Non-destructive sensing and its inverse model for canopy parameters using texture analysis and artificial neural network

Utilisation of micro-precision agriculture is essential as an optimization method in the production system to increase quality of product in plant factory. Plant canopy is one of essential indicators of its quality degradation. In this study, samples of cultured Sunagoke moss Rhacomitrium canescens were used. It has been utilized as an active greening material in a forestation technology to mitigate the urban heat island effect. Canopy parameters for moss as low stature plants remain difficult to be quantified accurately. The direct measurement of canopy parameters was considered relatively inefficient and destructive to the plants. Therefore, artificial intelligence for diagnosing quality degradation is proposed using texture analysis and artificial neural network. It consists of two models covering non-destructive sensing as canopy parameters prediction model and its inverse model as a simple vision model. This paper discussed building both models to monitor canopy parameters using experimental result, as an example of how micro-precision agriculture can be implemented in plant factory. A black box relationship between canopy image and canopy parameters is proposed. Non-destructive sensing is defined that canopy parameters can be predicted from its image while the inverse model refers to the opposite way. Canopy patterns were identified using texture analysis for extracting image based on grey level co-occurrence matrix in to energy, local homogeneity and contrast features. The canopy parameters from experiment were determined as textural class, moisture content, leaf area index and leaf water potential. Finally, artificial neural network was used to model this both way and complex relationship. Artificial neural network model performance was tested successfully to describe the relationship between textural features and canopy parameters using back-propagation supervised learning method and inspection data. Both trained ANN models produced satisfied correlation between measured and predicted value and minimum inspection error. The research result is applicable not only for determining canopy parameters, but also as a simple vision model supporting application of speaking plant approach in plant factory. (c) 2007 Elsevier B.V. All rights reserved.