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One way to look at agriculture is as a branch of matrix algebra. A farmer must constantly balance a set of variables, such as weather; soil moisture and nutrient levels; weeds, pests, and diseases; as well as the costs of taking measures to address these issues. Information technology is capable of solving the problems of the agricultural sector and streamlining production. In this sense, the role of smart agriculture (or precision agriculture) is to precisely measure the variables that enter the matrix, facilitating the farmer's work, optimizing their production, and maximizing their profit. One of the earliest examples of precision in agriculture occurred in 2001, from a decision made by the world's largest manufacturer of agricultural equipment, John Deere, which equipped its tractors and other mobile machinery with GPS. For farmers, this technology eliminated a frequent problem: tractors would no longer cover the same patch of land more than once. It is estimated that, in some cases, fuel costs were reduced by 40%. READ ALSO: • What is Computer Vision
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Information technology applied in agriculture
Since then, new technologies have been developed. Among them, high-density sampling, conducted to measure the mineral content and soil porosity, can predict the fertility of various areas of a property. Precise mapping of contour lines helps indicate how water resources move, and sensors implanted in the soil can monitor moisture levels at various depths. Some sensors are also capable of indicating nutrient content and how the soil changes in response to fertilizer application. All of this allows for variable rate seeding, which means the density of cultivated plants can be adapted to local conditions. And more importantly, the density itself can be precisely controlled by the farmer. Furthermore, when harvesting, the speed at which grains are placed in the reservoir can be measured from moment to moment. This information, when combined with data captured by GPS, creates a yield map that shows which patches of land were more or less productive and, therefore, how accurate the predictions of the sampling and sensors were. The results can then be fed into the planting pattern for the following season. Farmers also gather information from flyovers of their crops. Images captured by drones and airplanes are capable of measuring the amount of vegetative cover and distinguishing between crops and weeds.
Multispectral analysis
Using a technique called multispectral analysis, which analyzes how plants absorb or reflect different wavelengths of sunlight, farmers can find out which crops are thriving and which are not. Sensors attached to moving machinery can even make measurements on the run. For example, multispectral sensors installed on a tractor's spray nozzles can estimate the required amounts of nitrogen to be sprayed onto crops, adjusting the dose according to the measurement. Modern agriculture then produces a large amount of data, but it needs interpretation, and for this, information technology is essential. Thanks to the proliferation of farm management software, it is possible to provide more and more data made available by sensors, which are also becoming better and more accessible. Things are changing in the air, too. Drone manufacturers have been testing a wide range of models to find out which one is best suited to fly over farms equipped with multispectral cameras. Technological development in the fields of mapping and data processing has then enabled precise control of inputs and results by farmers. This movement leads us to believe that information technology is the present and the future of agricultural production. Finally, Pix Force is a company specialized in computer vision and digital image processing. We can bring our technology to your farm and add value to your product. Get in touch with us!
Fabio Caraça
Fábio Caraça is the Chief Growth Officer at Pix Force. He leads Pix Force's transformation into a scalable SaaS operation, combining strategic vision, culture, and high-impact execution.
