隨著農產環境的改變，現今甘藷的種植已無法如早期一般粗放管理。生產成本的提升使得管理農地的企業必須更精確的估計甘藷的優良產量，降低供應短缺的風險與成本，甚至是進一步地提升甘藷優良產量，最大化農地產值。過去優良產量預測多用人工視察與田間取樣的方式，判斷甘藷的生長狀況，推論最終的優良產量。但這樣的分析需要長期追蹤，也無法進行早期的供應規劃。故本研究以國內某甘藷契作廠商提供的甘藷生產資訊、某農業改良場提供的農業氣象資訊與農委會提供的甘藷批發價格。彙集種植日至種植日後4日的資訊，使用四種機器學習方法，建立可在甘藷種植初期預測優良產量的模型。研究亦使用階層式特徵分群與模擬退火法特徵選取的處理，刪除無關預測的特徵，以及與其它特徵組合高度相關的冗餘特徵。研究結果發現，梯度提升迴歸樹具有最佳的表現，其對於一般優良產量區間(2000-4000斤/分)的誤差約為其它區間(2000斤/分以下與4000斤/分以上)的一半。而特徵選取的結果呈現，種植後4日的資訊並可能較種植日後3日的資訊缺乏預測力。

With the changes in the agro-industrial environment, the cultivation of sweet potatoes cannot be managed as extensively as before. The increase in production costs makes it more necessary for enterprises to estimate the premium-grade yield of sweet potatoes accurately. They try to reduce the risk and cost of supply shortages, increase the yield of sweet potatoes and maximize it value even further.

Therefore, the study analyzed the data of sweet potato production information provided by a domestic sweet potato manufacturer, the agro-meteorological information provided by a agricultural improvement station, and the wholesale price of sweet potatoes provided by the COA. Collecting and compacting the information of planting day to 4 days after planting, the study established four kinds of machine learning model to predicting premium-grade yields at the early stage of sweet potato planting. The study also applied hierarchical feature clustering and simulated annealing based feature selection to remove irrelevant predicted features, and redundant features those are highly correlated with other feature combinations.

The results show that the gradient boosting regression tree had the best performance. The error for the general premium-grade yield interval (2000-4000 catty/0.1ha) was roughly half of other intervals (less than 2000 kg/0.1ha or more than 4,000 catty/0.1ha). In terms of feature selection, the experiment show that the 4 day after planting information may not as predictable as the information from the day of planting to 3 days after planting.

