Computer vision, deep-learning neural networks supports study of greenhouse mushroom cultivation

Worldwide, the mushroom industry is a booming business, valued at more than $50 billion in 2019, with most production originating in China and the United States. To create a more favorable environment for mushroom cultivation, farmers are moving their outdoor farming operations into large greenhouses, shifting environmental factors from external climates to internal microclimates. A major driver has been global warming, which has made outdoor farming less productive.

Controlling a greenhouse climate for optimal mushroom growth presents a number of challenges for farmers. A successful harvest requires frequent temperature changes, for instance. The greenhouse temperature must be reduced from +22°C to +16°C to stimulate fruiting. Moreover, relative humidity has to be maintained between 85% and 90%, and carbon dioxide at each of the six growth stages must be adjusted to appropriate levels.

For the most part, farmers must draw on their personal experience and visual observations to estimate the relationship between mushroom growth and the greenhouse microclimate and accordingly determine temperature, humidity, and other environmental factors. Greenhouses are equipped with environmental monitoring systems to obtain microclimate data. However, there is no sensor that can directly measure the growth of mushrooms to determine when adjustments should be made.


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