Machine Learning Algorithms in Agriculture
Machine learning algorithms study evaporation processes, soil moisture and temperature to understand ecosystem dynamics and impact on agriculture.
Nowadays, machine learning based applications are used to assess daily, weekly or monthly evapotranspiration, allowing more efficient use of irrigation systems and predicting daily dew point temperature, which helps to identify expected meteorological phenomena.
State-of-the-art machine learning algorithms have incorporated computer vision technologies to provide data for widespread multidimensional analysis of crops, weather and economics.
Apart from that, machine learning plays a very important role in weed detection, which is a serious concern in traditional agricultural production. Weed detection is a very difficult task because it is very difficult to detect and differentiate them from the main crop.
Such challenges can be overcome by applying low-cost ML algorithms without environmental issues. Algorithms such as artificial neural networks, support vector machines, decision trees, random forests, etc., which are used in crop management processes, still at the beginning of their journey, have already evolved towards artificial intelligence systems.
ML algorithms focus on the predictive accuracy of models rather based on modeling data with little or no human intervention, and can provide better decision support.