Early and accurate nutrient deficiency detection in hydroponic crops using ensemble machine learning and hyperspectral imaging

Abstract

Vertical indoor hydroponic farms are growing as a technological solution fostering agriculture productivity to address the ever increasing food demands in sustainable cities. These farms provide extensive control over the growing conditions to ensure all-weather cultivation of diverse crops within the available limited space. However, to assure the quality of hydroponic crops, continuous close-range crop monitoring and early detection of deficiencies are essential. Sensitive techniques such as hyperspectral imaging combined with ensemble based machine learning techniques have proven to provide improved reliable results. However, despite their potential, the application of these methods for early-stage nutrient deficiency detection in crops remains relatively underexplored. In this context, this research presents and proposes different machine learning-based approaches that utilizes ensemble techniques such as Random Forest (RF), Bagging or Bootstrap Aggregating, Adaboost or Adaptive Boosting, and eXtreme Gradient Boosting (XGB) classifiers for early detection of nutrient deficiencies in hydroponic crops. In the proposed approach, the features extracted from hyperspectral datacubes are trained to create machine learning models. Among the investigated models, the XGB classifier demonstrated the fastest computational time and test accuracy of 18.07 s and 99.6 %, respectively. This research also proposes a novel computer vision (CV) based approach to improve the tedious manual data labelling process involved in HSI dataset creation. Envisioned as an invaluable tool, the proposed non-invasive imaging system could detect as early as 3 days after stress induction and revolutionize the automated monitoring of indoor hydroponic farms with enhanced accuracy for a sustainable future.