A novel random forest-based approach for the non-destructive and explainable estimation of ammonia and chlorophyll in fresh-cut rocket leaves

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Stefano Polimena , Gianvito Pio , Maria Cefola , Michela Palumbo , Michelangelo Ceci , Giovanni Attolico
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引用次数: 0

Abstract

The perceived visual quality of fruits and vegetables plays a central role in the choices made by retail customers. Machine learning (ML) approaches based on image analysis have been recently proposed to overcome the poor efficiency and subjectivity of human visual evaluation as well as the expensiveness and destructiveness of physical and chemical methods that measure internal indicators. In this paper, we propose a ML method based on Random Forests for estimating the chlorophyll and ammonia contents (considered, in the literature, reliable indicators of product freshness) from images of fresh-cut rocket leaves. Our approach copes with specific issues raised by (i) the non-uniform distributions of ammonia and chlorophyll values and (ii) the need to provide insights into the features that produce a particular model outcome, aiming to enhance its trustworthiness. Our experiments, performed on real images of fresh-cut rocket leaves, proved that the proposed approach significantly outperforms 7 competitor methods, obtaining an improvement of the RSE results of 6.6% for the prediction of the ammonia and of 10.4% for the prediction of the chlorophyll over its best competitor. Moreover, a specific analysis of the explainability of the predictions showed that the learned models are based on reasonable features, empowering their acceptance in real-world applications.
一种基于随机森林的新方法,用于无损和可解释地估计鲜切火箭叶片中氨和叶绿素
水果和蔬菜的视觉感知质量在零售顾客的选择中起着核心作用。最近提出了基于图像分析的机器学习(ML)方法,以克服人类视觉评估的低效率和主观性,以及测量内部指标的物理和化学方法的昂贵和破坏性。在本文中,我们提出了一种基于随机森林的机器学习方法,用于从新鲜切割的火箭叶片图像中估计叶绿素和氨含量(在文献中被认为是产品新鲜度的可靠指标)。我们的方法处理了以下方面提出的具体问题:(i)氨和叶绿素值的不均匀分布以及(ii)需要提供对产生特定模型结果的特征的见解,旨在提高其可信度。我们在鲜切火箭叶片的真实图像上进行的实验证明,该方法显著优于7种竞争对手的方法,在预测氨的RSE结果上比最佳竞争对手提高了6.6%,在预测叶绿素的RSE结果上比最佳竞争对手提高了10.4%。此外,对预测的可解释性的具体分析表明,学习模型基于合理的特征,使其能够在现实世界的应用中被接受。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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