Abstract
Although many smart farming related approaches have been proposed to support farmers, crop modeling in smart farming and most effective factors for the yield remains an open problem. In this paper, we introduce Long Short Term Memory (LSTM) and Attention score mechanism, which gives the most effective factors to tomato yield using tomato growing under smart farm condition data set. Our finding shows that plant factors are more important as well as environmental factors. Next, we proposed DA-LSTM model for tomato yield prediction and best time frame for harvest based on a deep learning algorithm. This model shows high accuracy when compared with LSTM, XGBR and Support Vector Regression (SVR).
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Acknowledgment
We gratefully acknowledge support from Australian Government Research Training Program Scholarship. This work was supported by the International Cooperation Project of Institute of Information Engineering, Chinese Academy of Sciences under Grant No. Y7Z0511101, and Guangxi Key Laboratory of Trusted Software (No KX201528).
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De Alwis, S., Zhang, Y., Na, M., Li, G. (2019). Duo Attention with Deep Learning on Tomato Yield Prediction and Factor Interpretation. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11672. Springer, Cham. https://doi.org/10.1007/978-3-030-29894-4_56
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DOI: https://doi.org/10.1007/978-3-030-29894-4_56
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