Color-based Detection and Classification of the Quality of Lactuca Sativa L. on Vertical Indoor Farming Using Artificial Neural Network for Image Processing

Abstract

With the increase in urbanization, environmental pollution has increased and has taken a toll on the quality of Lactuca Sactiva L. or lettuce produced in the country. Vertical indoor farming (VIF) is one of the viable solutions in this problem. However, the crops indoor should have enough nutrients and proper lighting as they would receive outdoors to achieve optimal growth. This research aims to develop an algorithm that would detect and classify the quality of Lactuca Sactiva L. in a vertical indoor farm based on its color property. It was done by first collecting the images of the crop every 30 min the whole planting cycle of the lettuce in the VIF. The images that were gathered were classified manually into two categories: healthy and unhealthy and was eventually used as a training data sets. The second phase was the application and validation of the generated model in the VIF for another planting cycle which got an accuracy of 88 %. The final stage was to implement the validated model in another planting cycle which resulted to an accuracy of 96 %. The overall system was then implemented in the three-layer VIF as the trigger to the LED lights. When the system sees “healthy” -the LED is turned off, when it sees “unhealthy” -the LED is turned on and is set to the minimum required hours of exposure. The implementation of this system to the VIF is one of the factors that contributed to the optimal growth of Lactuca Sactiva L. which further resulted in a greener leaves and reduced harvest time.