Definition – Machine learning is when a computer uses algorithms and statistical models to learn how to perform a specific task without using direct instructions relying on patterns and deduction in stead. A set of training data is provided and the software develops its own algorithm, rather than a programmer that provides every step of the algorithm.
Machine learning is a subset of Artificial Intelligence (AI).
Well known techniques used in machine learning includes:
- Supervised machine learning :- The computer is provided with example inputs and the required outputs. The goal is for the computer to work out the algorithm
- Unsupervised machine learning :- Only inputs are given and the computer has to find its own patterns in the data.
- Re-enforced machine learning :- A computer program cooperates with an environment in which it must perform a certain task (such as driving a vehicle or playing a game against an opponent). As it solves its problem, the program is provided feedback that is comparable to rewards, which it tries to extend.
Examples of thhe application of machine learning in agriculture would be the following:
- A rover or drone takes pictures of fruit on trees in an orchard and provides a crop estimate based on the pictures taken. This will use supervised learning as the software will be taught how to identify fruit and measure them based and programmed parameters.
- Pictures of fly traps identify the types of flies and the instances, warning the farmer of pest control issues. Also supervised learning, as the software will be taught how to identify insects based and programmed parameters.
- Animal welfare classifiers can connect the chewing signals of livestock to a need to change the diet of the animal. By their movement patterns, including standing, moving, feeding, and drinking, they can tell the amount of stress the animal is exposed to and predict its susceptibility to diseases, weight gain and production.
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