Machine Learning in Agriculture | Venturus

Machine Learning in Agriculture

With the advent of agriculture 4.0, all the technology already used in the most diverse sectors of economic and social activity are coming strongly also to the agricultural field. Among all of these new technologies, the use of Artificial Intelligence / Machine Learning, are showing that they can and are disruptively influencing various aspects of agricultural production. And all this impact is still only at the beginning.


But, what is Machine Learning anyway?

“Machine Learning” is an AI application that allows systems to have the ability to automatically learn and improve analysis through their own experience without being explicitly programmed.  Machine learning focuses on developing computer programs that can access data and use it to improve your experience on its own. They allow software applications to become more efficient in predicting results. The basic premise of machine learning would be to build algorithms that can receive data and to use statistical analysis to predict a result while also updating the output data as new data becomes available.

The learning process begins with data or observations, used as examples, experience, or instructions, in order to discover patterns between the data provided and to make better or better decisions, based on the examples provided. The primary goal would be to allow computers to learn automatically without human intervention or help or adjusted actions.


Difference between Machine Learning and Artificial Intelligence

There is a confusion that Machine Learning and Artificial Intelligence mean the same thing, but in fact, Artificial Intelligence is a broad concept which includes Machine Learning as one of its features.

Artificial intelligence consists of computational mechanisms that rely on human behavior to solve problems. In other words, technology makes the computer “think” like a person to perform tasks. We humans are able to analyze data, find patterns or trends in them, make more informed analysis from there, and then use conclusions to make decisions. In a way, Artificial Intelligence follows this same principle.

Machine Learning is part of this concept, where, in a given situation, one tries to determine patterns and trends between the data to supply tools for decision making.


Como funciona o Learning

Machine Learning algorithms are usually categorized as “supervised” or “unsupervised“.

Supervised algorithms require a data scientist or data analyst with ML ability to provide both input values and expected output values, in addition to recorded accuracy prediction values during algorithm training. The data scientist determines which variables, or skills, the model must analyze and use to develop the predictions. Once the training is complete, the algorithm will apply what has been learned according to the new data set.

Unsupervised algorithms do not require training with the expected output values.  Instead, they use an interactive technique called “deep learning” to review the data and draw the appropriate conclusions. Unsupervised learning algorithms (neural networks) are used for more complex processing tasks than supervised learning tasks such as image recognition, speech recognition, and natural language generation. These neural networks work by combining millions of examples of training data and often identify correlations between many variables. Once properly trained, the algorithm uses its membership database to interpret new data. These unsupervised algorithms have only become feasible with the emergence of “big data” since they require massive amounts of training data.


Machine Learning implementation process

  • Identify relevant data set and prepare them for analysis;
  • Choose / Identify the type of ML algorithm to be used;
  • Build an analytical model based on the chosen algorithm;
  • Train the model in set of test data and review them as needed;
  • Run the template to generate results and other details.


That is, the use of machine learning is not an impossible factor to be used, it is enough to find out and act on the patterns that one wishes to identify. In agriculture, one can imagine the most different factors to be identified where machine learning would bring great gains in time, money, and accuracy of the analyzes to be performed.


Examples of Using Machine Learning and the BullGreen Case

Several are the cases of use of Machine Learning already widely used in companies. Some of these examples are already classic in certain areas.

Among the several examples we have:

  • – Smart Navigation: ex. Google Maps and Waze, provide more efficient navigation through Machine Learning algorithms;
  • – Recommendation of products for customers: who on the Web hasn’t seen suggestions of options close or related to the products that they wanted to look for?;
  • – Data crossing in the detection of health problems, among several other examples.

Venturus already has some examples of successful projects in which the ML concept is used.

One of the first projects to use ML refers to the DVAP (Water Leakage Detection) project. This is a project already completed and in operation, where the concepts of machine learning in the detection of water leakage were used, through the training of the patterns of the sounds emitted and detected in the pipe.


Venturus’s latest project in the area refers to Startup BullGreen. 

Using smartphone photos, the software crosses data with satellite information in order to obtain state metrics and pasture availability. The system is able to collect information such as forage height, level of weed infestation, forage quality and number of animals. The software was trained to classify the quality of pastures of the farm through training and classification of pasture status according to this information. In the case of this technique, supervised algorithms are used. That is, a score system is created of the pasture states for each farm picket. This project is already underway and in the process of adding further improvements in the evolution of the life cycle, but already acting in the field. More details here.

The use of Machine Learning techniques is already available and with several success stories in the most varied segments of the economy.  In agriculture, where recognition of sounds, images, patterns can be used in the most different situations, simply by detecting which are the areas of interest and in which the gain of information that this brings.

In the field, due to the multipolarity of actions to be carried out, the Machine Learning technique can bring many gains in the most diverse situations, such as: detecting leaks in irrigation, identifying pests and diseases in the crop, using success histories and problems detailed by region of the farm, among many other points. That is, agriculture presents many opportunities for development in the field and machine learning is one of the tools that adds great value in the farmer’s decision making.

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