INCORPORATING MACHINE LEARNING IN AGRICULTURE

January 27, 2022

Agriculture has an important role to play in global economy. Agricultural system has been increasingly being pressurized with exploding human population. Agro -technology and precision farming, also termed digital agriculture, whose concept and practical training sessions are provided to students of Private Agriculture College in Rajasthan, have been in practice which uses intense data collection techniques to assess agricultural growth along with eliminating negative environmental impact. Machine learning has emerged together with big data technologies to create new opportunities in comprehending about agriculture.

General Context of Machine Learning in Agriculture

 Modern agriculture has to cope with several challenges, including the increasing productivity, global climate changes and natural resources depletion. As a means of addressing the above issues to lessen environmental burden, Modernization of farming has emerged as a subject of study.

Private Agriculture College in Rajasthan educates its students on an essential prerequisite of modern agriculture, which is the adoption of Information and Communication Technology which involves management information systems, use of atmospheric sensors, cameras, drones, low-cost satellites and online services.

The four generic categories in agriculture exploiting machine learning techniques are:

• Yield Prediction– In general, yield prediction is one of the most important and challenging topics in modern agriculture. An accurate model can help farm owners to take informed management decisions on what to grow towards. A requirement of basic understanding of relationship between interactive factors and yield arises which requires help of powerful algorithms such as ML techniques.
• Disease Detection-Crop diseases constitute a major threat in agricultural production systems that deteriorate yield quality. Recent technological advances like ML Technology have made commercially available sensing systems able to identify diseased plants before even the symptoms become visible.
• Crop quality-Crop quality is very important for market. Developing decision support systems can help farmers in taking management decisions for increased quality of production. For example, selective harvesting is a management practice which can be studied through ML algorithms.
• Livestock Management-Livestock production systems have been defined as productivity per animal. The role of precision livestock farming is getting more and more significant. Precision livestock farming depends on non-invasive sensors, such as cameras, drones and temperature sensors. In order to benefit from large amounts of data, ML methodologies have become an integral part of modern livestock farming.