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Stabilizing Predictive Performance for
Ear Emergence in Rice Crops
across Cropping Regions
Yasuhiro Iuchi1, Hiroshi Uehara1, Yusuke Fukazawa2, and Yoshihiro Kaneta1
1Akita Prefectural University, Akita, Japan
PKAW2020
2Tokyo University, Tokyo, Japan
Jan. 7, 2021
2
Background
Rapid decline of farmers population
Loss of experience and implicit knowledge
Knowledge acquisition from agricultural data is expected to
be alternatives to human implicit knowledge
Predicting ear emergence is crucial for good harvesting quality
Ear Emergence
Ear emergence ≅ flowering
Conventional farming is
depending on farmers’ experiences
Goal: Predicting ear emergence of rice
crops based on machine learning
3
Planting Harvesting
Growing stages of rice crop
Related studies and the issue
Objective Variable Explanatory Variables
Date of ear emergence
Temperature, Rain falls,
Elevation, Soil types,…
= 𝑓
✔Good predictive performances have been acquired in related studies.
Machine Learning,
Statistical models
( )
Readily accessible regional variables seem to be exhausted!
✘Related studies have not attained predictive stabilities in
terms of regional variances.
Regional features
4
Proposal: Implicit regional variables
based on Hidden Markov Model
Date of ear emergence
= ( )
Proposed prediction
𝑓
Date of ear emergence
= ( )
𝑓
Related studies
Implicit regional
variables by HMM
5
Temperature, Rain falls,
Elevation, Soil types,…
Temperature, Rain falls,
Elevation, Soil types,…
Readily accessible variables
XGBoost
Time series that have averaged the
temperature for the 25 years
6
Pre-examination:
Implication from time series of micro-climate data
Distinctive patterns of time series fluctuations are found
Time series dependencies are implied
Pattern of sharp rise
Pattern of gentle sloping
Basin area
Seaside area
≅
≅
20
25
Basin
7
Acquiring two parameter sets
of transition pattern
Learning implicit regional states based
on the Baum-welch algorithm
20
22
Seaside
Time
≈
Time
≈
𝑝(𝑍𝑡, 𝑏𝑎𝑠𝑖𝑛|𝑍𝑡−1, 𝑏𝑎𝑠𝑖𝑛,𝐴𝑏𝑎𝑠𝑖𝑛)
𝐵𝑎𝑠𝑖𝑛 𝑝𝑎𝑟𝑎𝑚: { ҧ
𝐴𝑏𝑎𝑠𝑖𝑛,
ഥ
∅𝑏𝑎𝑠𝑖𝑛}
𝑆𝑒𝑎 𝑠𝑖𝑑𝑒 𝑝𝑎𝑟𝑎𝑚: { ҧ
𝐴𝑠𝑒𝑎𝑠𝑖𝑑𝑒,
ഥ
∅𝑠𝑒𝑎𝑠𝑖𝑑𝑒}
𝐴: 𝑇𝑟𝑎𝑛𝑠𝑖𝑡𝑖𝑜𝑛 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 ℎ𝑖𝑑𝑑𝑒𝑛 𝑠𝑡𝑎𝑡𝑒
∅: 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦
𝑝(𝑍𝑡, 𝑠𝑒𝑎
𝑠𝑖𝑑𝑒
|𝑍𝑡−1,𝑠𝑒𝑎
𝑠𝑖𝑑𝑒
, 𝐴𝑠𝑒𝑎
𝑠𝑖𝑑𝑒
)
𝑇𝑟𝑎𝑖𝑛𝑡, 𝐵𝑎𝑠𝑖𝑛 𝑇𝑟𝑎𝑖𝑛𝑡, 𝑆𝑒𝑠𝑠𝑖𝑑𝑒
𝑡
𝑡 𝑡
Temperature
: 𝐻𝑖𝑑𝑑𝑒𝑛 𝑠𝑡𝑎𝑡𝑒𝑠 𝑜𝑓 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒
𝑝(𝑇𝑟𝑎𝑖𝑛𝑡, 𝑏𝑎𝑠𝑖𝑛|𝑍𝑡,𝑏𝑎𝑠𝑖𝑛, ∅𝐵𝑎𝑠𝑖𝑛)
𝑍𝑡
𝑝(𝑇𝑟𝑎𝑖𝑛𝑡, 𝑠𝑒𝑎
𝑠𝑖𝑑𝑒
|𝑍𝑡,𝑠𝑒𝑎
𝑠𝑖𝑑𝑒
, ∅𝑠𝑒𝑎
𝑠𝑖𝑑𝑒
)
(hidden states)
Temperature
(hidden states)
𝑇𝑡,𝑥 : 𝑇𝑟𝑎𝑖𝑛 𝑑𝑎𝑡𝑎 𝑜𝑓 𝑏𝑎𝑠𝑖𝑛 𝑜𝑟 𝑠𝑒𝑎𝑠𝑖𝑑𝑒
1
- 𝑡 1
-
Deriving implicit regional variable
8
The temperature series data for all regions
Basin Seaside
Viterbi
likelihood 30 12
Larger likelihood is adopted as
an implicit regional variable
Basin Seaside
Is the data likely to be basin
or seaside ?
