Using AI Approaches for Predicting Tomato Growth in Hydroponic Systems

Abstract

In any hydroponic system, effective plant growth and yield prediction are important in precision farming. Improving model growth systems can improve plant growth conditions for enhanced crop production and better quality food and increase resource use efficiency (e.g. light use efficiency, fertiliser use efficiency, water use efficiency) that meets the market demand with lower costs. Recently, different Artificial Intelligence (AI) techniques include machine learning and, deep learning methods are employed for providing powerful analytical tools. The proposed research in this study uses different machine/deep learning techniques to predict yield and plant growth variation for tomato crop (Tiny Tim mini tomato) under three different experimental conditions. The three tomato plants were grown under 3 different light treatments. The number of yielded fruits is predicted depends on the environmental conditions in each light treatment. This research deploys deep learning techniques includes Bidirectional Long short-term Memory (Bi-LSTM) with an attention mechanism for punctuation restoration and a standard Long Short-Term Memory (LSTM) recurrent neuron network. As well as, some other machine learning techniques including Support Vector Machine (SVM) and Random Forest (RF) for predicting the tomato yield. In the three different treatments, the tomato fruits yield growth and number yielded fruits values are used by the employed AI techniques to model the targeted growth parameters. The comparative results obtained from each technique are presented and discussed utilising the cross-validation process, to evaluate the performance achieved by the different methods. Very promising results, based on the data generated from the tomato growth and development with three light treatments are presented. The results that have been achieved are (97.8%) and (88.2%) achieved by employing the Bi-LSTM and LSTM algorithms, respectively.