Self-Configuring CVS to Discriminate Rocket Leaves According to Cultivation Practices and to Correctly Attribute Visual Quality Level
Michela Palumbo,
Bernardo Pace,
Maria Cefola,
Francesco Fabiano Montesano,
Francesco Serio,
Giancarlo Colelli,
Giovanni Attolico
Affiliations
Michela Palumbo
Institute of Sciences of Food Production, National Research Council (CNR), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
Bernardo Pace
Institute of Sciences of Food Production, National Research Council (CNR), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
Maria Cefola
Institute of Sciences of Food Production, National Research Council (CNR), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy
Francesco Fabiano Montesano
Institute of Sciences of Food Production, CNR-National Research Council of Italy Via G. Amendola, 122/O, 70126 Bari, Italy
Francesco Serio
Institute of Sciences of Food Production, CNR-National Research Council of Italy Via G. Amendola, 122/O, 70126 Bari, Italy
Giancarlo Colelli
Department of Agriculture, Food, Natural Resources and Engineering, University of Foggia, Via Napoli 25, 71122 Foggia, Italy
Giovanni Attolico
Institute on Intelligent Industrial Systems and Technologies for Advanced Manufacturing, CNR-National Research Council of Italy Via G. Amendola, 122/O, 70126 Bari, Italy
Computer Vision Systems (CVS) represent a contactless and non-destructive tool to evaluate and monitor the quality of fruits and vegetables. This research paper proposes an innovative CVS, using a Random Forest model to automatically select the relevant features for classification, thereby avoiding their choice through a cumbersome and error-prone work of human designers. Moreover, three color correction techniques were evaluated and compared, in terms of classification performance to identify the best solution to provide consistent color measurements. The proposed CVS was applied to fresh-cut rocket, produced under greenhouse soilless cultivation conditions differing for the irrigation management strategy and the fertilization level. The first aim of this study was to objectively estimate the quality levels (QL) occurring during storage. The second aim was to non-destructively, and in a contactless manner, identify the cultivation approach using the digital images of the obtained product. The proposed CVS achieved an accuracy of about 95% in QL assessment and about 65–70% in the discrimination of the cultivation approach.
