Artificial intelligence decision support system based on artificial neural networks to predict the commercialization time by the evolution of peach quality

article

Ananias, Estevão
Gaspar, Pedro Dinis
Soares, V. N.G.J
Caldeira, J.M.L.P.

MDPI

ANANIAS, E. [et al.] (2021) - Artificial intelligence decision support system based on artificial neural networks to predict the commercialization time by the evolution of peach quality. Electronics.10, 2394. DOI https://doi.org/10.3390/electronics10192394

2079-9292

10.3390/electronics10192394

Artificial intelligence decision support system based on artificial neural networks to predict the commercialization time by the evolution of peach quality

Peach
Physical-chemical parameters
Refrigeration environment
Quality
Retail
Artificial intelligence decision support system
Smart cities

PrunusPós, Operation n. PDR2020‐ 101‐031695 (Partner), Consortium n. 87, Initiative n. 175 promoted by PDR2020 and co‐financed by FEADER under the Portugal 2020 initiative

2021-10-18T11:39:34Z
2021-10-18T11:39:34Z
2021

Climacteric fruit such as peaches are stored in cold chambers after harvest and usually are maintained there until the desired ripening is reached to direct these fruit to market. Producers, food industries and or traders have difficulties in defining the period when fruit are at the highest level of quality desired by consumers in terms of the physical-chemical parameters (hardness –H–, soluble solids content –SSC–, and acidity –Ac–). The evolution of peach quality in terms of these parameters depends directly on storage temperature –T– and relative humidity –RH–, as well on the storage duration –t–. This paper describes an Artificial Intelligence (AI) Decision Support System (DSS) designed to predict the evolution of the quality of peaches, namely the storage time required before commercialization as well as the late commercialization time. The peaches quality is stated in terms of the values of SSC, H and Ac that consumers most like for the storage T and RH. An Artificial neuronal network (ANN) is proposed to provide this prediction. The training and validation of the ANN were conducted with experimental data acquired in three different farmers’ cold storage facilities. A user interface was developed to provide an expedited and simple prediction of the marketable time of peaches, considering the storage temperature, relative humidity, and initial physical and chemical parameters. This AI DSS may help the vegetable sector (logistics and retailers), especially smaller neighborhood grocery stores, define the marketable period of fruit. It will contribute with advantages and benefits for all parties—producers, traders, retailers, and consumers—by being able to provide fruit at the highest quality and reducing waste in the process. In this sense, the ANN DSS proposed in this study contributes to new AI-based solutions for smart cities.
info:eu-repo/semantics/publishedVersion

openAccess
http://creativecommons.org/licenses/by/4.0/

eng