It is worth emphasizing here that all the ideas of X, Moonshot Factory arouse great interest, because it is a semi-secret research and development center established by Google in January 2010. In short, everything that happens there is shrouded in mystery, which gives the impression of working on very important projects - and the company does not correct us by ensuring that it works on ideas on a large scale, using technological innovations for this purpose. Its newest child is Mineral, an electric vehicle that is to be used in sustainable agriculture.
It is to use the latest software and hardware applications so that farmers can afford more diversified sowing. Most of them focus on one type of plant, because it is easier to look after the crops, and specialization in one species also allows for faster response to emerging problems. On the other top technology guides if we focus mainly on rice, wheat and corn, they become more susceptible to pests, disease and climate change, and degrade soil quality and the diversity of its microbiome. In short, it has little to do with sustainable farming, so Team X decided to do something about it.
His answer is the so-called computational agriculture, i.e. relying on advanced equipment, software and sensors that will allow farmers to use all 30,000 edible species available on our planet, while selecting plants appropriately for the prevailing climate, which in turn will improve their resistance to certain conditions and reduce the amount of used fertilizers, chemicals and water. The starting point was to gather information about soil, historical crops and weather at various locations, then a prototype of an electric vehicle was developed that monitored how the plants were grown under these different conditions, and eventually sent to California strawberry and soybean fields in Illinois.
Mineral uses GPS to pinpoint the exact location of each plant, and then cameras and sensors collect the necessary information about its condition. This allows the vehicle to inspect plants such as melons, lettuce, oats and barley, offering detailed data on leaf and fruit size, plant height or grain quantity. These are still compiled with weather and soil data and satellite imagery, and machine learning is also involved, which creates certain identification patterns of plant growth under specific conditions, allowing farmers to better predict certain phenomena and, as a result, significantly improve the performance of their fields.