Abstract

The growing population on planet Earth and the deteriorating environment are leading humanity to a swift depletion of resources. And if it is possible to reduce the use of some, it is impossible to eliminate, or even decrease the consumption of protein. Thus, an alternative solution needs to be found. For the past several decades scholars have suggested to breeding crickets as an alternative source of protein. Numerous studies have been made, which resulted in a simple description of the process and a manual of how to establish a breeding cricket farm. However, the fluctuations in breeding conditions stemming from the lack of automation in this sphere, are a hazard to the safe growth and development of the cricket breeding stock. This paper focuses on the developed prototype of a video monitoring equipment developed using machine learning technologies aiming to help identifying hazardous conditions based on the training received in the process of the experiment and numerous tests. The prototype has shown a 70% accuracy rate, yet is capable of determining when the crickets are subjected to various stressors, namely water, nutrition, thermal and methane. Via observing the cricket population, the prototype is learning to alert the breeder as to the potential danger, thereby preserving the cricket population, and increasing the chances of a future mass production of protein from crickets.

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