Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

Abstract

Water stress and in particular drought are some of the most significant factors affecting plant growth, food production, and thus food security. Furthermore, the possibility to predict and shape irrigation on real plant demands is priceless. The objective of this study is to characterize, classify, and forecast water stress in tomato plants by means of in vivo real time data obtained through a novel sensor, named bioristor, and of different artificial intelligence models. First of all, we have applied classification models, namely Decision Trees and Random Forest, to try to distinguish four different stress statuses of tomato plants. Then, we have predicted, through the help of recurrent neural networks, the future status of a plant when considering both a binary (water stressed and not water stressed) and a four-status scenario. The obtained results are very good in terms of accuracy, precision, recall, F-measure, and of the resulting confusion matrices, and they suggest that the considered novel data and features coming from the bioristor, together with the used machine and deep learning models, can be successfully applied to real-world on-the-field smart irrigation scenarios in the future.

Keywords

• AI modeling and forecasting
• bioristor
• precision agriculture
• recurrent neural network
• tomato plants
• tree-based classifiers