The Importance of Machine Learning in Crop and Soil Monitoring

What is Machine Learning?

Machine learning is a branch of Artificial Intelligence that focuses on the use of data and computer algorithms that can improve over time through experience by imitating the way humans learn. These algorithms build a model based on sample data, known as training data, to make predictions or decisions without being explicitly programmed to do so.

Machine learning algorithms are used in a wide variety of applications, such as Computer Vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. 

How Can Artificial Intelligence Improve Soil and Crop Quality?

Soil is considered a valuable resource since it is the cornerstone of global agriculture and food production. Unfortunately, due to the difficulty of deploying workers on the fields due to harsh weather conditions and the inaccessibility of some places, evaluating soil quality might prove difficult. Furthermore, soil quality is degrading due to increased deforestation. 

According to a recent report by the United Nations, about one-third of the lands suitable for farming have vanished. It was also reported that if the current rate of loss continues, the world’s topsoil could become infertile within 60 years. This paints a grim picture of the future of agriculture.

However, Artificial Intelligence applications can aid in protecting and conserving soil and crop quality by allowing for the faster and safer processing of massive volumes of data collected during physical soil sampling and also through remote imaging.

Machine Learning for Crop and Soil Monitoring

Machine learning applications use various methods to support data analysis and generate sufficient elements to provide a statistical solution.

Farmers can submit soil pictures to an AI algorithm to receive recommendations that will help improve the soil quality and quantity.

There are also sensors that are equipped with the tech to identify nutrient deficiencies in soil and provide farmers with ideas on utilizing fertilizers to increase harvest quality.

The use of drones is another method that takes advantage of machine learning algorithms to analyze crop health. The drone captures aerial image data from fields and transfers the image to a computer. The computer analyzes the captured images using machine learning algorithms and provides a detailed report containing the current health of the crop. Drones have remarkably accelerated the growth of aerial crop monitoring. 

Artificial Intelligence in agriculture not only assists farmers to automate their farming operations but also accelerates the change to precision cultivation to enhance crop production and quality while using less resources.

As technological advancements continue to progress, so will the possibilities of AI in agriculture. The overall effect of this will be increased profits through cost reduction and improved productivity.


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