AI and Machine Learning in agriculture

Wouldn’t it be wonderful if farmer get to know how much produce he/she will get at crop maturation and the monetary value of it, wouldn’t it be wonderful if farmers are pre-equipped for pest and disease infestation in advance, wouldn’t it be wonderful if each farmer get the customised advisory according to his field and farm. wouldn’t it be wonderful if all farmers get the world class marketing for their produce..yup, it would be amazing…hypothetically it is all possible with the help of machine learning.

Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment…by using artificial intelligence and machine learning.

The principle of artificial intelligence is one where a machine can perceive its environment, and through a certain capacity of flexible rationality, take action to address a specified goal related to that environment. Machine learning is when this same machine, according to a specified set of protocols, improves in its ability to address problems and goals related to the environment as the statistical nature of the data it receives increases. Put more plainly, as the system receives an increasing amount of similar sets of data that can be categorized into specified protocols, its ability to rationalize increases, allowing it to better “predict” on a range of outcomes.

What Makes Agriculture Different?

Agriculture is one of the most difficult fields to contain for the purpose of statistical quantification.

Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and disease may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.

By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.

What may occur with the same seed and fertilizer program in the south Indian region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variance would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.

So the problem with deploying machine learning and artificial intelligence in agriculture is not that scientists lack the capacity to develop programs and protocols to begin to address the biggest of growers’ concerns; the problem is that in most cases, no two environments will be exactly alike, which makes the testing, validation and successful rollout of such technologies much more laborious than in most other industries.

In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact in the field, more effort, skills, and funding is needed to test these technologies in farmers’ fields.

There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.

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