| Title: |
Smart Sensors and Artificial Intelligence Driven Alert System for Optimizing Red Peppers Drying in Southern Italy |
| Authors: |
Fiorentino C.; D'Antonio P.; Toscano F.; Capece N.; Conceicao L. A.; Scalcione E.; Modugno F.; Sannino M.; Colonna R.; Lacetra E.; Di Mambro G. |
| Contributors: |
Fiorentino, C.; D'Antonio, P.; Toscano, F.; Capece, N.; Conceicao, L. A.; Scalcione, E.; Modugno, F.; Sannino, M.; Colonna, R.; Lacetra, E.; Di Mambro, G. |
| Publication Year: |
2025 |
| Collection: |
IRIS Università degli Studi di Napoli Federico II |
| Subject Terms: |
red pepper; weather station; machine learning; smart sensors; greenhouse |
| Description: |
The Senise red pepper, known as peperone crusco, is a protected geographical indication (PGI) product from Basilicata, Italy, traditionally consumed dried. Producers use semi-open greenhouses to meet PGI standards, but significant losses are caused by rot from microorganisms thriving in high moisture, temperature, and humidity, which also encourage pest infestations. To minimize losses, a low-cost alert system was developed. The study, conducted in summer 2022 and 2023, used external parameters from the ALSIA Senise weather station and internal sensors monitoring the air temperature and humidity inside the greenhouse. Since rot is complex and difficult to model, an artificial intelligence (AI)-based approach was adopted. A feed forward neural network (FFNN) estimated greenhouse climate conditions as if it were empty, comparing them with actual values when peppers were present. This revealed the most critical period was the first 3–4 days after introduction and identified a critical air relative humidity threshold. The system could also predict microclimatic parameters inside the greenhouse with red peppers, issuing warnings one hour before risk conditions arose. In 2023, it was tested by comparing predicted values with previously identified thresholds. When critical levels were exceeded, greenhouse operators were alerted to adjust conditions. In 2023, pepper rot decreased. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/wos/WOS:001431816800001; volume:17; issue:4; numberofpages:21; journal:SUSTAINABILITY; https://hdl.handle.net/11588/998278 |
| DOI: |
10.3390/su17041682 |
| Availability: |
https://hdl.handle.net/11588/998278; https://doi.org/10.3390/su17041682 |
| Rights: |
info:eu-repo/semantics/openAccess ; license:Dominio pubblico ; license uri:http://creativecommons.org/publicdomain/zero/1.0/ |
| Accession Number: |
edsbas.A53DBF5D |
| Database: |
BASE |