التفاصيل البيبلوغرافية
| العنوان: |
An investigation into table grape risk factors that affect quality along the export supply chain |
| المؤلفون: |
Rossouw, Christopher Guillaume |
| المساهمون: |
Goedhals-Gerber, Leila Louise, Mantadelis, Theofrastos, Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics. |
| بيانات النشر: |
Stellenbosch University |
| سنة النشر: |
2022 |
| المجموعة: |
Stellenbosch University: SUNScholar Research Repository |
| مصطلحات موضوعية: |
Grapes -- Diseases and pests -- South Africa, Table grapes -- South Africa, Fruit trade -- South Africa, Food supply -- South Africa, UCTD |
| الوصف: |
Thesis (MCom)--Stellenbosch University, 2022. ; ENGLISH SUMMARY: Table grapes are a highly perishable product, where a large proportion of grapes produced for export to Europe arrive in a substandard condition. Fruit in this condition requires repacking to remove the rotten food parts, or in extreme cases, the entire shipment is dumped resulting in a total loss. Both outcomes’ result in a potential loss of income for stakeholders and food waste, which could be avoided if proper upstream intervention had been taken. This ongoing occurrence prompted the investigation into what the factors are that cause the poor arrival quality of table grapes. The study also applied machine learning techniques to predict the probable arrival scores (green, amber, and red) based on input variables gathered throughout the supply chain. The data analysed was obtained from five diverse secondary sources consisting of intake quality shed reports, arrival quality reports, logistical nominal data, recorder temperature data, and climate data. The eventual dataset consisted of 467 observations. The analysis process applied consisted of descriptive and inferential statistics to explain the relationship between the upstream variables and the downstream resultant quality scores as well as how the upstream variables interact with one another. The results from the preliminary analysis aided in feature selection for the model building process. Four classification models, consisting of Logistic Regression, k-Nearest Neighbours, Decision Trees, and Random Forests (RF), were trained, and evaluated. The RF classifier demonstrated the best cross-validation score on the training data and was retained for further evaluation. The RF classifier’s accuracy score was 0.63 for the unseen test set and performed best when predicting red class-labels but struggled on green and performed worst for amber class predictions. Variables that had the largest impact on the arrival quality scores consisted of the climactic variables two weeks prior to harvest, the ... |
| نوع الوثيقة: |
thesis |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
http://hdl.handle.net/10019.1/126114 |
| الاتاحة: |
http://hdl.handle.net/10019.1/126114 |
| Rights: |
Stellenbosch University |
| رقم الانضمام: |
edsbas.6B47ABAE |
| قاعدة البيانات: |
BASE |