𝑉𝑖𝑡𝑒𝑟𝑏𝑖 𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑
= 𝐿( ҧ
𝐴𝑏𝑎𝑠𝑖𝑛, ഥ
∅𝑏𝑎𝑠𝑖𝑛, 𝑍𝑏𝑎𝑠𝑖𝑛|𝐴𝑙𝑙 𝑑𝑎𝑡𝑎)
𝑉𝑖𝑡𝑒𝑟𝑏𝑖 𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑
= 𝐿 ҧ
𝐴𝑠𝑒𝑎
𝑠𝑖𝑑𝑒
, ഥ
∅𝑠𝑒𝑎
𝑠𝑖𝑑𝑒
, 𝑍𝑠𝑒𝑎
𝑠𝑖𝑑𝑒
𝐴𝑙𝑙 𝑑𝑎𝑡𝑎
>
Dataset
Date of ear emergence = ( )
𝑓 Implicit regional
variables by HMM
9
Temperature, Rain falls,
115 diversified observation points across
Akita prefecture
Micro-climate:
・From The National Agriculture and Food Research
Organization(NARO)
・The temperature, rainfall for each 1𝑘𝑚2
meshed area
Elevation, Soil types, …
Cropping records:
・Accumulated over 25 years (1,485 records)
(Akita institute of agriculture)
XGBoost
111km
182km
Baseline
Proposed
Basin
Seaside
Evaluation method
𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 =
1
40
෍
𝑘=1
40
𝑟𝑘 − ҧ
𝑟 2
𝑅𝑀𝑆𝐸(𝑟𝑘) =
1
𝑁𝑘
෍
𝑛𝑘=1
𝑁𝑘
𝑦𝑛𝑘
− ො
𝑦𝑛𝑘
2
RMSEs by each region Variance of RMSEs by region
Datasets are split into 40 small regions to
verify the predictive stability regardless of
regional characteristics
10
𝑁𝑘: The number of testing data(𝑛𝑘) in the 𝑘th region
ҧ
𝑟: Average of the regional RMSE
Unit of small region
(10 𝑘𝑚2)
ҧ
𝑟 =
1
40
෍
𝑘=1
40
𝑟𝑘
𝑦𝑘: Acutual date of the ear emergence
ො
𝑦𝑘: Predicted data of the ear emergence
Result
✔ Variance of regional RMSEs is improved.
Regional RMSEs
11
Explanatory variables Importance
Planting date 80
Likelihood of
temperature
58
Temperature June 20 51
Temperature June 15 37
Temperature June 5 29
Feature importance of Region 1
Engineered variables tend to be
ranked higher!
✔ Overall RMSE is improved.
