Development on Crop Yield Forecasting Model for Major Vegetable Crops using Meteorological Information of Main Production Area

Lim, Chul-Hee; 김강선,; Lee, Eun Jung; Heo, Seulkee; 김태연,; Kim, Young Seok; Woo-Kyun, Lee，

doi:10.15531/ksccr.2016.7.2.193

Cited by 9 publications

(6 citation statements)

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“…In this study, the attention layer was implemented by inspiration from the attention mechanism used in the seq2seq model. The operations performed in the attention layer are shown in Equations ( 7) and (8). First, matrix multiplication is performed by multiplying the three-dimensional input X by the weight W a and adding bias b a .…”

Section: Attention Mechanismmentioning

confidence: 99%

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STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Yin

Dong

et al. 2020

Agriculture

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It is difficult to forecast vegetable prices because they are affected by numerous factors, such as weather and crop production, and the time-series data have strong non-linear and non-stationary characteristics. To address these issues, we propose the STL-ATTLSTM (STL-Attention-based LSTM) model, which integrates the seasonal trend decomposition using the Loess (STL) preprocessing method and attention mechanism based on long short-term memory (LSTM). The proposed STL-ATTLSTM forecasts monthly vegetable prices using various types of information, such as vegetable prices, weather information of the main production areas, and market trading volumes. The STL method decomposes time-series vegetable price data into trend, seasonality, and remainder components. It uses the remainder component by removing the trend and seasonality components. In the model training process, attention weights are assigned to all input variables; thus, the model’s prediction performance is improved by focusing on the variables that affect the prediction results. The proposed STL-ATTLSTM was applied to five crops, namely cabbage, radish, onion, hot pepper, and garlic, and its performance was compared to three benchmark models (i.e., LSTM, attention LSTM, and STL-LSTM). The performance results show that the LSTM model combined with the STL method (STL-LSTM) achieved a 12% higher prediction accuracy than the attention LSTM model that did not use the STL method and solved the prediction lag arising from high seasonality. The attention LSTM model improved the prediction accuracy by approximately 4% to 5% compared to the LSTM model. The STL-ATTLSTM model achieved the best performance, with an average root mean square error (RMSE) of 380, and an average mean absolute percentage error (MAPE) of 7%.

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Section: Attention Mechanismmentioning

confidence: 99%

“…Climate change has a direct impact on agriculture, as the weather is essential information for growing crops [7]. In particular, more accurate production prediction will be possible using weather information about the main production areas where crops are grown [8].…”

Section: Introductionmentioning

confidence: 99%

STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Yin

Dong

et al. 2020

Agriculture

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“…In this study, we evaluated agricultural drought considering climate change by using crop phenology. Although only two representative crops were considered in this study, the effects of climate change on overall agriculture could be predicted deeply with the application of vegetables and other main crops [56].…”

Section: Implications Of the New Agricultural Drought Index Using A Wmentioning

confidence: 99%

Assessment of Agricultural Drought Considering the Hydrological Cycle and Crop Phenology in the Korean Peninsula

Lim

Kim

Chun

et al. 2019

Water

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Hydrological changes attributable to global warming increase the severity and frequency of droughts, which in turn affect agriculture. Hence, we proposed the Standardized Agricultural Drought Index (SADI), which is a new drought index specialized for agriculture and crops, and evaluated current and expected droughts in the Korean Peninsula. The SADI applies crop phenology to the hydrological cycle, which is a basic element that assesses drought. The SADI of rice and maize was calculated using representative hydrological variables (precipitation, evapotranspiration, and runoff) of the crop growing season. In order to evaluate the effectiveness of SADI, the three-month Standardized Precipitation Index, which is a representative drought index, and rainfed crop yield were estimated together. The performance evaluation of SADI showed that the correlation between rainfed crop yield and SADI was very high compared with that of existing drought index. The results of the assessment of drought over the past three decades provided a good indication of a major drought period and differentiated the results for crops and regions. The results of two future scenarios showed common drought risks in the western plains of North Korea. Successfully validated SADIs could be effectively applied to agricultural drought assessments in light of future climate change, and would be a good example of the water-food nexus approach.

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“…The Korean Peninsula is approximately 1100 km in length from south to north and approximately 300 km in width from east to west; the total area is approximately 220,000 km 2 [25]. The Korean Peninsula corresponds to the mid-latitude region (33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43) • N) and is located in the east of the Asian continent; it belongs to the temperate monsoon climate, which is affected by seasonal winds [26]. It is characterized by climatic characteristics of hot, humid summers and cold, dry winters.…”

Section: Study Areamentioning

confidence: 99%

“…These three powerful bioclimatic variables can predict the forest habitat more effectively than general climate variables [13,14,24]. Since only the core variables are used, the problem of multicollinearity caused by similar variables can be prevented [39,40].…”

Section: Climate Datamentioning

confidence: 99%

Assessing Climate Change Impact on Forest Habitat Suitability and Diversity in the Korean Peninsula

Lim

Yoo

Choi

et al. 2018

Forests

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Habitat changes in temperate forests are more vulnerable to climate change than tropical or boreal forests. This study assessed forest habitat suitability and diversity to determine the impact of climate change on the Korean Peninsula. We used the MaxEnt (Maximum Entropy) species distribution model, three key climate indices, and two representative climate change scenarios, using short and long-term data. Two of the three key climate indices related to temperature were more capricious than the precipitation-related index in the future. In the baseline prediction, both statistical and qualitative validation using the actual vegetation map showed excellent results. Regarding forest habitat suitability, northward migration and substantial increase were definitely distinctive in warm temperate evergreen forest. On the other hand, subalpine forest areas decreased significantly due to climate change; the suitable area for Representative Concentration Pathways (RCP) 8.5 2070s decreased by more than half. With regard to forest habitat diversity, regions with high diversity declined due to climate change. In the RCP 8.5 scenario, areas where all three forest types are suitable no longer appeared; however, in the case of RCP 4.5 2050s, suitable areas for two forest types increased, which implies climate change is not only negative in terms of diversity. As this negative prediction of future change is discouraging, active mitigation and adaptation are required to prevent these changes. The sustainability of future ecosystems is still dependent on our efforts.

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Development on Crop Yield Forecasting Model for Major Vegetable Crops using Meteorological Information of Main Production Area

Cited by 9 publications

References 0 publications

STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Assessment of Agricultural Drought Considering the Hydrological Cycle and Crop Phenology in the Korean Peninsula

Assessing Climate Change Impact on Forest Habitat Suitability and Diversity in the Korean Peninsula

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