Machine learning for the identification of colour cues to estimate quality parameters of rocket leaves

Abstract

Computer Vision Systems (CVSs) have proved to be a powerful tool to evaluate the quality of agricultural products in a non-destructive, contactless, sustainable and objective way. Machine learning techniques have proved to simplify the development of CVS and to provide better performance and greater flexibility in matching the requirements of different products and environmental characteristics, but they are often computationally complex and difficult to be understood by humans. It is desirable to develop methods that exploit the benefits of learning and generate simple and fast solutions that are also interpretable by humans. The approach described in this paper analyses a previously developed and effective machine learning model to extract the information useful to develop computationally light and easily understandable algorithms that evaluate the characteristics of interest on rocket leaves. A Random Forest model previously developed to classify visual quality and to estimate chlorophyll and ammonia contents in rocket leaves has been studied to identify a small set of visual characteristics (colours) that correlate with relevant properties of the product. These visual characteristics have been used as input for several simple, fast and easily understandable algorithms that classify visual quality (QL) and estimate chlorophyll and ammonia contents with lower computational complexities compared to the original Random Forest model. Results obtained by these methods are shown and compared with the ones provided by the original Random Forest model. All the algorithms provided a good separation between marketable and non-marketable samples. They required from 1ms to 22 ms to classify a new sample instead of the 25 ms of the original Random Forest model. Additionally, two methods provided good prediction of chlorophyll (R2v = 0.70) and ammonia (R2v = 0.72) contents requiring only 3 ms and 1 ms respectively.