Baseline Proposed
Variance of regional RMSE 0.83 0.49
Overall RMSE 3.38 2.94
Region Baseline Proposed
Region 1
(Northern Akita city)
4.36 2.61
Region 2(Akita city) 4.05 2.99
Region 3(Yokote city) 3.39 1.86
Conclusion:
✔️ improving the variance of regional RMSE
✔️ improving overall RMSE
12
Implicit engineered variables by HMM were found to
be effective in terms of
Future work:
✔️ the other prefectures
✔️ the different types of rice crops
Expanding the data for
Overview of
Hidden Markov model
13
Baum welch
algorithm
Viterbi
algorithm
Dataset of basin
Dataset of seaside
Basin param set
Seaside param set
All time series dataset
Basin likelihood
Seaside likelihood
HMM
Additional

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Stabilizing Predictive Performance for Ear Emergence in Rice Crops across Cropping Regions

  • 1. Stabilizing Predictive Performance for Ear Emergence in Rice Crops across Cropping Regions Yasuhiro Iuchi1, Hiroshi Uehara1, Yusuke Fukazawa2, and Yoshihiro Kaneta1 1Akita Prefectural University, Akita, Japan PKAW2020 2Tokyo University, Tokyo, Japan Jan. 7, 2021
  • 2. 2 Background Rapid decline of farmers population Loss of experience and implicit knowledge Knowledge acquisition from agricultural data is expected to be alternatives to human implicit knowledge
  • 3. Predicting ear emergence is crucial for good harvesting quality Ear Emergence Ear emergence ≅ flowering Conventional farming is depending on farmers’ experiences Goal: Predicting ear emergence of rice crops based on machine learning 3 Planting Harvesting Growing stages of rice crop
  • 4. Related studies and the issue Objective Variable Explanatory Variables Date of ear emergence Temperature, Rain falls, Elevation, Soil types,… = 𝑓 ✔Good predictive performances have been acquired in related studies. Machine Learning, Statistical models ( ) Readily accessible regional variables seem to be exhausted! ✘Related studies have not attained predictive stabilities in terms of regional variances. Regional features 4
  • 5. Proposal: Implicit regional variables based on Hidden Markov Model Date of ear emergence = ( ) Proposed prediction 𝑓 Date of ear emergence = ( ) 𝑓 Related studies Implicit regional variables by HMM 5 Temperature, Rain falls, Elevation, Soil types,… Temperature, Rain falls, Elevation, Soil types,… Readily accessible variables XGBoost
  • 6. Time series that have averaged the temperature for the 25 years 6 Pre-examination: Implication from time series of micro-climate data Distinctive patterns of time series fluctuations are found Time series dependencies are implied Pattern of sharp rise Pattern of gentle sloping Basin area Seaside area ≅ ≅
