Throughout the history of the human race, food production has been a vital condition for the development of human society. Ancient agricultural civilizations in Egypt and Mesopotamia came up with the concept of irrigation, redirecting floodwaters of the Nile, Tigris, and Euphrates into arable areas through an elaborate network of canals. Similar irrigation methods were also known in ancient China, Persia, and India. The purpose of these methods was to efficiently use irrigation water regulating the timing, duration, and frequency of supply to the fields, which was achieved by designing a system of gates, valves, and pumps.
Times have changed and so have the irrigation methods used. The general goal is to find a compromise between yield and irrigation water consumption. To achieve this, much like earlier in the human history, the contemporary methods aim to control the timing and quantity of water supplied to plants to maintain the water content of the soil and achieve specific water use efficiency affecting the yield. However, the new times have also brought new challenges, such as climate change and requirements of better accuracy and predictability. This entails analyzing the behaviour of the soil, beating the drought stress, finding out which seeds are grown and where (sometimes, without having to go to that particular area), analyzing the recent history identifying trends of the land use, planning the targeted use of pesticides, and much more. The intelligent tools agricultural producers need to support their decision-making have to be capable of doing all this.
Too much to ask? However, this is exactly what WaterFox of heliopas AI is designed to do.