How Artificial Intelligence Can Improve a Cannabis Harvest


Artificial intelligence (AI) and machine learning are making great strides in enabling richer marijuana yields for growers and dispensaries. One of the primary ways that artificial intelligence bolsters yields is through monitoring the lighting conditions and light spectrum in grow rooms and responding accordingly to adverse conditions.

In both indoor and outdoor growing environments, AI allows growers the chance to fine-tune the ways in which inputs (e.g., carbon dioxide levels) affect outputs (e.g., crop yields). AI is able to monitor environmental conditions and other inputs in real-time in order to increase the level of multiple outputs, like a crop's energy consumption or the cost of labor.

High-definition cameras, machine vision, and better sensors are enabling this level of heightened control over cannabis harvests. AI uses data gleaned from these sources to automate, economize, and streamline various processes involved in cannabis cultivation. Growers adopting artificial intelligence into their business operations can expect a 50 percent reduction in errors related to harvest forecasts, which could wind up saving growers tens or hundreds of thousands of dollars annually.

Computers have simply become better at detecting subtle changes in the growing conditions of cannabis in real-time and efficiently crunching all of the data. Manually calculating all of the input data related to cannabis cultivation would be a Herculean challenge for an entire team of agronomists and researchers, and it might not even be possible under more involved conditions.

Machine learning will take all of the data collected from high-definition cameras, perhaps piloted by drone technology, and sensors near cannabis crops and make inferences that no human could ever make. Machine learning as such seeks to discover patterns and make inferences to arrive at a solution in the most efficient manner possible without recourse to a programmer's algorithm per se.

The implications from incorporating machine learning into cannabis cultivation are vast in the sense that growers may be given suggestions about moving their crops or changing lighting conditions that they'd never considered before. Machine learning works in a way similar to exploratory factor analysis in statistics as they both will look for patterns without being explicitly down by a programmer or statistician which patterns to seek out. The results can, therefore, be totally unprecedented and revolutionary depending on the complexity of artificial intelligence at play.

The future of artificial intelligence in improving cannabis harvests is brighter than ever. AI will blend with chemistry and business marketing to, for instance, lead to the discovery of new cannabis strains and personalize product recommendations for customers.

After all, more states loosening legal restrictions on cannabis means that cannabis will sooner rather than later become big business. Governors of particular states will want higher tax revenues from cannabis, and customers will yearn for more pleasant strains of cannabis. Artificial intelligence will make that dream a reality by improving crop yields, product recommendations, and delivery options so that consumers can get the ideal product without having to endlessly search.


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