  • 7. 20 25 Basin 7 Acquiring two parameter sets of transition pattern Learning implicit regional states based on the Baum-welch algorithm 20 22 Seaside Time ≈ Time ≈ 𝑝(𝑍𝑡, 𝑏𝑎𝑠𝑖𝑛|𝑍𝑡−1, 𝑏𝑎𝑠𝑖𝑛,𝐴𝑏𝑎𝑠𝑖𝑛) 𝐵𝑎𝑠𝑖𝑛 𝑝𝑎𝑟𝑎𝑚: { ҧ 𝐴𝑏𝑎𝑠𝑖𝑛, ഥ ∅𝑏𝑎𝑠𝑖𝑛} 𝑆𝑒𝑎 𝑠𝑖𝑑𝑒 𝑝𝑎𝑟𝑎𝑚: { ҧ 𝐴𝑠𝑒𝑎𝑠𝑖𝑑𝑒, ഥ ∅𝑠𝑒𝑎𝑠𝑖𝑑𝑒} 𝐴: 𝑇𝑟𝑎𝑛𝑠𝑖𝑡𝑖𝑜𝑛 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 ℎ𝑖𝑑𝑑𝑒𝑛 𝑠𝑡𝑎𝑡𝑒 ∅: 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑝(𝑍𝑡, 𝑠𝑒𝑎 𝑠𝑖𝑑𝑒 |𝑍𝑡−1,𝑠𝑒𝑎 𝑠𝑖𝑑𝑒 , 𝐴𝑠𝑒𝑎 𝑠𝑖𝑑𝑒 ) 𝑇𝑟𝑎𝑖𝑛𝑡, 𝐵𝑎𝑠𝑖𝑛 𝑇𝑟𝑎𝑖𝑛𝑡, 𝑆𝑒𝑠𝑠𝑖𝑑𝑒 𝑡 𝑡 𝑡 Temperature : 𝐻𝑖𝑑𝑑𝑒𝑛 𝑠𝑡𝑎𝑡𝑒𝑠 𝑜𝑓 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑝(𝑇𝑟𝑎𝑖𝑛𝑡, 𝑏𝑎𝑠𝑖𝑛|𝑍𝑡,𝑏𝑎𝑠𝑖𝑛, ∅𝐵𝑎𝑠𝑖𝑛) 𝑍𝑡 𝑝(𝑇𝑟𝑎𝑖𝑛𝑡, 𝑠𝑒𝑎 𝑠𝑖𝑑𝑒 |𝑍𝑡,𝑠𝑒𝑎 𝑠𝑖𝑑𝑒 , ∅𝑠𝑒𝑎 𝑠𝑖𝑑𝑒 ) (hidden states) Temperature (hidden states) 𝑇𝑡,𝑥 : 𝑇𝑟𝑎𝑖𝑛 𝑑𝑎𝑡𝑎 𝑜𝑓 𝑏𝑎𝑠𝑖𝑛 𝑜𝑟 𝑠𝑒𝑎𝑠𝑖𝑑𝑒 1 - 𝑡 1 -
  • 8. Deriving implicit regional variable 8 The temperature series data for all regions Basin Seaside Viterbi likelihood 30 12 Larger likelihood is adopted as an implicit regional variable Basin Seaside Is the data likely to be basin or seaside ? 𝑉𝑖𝑡𝑒𝑟𝑏𝑖 𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 = 𝐿( ҧ 𝐴𝑏𝑎𝑠𝑖𝑛, ഥ ∅𝑏𝑎𝑠𝑖𝑛, 𝑍𝑏𝑎𝑠𝑖𝑛|𝐴𝑙𝑙 𝑑𝑎𝑡𝑎) 𝑉𝑖𝑡𝑒𝑟𝑏𝑖 𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 = 𝐿 ҧ 𝐴𝑠𝑒𝑎 𝑠𝑖𝑑𝑒 , ഥ ∅𝑠𝑒𝑎 𝑠𝑖𝑑𝑒 , 𝑍𝑠𝑒𝑎 𝑠𝑖𝑑𝑒 𝐴𝑙𝑙 𝑑𝑎𝑡𝑎 >
  • 9. Dataset Date of ear emergence = ( ) 𝑓 Implicit regional variables by HMM 9 Temperature, Rain falls, 115 diversified observation points across Akita prefecture Micro-climate: ・From The National Agriculture and Food Research Organization(NARO) ・The temperature, rainfall for each 1𝑘𝑚2 meshed area Elevation, Soil types, … Cropping records: ・Accumulated over 25 years (1,485 records) (Akita institute of agriculture) XGBoost 111km 182km Baseline Proposed Basin Seaside
  • 10. Evaluation method 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 = 1 40 ෍ 𝑘=1 40 𝑟𝑘 − ҧ 𝑟 2 𝑅𝑀𝑆𝐸(𝑟𝑘) = 1 𝑁𝑘 ෍ 𝑛𝑘=1 𝑁𝑘 𝑦𝑛𝑘 − ො 𝑦𝑛𝑘 2 RMSEs by each region Variance of RMSEs by region Datasets are split into 40 small regions to verify the predictive stability regardless of regional characteristics 10 𝑁𝑘: The number of testing data(𝑛𝑘) in the 𝑘th region ҧ 𝑟: Average of the regional RMSE Unit of small region (10 𝑘𝑚2) ҧ 𝑟 = 1 40 ෍ 𝑘=1 40 𝑟𝑘 𝑦𝑘: Acutual date of the ear emergence ො 𝑦𝑘: Predicted data of the ear emergence
  • 11. Result ✔ Variance of regional RMSEs is improved. Regional RMSEs 11 Explanatory variables Importance Planting date 80 Likelihood of temperature 58 Temperature June 20 51 Temperature June 15 37 Temperature June 5 29 Feature importance of Region 1 Engineered variables tend to be ranked higher! ✔ Overall RMSE is improved. Baseline Proposed Variance of regional RMSE 0.83 0.49 Overall RMSE 3.38 2.94 Region Baseline Proposed Region 1 (Northern Akita city) 4.36 2.61 Region 2(Akita city) 4.05 2.99 Region 3(Yokote city) 3.39 1.86
  • 12. Conclusion: ✔️ improving the variance of regional RMSE ✔️ improving overall RMSE 12 Implicit engineered variables by HMM were found to be effective in terms of Future work: ✔️ the other prefectures ✔️ the different types of rice crops Expanding the data for
  • 13. Overview of Hidden Markov model 13 Baum welch algorithm Viterbi algorithm Dataset of basin Dataset of seaside Basin param set Seaside param set All time series dataset Basin likelihood Seaside likelihood HMM Additional