostly to over-irrigation which stresses the plants, lowering crop-yields while valuable water resources are lost. Many nations are starting to address this issue, since irrigation makes up for 70% of the overall water consumption worldwide*, by introducing stricter regulations in water usage. Therefore, the problem that arises is how the irrigation process can be optimized.
Automation is the only way forward
Most farmers rely on someone to physically be present on the field at the time of irrigation in order to initiate the process. It is clear that due to labor shortages and rising costs for farm work, farmers are turning to technology to make farms more efficient and automate the crop production cycle. Irrigation is one of the processes that needs to be automated due to the time constrains that it creates. This is why Chloe Iris was created, in order to provide easy and remote access to the irrigation process. This leads to lower labor costs and less time spent in the farm allowing for larger areas to be cultivated.
Nevertheless, this is only part of the equation because automation does not solve the main challenge which is how often and how much should someone irrigate.
Every field has different needs
Each field is a different ecosystem. This is due to soil, weather patterns and crops differentiations. Therefore it is impossible to automate the process with one simplified way. For example what is the correct decision for irrigation when a field is relatively dry but there is a possible rain coming in three days? Or how much should someone irrigate in May when there is a higher chance of rain? These questions are impossible to answer optimally with simple rules. The only way to address them is to study each case distinctly.
Automation with Artificial Intelligence
What AI provides is a unique solution to this problem. Imagine an agronomist who has studied your particular field for hundreds of years and knows exactly its climate, soil and crop. This is exactly what our algorithm is doing. It trains on historical weather conditions, plant characteristics and soil properties in order to find the optimal approach to irrigation for your particular needs. It trains for hundreds of years trying out various policies in order to achieve the lowest consumption without disturbing the plant. At the same time, it uses weather forecasts when taking decisions in order to ensure that future weather is taken into account.
This way our approach can achieve better results than any other product currently in the market.
Gathering actual data
Last but not least, gathering actual data from the field is absolutely essential to ensure that the correct conditions are met. A monitoring device can give feedback to the system that is needed in order to enhance its estimations and avoid making assumptions. For example any product that is based only on weather information and not actual data for its decisions, makes at best a relative estimation of what is actually happening on the field. This approach can lead to large deviations from reality that can cause serious damage to the crop. Chloe Sense resolves this issue with the humidity and EC sensors it provides. With its flexible design Chloe Sense can be configured in different soil depths depending on the crop roots in order to provide adequate information at a reasonable price.
In conclusion, our proposed solution deals with all the pain points of irrigation: automation, data collection and decision making support. The main goal of Chloe is achieving environmental sustainability without disrupting agricultural production using modern technologies in the service of agriculture. Our system achieves this goal while lowering costs and making the process easier for the farmer.