The Use of AI/ML tools for help agronomist to improve their recommendations

I strongly believe that the next unicorns, that is, companies with values greater than US$ 1 Bi dollars will be the agtechs, and among the technologies I believe to be most promising are the use of machine learning (ML) and artificial intelligence (Ai ).

The next revolution in agriculture after the use of fertilizers, chemicals, seeds and biotechnology will come from the use of data. Today, agronomists most often use their instinct or pre-existing knowledge to make decisions and be more assertive in these decisions. 

Among the companies that use this technology and stand out is 6th Grain, a company based in Washington that has established some partnerships in Brazil such as Sinagro, Rural Clima, Adubos Araguaia, among others and around the world companies as Syngenta, UPL and Bill and Melinda Gates foundation.

6th Grain has built a machine learning (ML) and artificial intelligence (AI) team that is focused on transforming satellite remote sensing and weather observations into high quality intelligence for agriculture. 

Crop monitoring is critical to the global food system, since two-thirds of food calories worldwide is derived from four staple crops – wheat, maize, rice, and soybean – it is important that we can anticipate problems with production. Weather conditions, such as deficits in precipitation, increases in temperatures, and changes in soil moisture can affect food production over large areas.

In addition to weather-related stresses, crop losses can be accentuated with biotic losses attributable to pests and diseases.  Declines in yield can translate into very large economic losses, with countries with substantial agriculture sectors such as Brazil, United States and China losing over $40 billion in potential revenue each over the period 1983-2009.  

As the climate changes, threats to crop yields are increasing. Unpredictable, extreme weather characterizes more growing seasons, driving the need to better understand the likely impact of these conditions on production. 

Monitoring productivity across hundreds of fields is essential for ensuring overall profitability of a farming concern, especially in times of low commodity prices and ever-rising costs of labor, inputs, fuel, machinery and rent. Monitoring fields using a digital tool can ensure prompt action when productivity falls, as well as ensuring that decisions made later in the season are appropriate. 6th Grain’s machine learning team uses satellite remote sensing of biomass to develop maps of field variability that provides site-specific information about the permanent factors (mainly climate and soil properties) and management factors (nutrients supply, pest problems or irrigation uniformity in irrigated lands) that affect the productivity. Thus, application rates of planned agronomic inputs (seeding rates, water, and fertilizers) can be adapted to the actual demand and needs of the crop. 6th Grain’s Crop Monitoring tools combine multiple global satellite-derived decision support models into one for improved field-level decision making. 

Monitoring productivity across hundreds of fields is essential for ensuring overall profitability of a farming concern, especially in times of low commodity prices and ever-rising costs of labor, inputs, fuel, machinery and rent. Monitoring fields using a digital tool can ensure prompt action when productivity falls, as well as ensuring that decisions made later in the season are appropriate. 

6th Grain and its partners use satellite remote sensing of biomass to develop maps of field variability that provides site-specific information about the permanent factors (mainly climate and soil properties) and management factors (nutrients supply, pest problems or irrigation uniformity in irrigated lands) that affect the productivity. Thus, application rates of planned agronomic inputs (seeding rates, water, and fertilizers) can be adapted to the actual demand and needs of the crop. 6th Grain’s Crop Monitoring tools combine multiple global satellite-derived decision support models into one for improved field-level decision making. 

Thanks to this technology, 6G can provides for farmers and agronomists correct tool to improve their decisions as apply the correct foliar nutrient or the pest or disease control for example.

If you want to know better these solutions and digitize your field and begin monitoring yield, starting as early as three weeks after planting look for 6th Grain.  www.6grain.com / www.6gbrazil.com.br