Breiman, Leo; Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1987). Occam's razor. Information processing letters, 24(6), 377-380.
Belehu, T., & Hammes, P. S. (2004). Effect of temperature, soil moisture content and type of cutting on establishment of sweet potato cuttings. South African Journal of Plant and Soil, 21(2), 85-89.
Brownlee, J. (2011). Clever algorithms: nature-inspired programming recipes. Jason Brownlee.
Chen, X., Kou, M., Tang, Z., Zhang, A., Li, H., & Wei, M. (2017). Responses of root physiological characteristics and yield of sweet potato to humic acid urea fertilizer. PloS one, 12(12), e0189715.
Dash, M., & Liu, H. (2003). Consistency-based search in feature selection. Artificial intelligence, 151(1-2), 155-176.
Edmond, J. B., & Ammerman, G. R. (1971). Sweet potatoes: Production, processing, marketing.
Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.
Fogel, D. B., & Fogel, L. J. (1995, September). An introduction to evolutionary programming. In European Conference on Artificial Evolution (pp. 21-33). Springer, Berlin, Heidelberg.
Glover, F. (1989). Tabu search—part I. ORSA Journal on computing, 1(3), 190-206.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157-1182.
Gajanayake, B., Reddy, K. R., Shankle, M. W., & Arancibia, R. A. (2013). Early-season soil moisture deficit reduces sweetpotato storage root initiation and development. HortScience, 48(12), 1457-1462.
Haupt, R. L. (1995). An introduction to genetic algorithms for electromagnetics. IEEE Antennas and Propagation Magazine, 37(2), 7-15.
Hsu, H. H., & Hsieh, C. W. (2010). Feature Selection via Correlation Coefficient Clustering. JSW, 5(12), 1371-1377.
John, G. H., Kohavi, R., & Pfleger, K. (1994). Irrelevant features and the subset selection problem. In Machine Learning Proceedings 1994 (pp. 121-129).
Jović, A., Brkić, K., & Bogunović, N. (2015, May). A review of feature selection methods with applications. In Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2015 38th International Convention on (pp. 1200-1205). IEEE.
Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial intelligence, 97(1-2), 273-324.
Mitchell, T. (1997). Machine Learning. New York: McGraw-Hill.
Miao, R., Khanna, M., & Huang, H. (2015). Responsiveness of crop yield and acreage to prices and climate. American Journal of Agricultural Economics, 98(1), 191-211.
O'BRIEN, P. J. (1972). The Sweet Potato: Its Origin and Dispersal 1. American anthropologist, 74(3), 342-365.
Onwueme, I. C., & Johnston, M. (2000). Influence of shade on stomatal density, leaf size and other leaf characteristics in the major tropical root crops, tannia, sweet potato, yam, cassava and taro. Experimental Agriculture, 36(4), 509-516.
Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on pattern analysis and machine intelligence, 27(8), 1226-1238.
Robnik-Šikonja, M., & Kononenko, I. (1997, July). An adaptation of Relief for attribute estimation in regression. In Machine Learning: Proceedings of the Fourteenth International Conference (ICML’97) (Vol. 5, pp. 296-304).
Ravi, V., Naskar, S. K., Makeshkumar, T., Babu, B., & Krishnan, B. P. (2009). Molecular physiology of storage root formation and development in sweet potato (Ipomoea batatas (L.) Lam.). J Root Crops, 35(1), 1-27.
Spence, J. A., & Humphries, E. C. (1972). Effect of moisture supply, root temperature, and growth regulators on photosynthesis of isolated rooted leaves of sweet potato (Ipomoea batatas). Annals of Botany, 36(1), 115-121.
von Laarhoven, P. J. M., & Aarts, E. H. (1987). Simulated annealing: theory and applications. Mathematics and its Applications, Kluwer, Dordrecht.
Villordon, A. Q., La Bonte, D. R., Firon, N., Kfir, Y., Pressman, E., & Schwartz, A. (2009). Characterization of adventitious root development in sweetpotato. HortScience, 44(3), 651-655.
Villordon, A., Clark, C., Smith, T., Ferrin, D., & LaBonte, D. (2010). Combining linear regression and machine learning approaches to identify consensus variables related to optimum sweetpotato transplanting date. HortScience, 45(4), 684-686.
Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301), 236-244.
Wooldridge, J. M. (2015). Introductory econometrics: A modern approach(pp. 69-70). Nelson Education.
Yu, L., & Liu, H. (2004). Efficient feature selection via analysis of relevance and redundancy. Journal of machine learning research, 5(Oct), 1205-1224.
Zhang, H., Xue, Y., Wang, Z., Yang, J., & Zhang, J. (2009). Morphological and physiological traits of roots and their relationships with shoot growth in “super” rice. Field Crops Research, 113(1), 31-40.

李良(1981)。不同期作對甘藷生長收量及品質之影響。科學發展月刊。9(8)：693-706。
姚銘輝、陳智霖、邱郁凱、洪福良(2016)。計算流體力學於農業溫室的應用。農業試驗所技術服務季刊。108。
徐芳玉(2017)，「應用機器學習於作物生長關鍵因素及產量與良率預測之研究」。長榮大學。資訊管理學研究所。
陳孟萱(2014)，「應用機器學習方法於作物種植條件之研究」，國立成功大學，會計學系研究所。
鄭勝華(1985)。食物地理研究。師大地理研究報告第12期。pp.261-286。國立台灣師範大學。
劉志偉(2009)。國際農糧體制與臺灣的糧食依賴： 戰後臺灣養豬業的歷史考察。臺灣史研究。16(2):105-160。
賴永昌、李炳和、劉復誠(1996)。春秋作甘藷產量差異之探討。中華農業氣象。3:169-173。
賴永昌、黃哲倫 (2012)。食用甘藷栽培技術及品種介紹。農業試驗所特刊甘藷健康管理技術暨操作手冊。163。
羅淑芳、林靜怡、黃守宏(2016)。北部地區冬季甘藷之健康種苗繁殖技術、蟲害發生調查及成本分析。作物、環境與生物資訊13(3):pp.116 – 125