Explainable artificial intelligence to estimate the Sri Lankan (Ceylon) Tea crop yield
Accurate cash crop yield prediction is important in the context of changing climate to ensure sustainable development. Artificial intelligence (AI) has played a significant role in prediction in nonlinear systems including cash crop prediction. However, despite its global recognition, Sri Lanka'...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525002321 |
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| author | Lakindu Mampitiya Harindu S. Sumanasekara Namal Rathnayake Yukinobu Hoshino Upaka Rathnayake |
| author_facet | Lakindu Mampitiya Harindu S. Sumanasekara Namal Rathnayake Yukinobu Hoshino Upaka Rathnayake |
| author_sort | Lakindu Mampitiya |
| collection | DOAJ |
| description | Accurate cash crop yield prediction is important in the context of changing climate to ensure sustainable development. Artificial intelligence (AI) has played a significant role in prediction in nonlinear systems including cash crop prediction. However, despite its global recognition, Sri Lanka's renowned Ceylon Tea has never been modeled for yield prediction based on meteorological, soil, and fertilizer parameters. Sri Lanka is one of the most impacted countries due to climate change while its agricultural production is significant in economic contribution. Therefore, this study presents the first-ever research to predict the tea crop yield with respect to meteorological, soil, and fertilizer features. State-of-the-art machine learning models were tested with the aid of Dampahala Tea Company production. Results showcased that the CatBoost algorithm produces the best prediction model with a coefficient of determination, RCatBoost2= of 0.90374 (other models tested, RXGBoost2=0.87385;RLightGBM2=0.79772;RANN2=0.57346;RRidge2=0.42033;RLASSO2=0.42512;RElasticNet2=0.42168). In addition, explainability aspects using SHAP showcased that the morning relative humidity, evaporation, and T0 200 fertilizer have a significant impact on the prediction model. The developed AI model can be disseminated across the country to enhance understanding of predictions under future climatic scenarios. |
| format | Article |
| id | doaj-art-89264d7ee9524fdc9510c7d4efd92d7b |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-89264d7ee9524fdc9510c7d4efd92d7b2025-08-20T02:31:44ZengElsevierSmart Agricultural Technology2772-37552025-08-011110099910.1016/j.atech.2025.100999Explainable artificial intelligence to estimate the Sri Lankan (Ceylon) Tea crop yieldLakindu Mampitiya0Harindu S. Sumanasekara1Namal Rathnayake2Yukinobu Hoshino3Upaka Rathnayake4Water Resources Management and Soft Computing Research Laboratory, Millennium City, Athurugiriya 10150, Sri LankaRegional Integrated Multi-Hazard Early Warning System (RIMES) - Sri Lanka National Centre for Climate Applications (SNCCA), Irrigation Department, Colombo, Sri LankaDepartment of Civil Engineering, Faculty of Engineering, The University of Tokyo, Bunkyo City, Tokyo 113-8656, JapanSchool of Systems Engineering, Kochi University of Technology, Tosayamada, Kami, Kochi 782-8502, JapanDepartment of Civil Engineering and Construction, Faculty of Engineering and Design, Atlantic Technological University, F91 YW50 Sligo, Ireland; Corresponding author.Accurate cash crop yield prediction is important in the context of changing climate to ensure sustainable development. Artificial intelligence (AI) has played a significant role in prediction in nonlinear systems including cash crop prediction. However, despite its global recognition, Sri Lanka's renowned Ceylon Tea has never been modeled for yield prediction based on meteorological, soil, and fertilizer parameters. Sri Lanka is one of the most impacted countries due to climate change while its agricultural production is significant in economic contribution. Therefore, this study presents the first-ever research to predict the tea crop yield with respect to meteorological, soil, and fertilizer features. State-of-the-art machine learning models were tested with the aid of Dampahala Tea Company production. Results showcased that the CatBoost algorithm produces the best prediction model with a coefficient of determination, RCatBoost2= of 0.90374 (other models tested, RXGBoost2=0.87385;RLightGBM2=0.79772;RANN2=0.57346;RRidge2=0.42033;RLASSO2=0.42512;RElasticNet2=0.42168). In addition, explainability aspects using SHAP showcased that the morning relative humidity, evaporation, and T0 200 fertilizer have a significant impact on the prediction model. The developed AI model can be disseminated across the country to enhance understanding of predictions under future climatic scenarios.http://www.sciencedirect.com/science/article/pii/S2772375525002321Explainable artificial intelligenceMachine learning techniquesMeteorological parametersSri lankan (Ceylon) TeaYIELD prediction |
| spellingShingle | Lakindu Mampitiya Harindu S. Sumanasekara Namal Rathnayake Yukinobu Hoshino Upaka Rathnayake Explainable artificial intelligence to estimate the Sri Lankan (Ceylon) Tea crop yield Smart Agricultural Technology Explainable artificial intelligence Machine learning techniques Meteorological parameters Sri lankan (Ceylon) Tea YIELD prediction |
| title | Explainable artificial intelligence to estimate the Sri Lankan (Ceylon) Tea crop yield |
| title_full | Explainable artificial intelligence to estimate the Sri Lankan (Ceylon) Tea crop yield |
| title_fullStr | Explainable artificial intelligence to estimate the Sri Lankan (Ceylon) Tea crop yield |
| title_full_unstemmed | Explainable artificial intelligence to estimate the Sri Lankan (Ceylon) Tea crop yield |
| title_short | Explainable artificial intelligence to estimate the Sri Lankan (Ceylon) Tea crop yield |
| title_sort | explainable artificial intelligence to estimate the sri lankan ceylon tea crop yield |
| topic | Explainable artificial intelligence Machine learning techniques Meteorological parameters Sri lankan (Ceylon) Tea YIELD prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525002321 |
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