Articles

Amodio, M.L., Attolico, G., Bonelli, L., Cefola, M., Fazayeli, H., Montesano, F., Pace, B., Palumbo, M., Serio, F., Stasi, A., Colelli, G. (2023). Sustaining low-impact practices in horticulture through non-destructive approach to provide more information on fresh produce history & quality: the SUS&LOW project. Advances in Horticultural Science, 37(1),123-132. https://doi.org/10.36253/ahsc-13899

Cozzolino, R., Palumbo, M., Cefola, M., Laurino, C., Siano, F., De Giulio, B., Pace, B. (2023). E-nose and Attenuated Total Reflectance-Fourier Transform Infrared data to estimate the shelf-life of fresh-cut Barattiere packaged in polypropylene or in biodegradable polylactic acid bags. Food Packaging and Shelf Life, 23, 101130. https://doi.org/10.1016/j.fpsl.2023.101130

Krishnan, U., Palumbo, M., Attolico, G. (2023). Semantic segmentation of packaged and unpackaged fresh-cut apples using deep learning. Applied Science, 13, 6969. https://doi.org/10.3390/app13126969

Palumbo, M., Cefola, M., Pace, B., Attolico, G., Colelli, G. (2023). Computer vision system based on conventional imaging for non-destructively evaluating quality attributes in fresh and packaged fruit and vegetables. Postharvest Biology and Technology, 200, 112332. https://doi.org/10.1016/j.postharvbio.2023.112332

Palumbo, M., Pace, B., Cefola, M., Montesano, F. F., Colelli, G., Attolico, G. (2022). Non-destructive and contactless estimation of chlorophyll and ammonia contents in packaged fresh-cut rocket leaves by a Computer Vision System. Postharvest Biology and Technology, 189, 111910. https://doi.org/10.1016/j.postharvbio.2022.111910

Palumbo, M., Pace, B., Cefola, M., Montesano, F. F., Serio, F., Colelli, G., Attolico, G. (2021). Self-configuring CVS to discriminate rocket leaves according to cultivation practices and to correctly attribute visual quality level. Agronomy, 11(7), 1353. https://doi.org/10.3390/agronomy11071353

Pio G., Polimena S., Cefola M., Palumbo M., Ceci M., Attolico G. (2023). Tackling non-uniform value distributions with random forests for the non-invasive and explainable estimation of ammonia and chlorophyll of fresh-cut rocket leaves. Computers and Electronics in Agriculture.

Palumbo M., Cefola M., Pace B., Colelli G., Attolico G. (2023). Machine learning for the identification of colour cues to estimate quality parameters of rocket leaves. Journal of Food Engineering.

Palumbo M., Bonelli L., Pace B., Montesano F.F., Serio F., Cefola M. (2023). Sustainable fertilization and soilless cultivation to preserve yield and postharvest quality of fresh-cut wild rocket. Plants.

Fazayeli H., Amodio M.L., Fatchurrahman D., Serio F., Montesano F.F., Burudd I., Peruzzie A., Colelli G. (2023). Potential application of hyperspectral imaging and FT-NIR spectroscopy for discrimination of soilless tomato according to growing techniques, water use efficiency and fertilizer productivity. Scientia Horticulturae.