Developing an IoT and ML-driven platform for fruit ripeness evaluation and spoilage detection: A case study on bananas
Food waste is a significant global problem that demands immediate action to reduce it. This study presents a novel framework that merges Internet of Things (IoT) and machine learning (ML) technologies to detect fruit ripeness and spoilage, which is essential in minimizing losses in the cold chain pr...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671125000038 |
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| author | Rajini M Persis Voola |
| author_facet | Rajini M Persis Voola |
| author_sort | Rajini M |
| collection | DOAJ |
| description | Food waste is a significant global problem that demands immediate action to reduce it. This study presents a novel framework that merges Internet of Things (IoT) and machine learning (ML) technologies to detect fruit ripeness and spoilage, which is essential in minimizing losses in the cold chain process of fresh produce industry. The study employed temperature, humidity, and gas emission sensors along with an ESP32 microcontroller to establish a unique framework that achieved exceptional accuracy in predicting banana ripeness stages. This framework employed various machine learning algorithms to detect ripeness stages, with the CatBoost classifier exhibiting exceptional performance, demonstrating its dependability and effectiveness in assessing fruit quality. The benefits of this research extend beyond fruit ripeness detection and pave the way for future advancements in automating quality assessment in agricultural supply chains. |
| format | Article |
| id | doaj-art-76342fa7ad9a4cfe90568956980f6f68 |
| institution | Kabale University |
| issn | 2772-6711 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-76342fa7ad9a4cfe90568956980f6f682025-01-11T06:42:19ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112025-03-0111100896Developing an IoT and ML-driven platform for fruit ripeness evaluation and spoilage detection: A case study on bananasRajini M0Persis Voola1Department of CSE, Government Degree College, Ravulapalem, A.P, India; Corresponding author.Department of CSE, Adikavi Nannaya University, Rajahmundry, A.P, IndiaFood waste is a significant global problem that demands immediate action to reduce it. This study presents a novel framework that merges Internet of Things (IoT) and machine learning (ML) technologies to detect fruit ripeness and spoilage, which is essential in minimizing losses in the cold chain process of fresh produce industry. The study employed temperature, humidity, and gas emission sensors along with an ESP32 microcontroller to establish a unique framework that achieved exceptional accuracy in predicting banana ripeness stages. This framework employed various machine learning algorithms to detect ripeness stages, with the CatBoost classifier exhibiting exceptional performance, demonstrating its dependability and effectiveness in assessing fruit quality. The benefits of this research extend beyond fruit ripeness detection and pave the way for future advancements in automating quality assessment in agricultural supply chains.http://www.sciencedirect.com/science/article/pii/S2772671125000038Internet of ThingsCold chainFruit quality predictionCatBoost classifier |
| spellingShingle | Rajini M Persis Voola Developing an IoT and ML-driven platform for fruit ripeness evaluation and spoilage detection: A case study on bananas e-Prime: Advances in Electrical Engineering, Electronics and Energy Internet of Things Cold chain Fruit quality prediction CatBoost classifier |
| title | Developing an IoT and ML-driven platform for fruit ripeness evaluation and spoilage detection: A case study on bananas |
| title_full | Developing an IoT and ML-driven platform for fruit ripeness evaluation and spoilage detection: A case study on bananas |
| title_fullStr | Developing an IoT and ML-driven platform for fruit ripeness evaluation and spoilage detection: A case study on bananas |
| title_full_unstemmed | Developing an IoT and ML-driven platform for fruit ripeness evaluation and spoilage detection: A case study on bananas |
| title_short | Developing an IoT and ML-driven platform for fruit ripeness evaluation and spoilage detection: A case study on bananas |
| title_sort | developing an iot and ml driven platform for fruit ripeness evaluation and spoilage detection a case study on bananas |
| topic | Internet of Things Cold chain Fruit quality prediction CatBoost classifier |
| url | http://www.sciencedirect.com/science/article/pii/S2772671125000038 |
| work_keys_str_mv | AT rajinim developinganiotandmldrivenplatformforfruitripenessevaluationandspoilagedetectionacasestudyonbananas AT persisvoola developinganiotandmldrivenplatformforfruitripenessevaluationandspoilagedetectionacasestudyonbananas |