Artificial intelligence approaches to predict growth, harvest day, and quality of lettuce (Lactuca sativa L.) in a IoT-enabled greenhouse system

Abstract

A new methodology is presented to predict several physiological parameters related to leaf lettuce (Lactuca sativa L.) plant growth (number of leaves, contour area of leaves, and dry mass), photosynthesis (net rate) and transpiration to decipher the harvest time and growth quality of lettuce grown in a hydroponic system. To that end, some artificial intelligence tools were integrated, fuzzy logic, neural networks and a hybrid of both; i.e. neural fuzzy. A small-scale hydroponic cultivation system using the Internet of Things (IoT) was utilised for environmental data collection and imaging systems during the commercial production of lettuce for measuring and predicting plant growth parameters in a greenhouse. The results show the modelling effectiveness and feasibility of the artificial intelligence approaches, which can be employed to provide decisive management information to farmers.