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Applications of Machine Learning in Apple Crop Yield Prediction
This study proposes the application of machine learning techniques to predict yield in the apple industry. Crop yield prediction is important because it impacts resource and capacity planning. It is, however, challenging because yield is affected by multiple interrelated factors such as climate cond...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2022
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| _version_ | 1867613168026517504 |
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| access_status_str | Open Access |
| author | van den Heever, Deirdre |
| author2 | Britz, Stefan S |
| author_browse | Britz, Stefan S van den Heever, Deirdre |
| author_facet | Britz, Stefan S van den Heever, Deirdre |
| author_sort | van den Heever, Deirdre |
| collection | Thesis |
| description | This study proposes the application of machine learning techniques to predict yield in the apple industry. Crop yield prediction is important because it impacts resource and capacity planning. It is, however, challenging because yield is affected by multiple interrelated factors such as climate conditions and orchard management practices. Machine learning methods have the ability to model complex relationships between input and output features. This study considers the following machine learning methods for apple yield prediction: multiple linear regression, artificial neural networks, random forests and gradient boosting. The models are trained, optimised, and evaluated using both a random and chronological data split, and the out-of-sample results are compared to find the best-suited model. The methodology is based on a literature analysis that aims to provide a holistic view of the field of study by including research in the following domains: smart farming, machine learning, apple crop management and crop yield prediction. The models are built using apple production data and environmental factors, with the modelled yield measured in metric tonnes per hectare. The results show that the random forest model is the best performing model overall with a Root Mean Square Error (RMSE) of 21.52 and 14.14 using the chronological and random data splits respectively. The final machine learning model outperforms simple estimator models showing that a data-driven approach using machine learning methods has the potential to benefit apple growers. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/36192 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:50.330Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/36192 Applications of Machine Learning in Apple Crop Yield Prediction van den Heever, Deirdre Britz, Stefan S Statistical Sciences This study proposes the application of machine learning techniques to predict yield in the apple industry. Crop yield prediction is important because it impacts resource and capacity planning. It is, however, challenging because yield is affected by multiple interrelated factors such as climate conditions and orchard management practices. Machine learning methods have the ability to model complex relationships between input and output features. This study considers the following machine learning methods for apple yield prediction: multiple linear regression, artificial neural networks, random forests and gradient boosting. The models are trained, optimised, and evaluated using both a random and chronological data split, and the out-of-sample results are compared to find the best-suited model. The methodology is based on a literature analysis that aims to provide a holistic view of the field of study by including research in the following domains: smart farming, machine learning, apple crop management and crop yield prediction. The models are built using apple production data and environmental factors, with the modelled yield measured in metric tonnes per hectare. The results show that the random forest model is the best performing model overall with a Root Mean Square Error (RMSE) of 21.52 and 14.14 using the chronological and random data splits respectively. The final machine learning model outperforms simple estimator models showing that a data-driven approach using machine learning methods has the potential to benefit apple growers. 2022-03-22T09:53:25Z 2022-03-22T09:53:25Z 2021 2022-03-22T07:03:16Z Master Thesis Masters MSc http://hdl.handle.net/11427/36192 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences van den Heever, Deirdre Applications of Machine Learning in Apple Crop Yield Prediction |
| thesis_degree_str | Master's |
| title | Applications of Machine Learning in Apple Crop Yield Prediction |
| title_full | Applications of Machine Learning in Apple Crop Yield Prediction |
| title_fullStr | Applications of Machine Learning in Apple Crop Yield Prediction |
| title_full_unstemmed | Applications of Machine Learning in Apple Crop Yield Prediction |
| title_short | Applications of Machine Learning in Apple Crop Yield Prediction |
| title_sort | applications of machine learning in apple crop yield prediction |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/36192 |
| work_keys_str_mv | AT vandenheeverdeirdre applicationsofmachinelearninginapplecropyieldprediction |