Deep machine learning with Sentinel satellite data to map paddy rice production stages across West Java, Indonesia

2021 ◽  
Vol 265 ◽  
pp. 112679
Author(s):  
K.R. Thorp ◽  
D. Drajat
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Paddy Rice ◽  
Rice Production ◽  
West Java ◽  
Production Stages

scholarly journals Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning and Rice Production Tasks

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Rayner Alfred ◽  
Joe Henry Obit ◽  
Christie Chin Pei Yee ◽  
Haviluddin Haviluddin ◽  
Yuto Lim
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Paddy Rice ◽  
Rice Production ◽  
Smart Farming

scholarly journals A Scalable Machine Learning Pipeline for Paddy Rice Classification Using Multi-Temporal Sentinel Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1769
Author(s):  
Vasileios Sitokonstantinou ◽  
Alkiviadis Koukos ◽  
Thanassis Drivas ◽  
Charalampos Kontoes ◽  
Ioannis Papoutsis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
High Performance ◽  
Large Scale ◽  
Paddy Rice ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Rice Area ◽  
Multi Temporal

The demand for rice production in Asia is expected to increase by 70% in the next 30 years, which makes evident the need for a balanced productivity and effective food security management at a national and continental level. Consequently, the timely and accurate mapping of paddy rice extent and its productivity assessment is of utmost significance. In turn, this requires continuous area monitoring and large scale mapping, at the parcel level, through the processing of big satellite data of high spatial resolution. This work designs and implements a paddy rice mapping pipeline in South Korea that is based on a time-series of Sentinel-1 and Sentinel-2 data for the year of 2018. There are two challenges that we address; the first one is the ability of our model to manage big satellite data and scale for a nationwide application. The second one is the algorithm’s capacity to cope with scarce labeled data to train supervised machine learning algorithms. Specifically, we implement an approach that combines unsupervised and supervised learning. First, we generate pseudo-labels for rice classification from a single site (Seosan-Dangjin) by using a dynamic k-means clustering approach. The pseudo-labels are then used to train a Random Forest (RF) classifier that is fine-tuned to generalize in two other sites (Haenam and Cheorwon). The optimized model was then tested against 40 labeled plots, evenly distributed across the country. The paddy rice mapping pipeline is scalable as it has been deployed in a High Performance Data Analytics (HPDA) environment using distributed implementations for both k-means and RF classifiers. When tested across the country, our model provided an overall accuracy of 96.69% and a kappa coefficient 0.87. Even more, the accurate paddy rice area mapping was returned early in the year (late July), which is key for timely decision-making. Finally, the performance of the generalized paddy rice classification model, when applied in the sites of Haenam and Cheorwon, was compared to the performance of two equivalent models that were trained with locally sampled labels. The results were comparable and highlighted the success of the model’s generalization and its applicability to other regions.

scholarly journals Competitiveness effect of the UPSUS Program on rice production in West Java Province, Indonesia

2021 ◽  
Vol 653 (1) ◽  
pp. 012010
Author(s):  
A Setiyanto ◽  
I M Pabuayon ◽  
C B Quicoy ◽  
J V Camacho ◽  
D P T Depositario
Keyword(s):  
Rice Production ◽  
West Java

scholarly journals Remote sensing versus the area sampling frame method in paddy rice acreage estimation in Indramayu regency, West Java province, Indonesia

2020 ◽  
Vol 42 (5) ◽  
pp. 1738-1767
Author(s):  
Laju Gandharum ◽  
Mari E. Mulyani ◽  
Djoko M. Hartono ◽  
Asep Karsidi ◽  
Mubariq Ahmad
Keyword(s):  
Remote Sensing ◽  
Paddy Rice ◽  
Sampling Frame ◽  
West Java ◽  
Acreage Estimation ◽  
Area Sampling ◽  
Frame Method

scholarly journals Prediction of Maize Yield at the City Level in China Using Multi-Source Data

2021 ◽  
Vol 13 (1) ◽  
pp. 146
Author(s):  
Xinxin Chen ◽  
Lan Feng ◽  
Rui Yao ◽  
Xiaojun Wu ◽  
Jia Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Vegetation Index ◽  
Maize Yield ◽  
Machine Learning Algorithms ◽  
Shortwave Radiation ◽  
Growth Stages ◽  
Climate Data ◽  
Early Peak ◽  
The City

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

Machine Learning based Extraction of Electrical Substations from High Resolution Satellite Data: Outcome of the ICETCI 2021 Challenge

2021 ◽  
Author(s):  
Sreenivasan G ◽  
Anju Bajpai ◽  
Prakasa Rao D S ◽  
Girish Kumar T P ◽  
Ashish Shrivastava ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Data

Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery

2022 ◽  
Vol 305 ◽  
pp. 117834
Author(s):  
Alfredo Nespoli ◽  
Alessandro Niccolai ◽  
Emanuele Ogliari ◽  
Giovanni Perego ◽  
Elena Collino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Machine Learning Techniques ◽  
Solar Irradiation ◽  
Cloud Type ◽  
Learning Techniques ◽  
Type Classification

scholarly journals Response of a local rice variety Pandanwangi to infection of bacterial leaf blight Xanthomonas oryzae pv. oryzae strain III, IV, and VIII

2021 ◽  
Vol 905 (1) ◽  
pp. 012068
Author(s):  
Hadiwiyono ◽  
S H Poromarto ◽  
S Widono ◽  
R F Rizal
Keyword(s):  
Rice Variety ◽  
Leaf Blight ◽  
Rice Production ◽  
Xanthomonas Oryzae ◽  
Bacterial Leaf Blight ◽  
Artificial Inoculation ◽  
Limiting Factors ◽  
Bacterial Suspension ◽  
West Java ◽  
Local Cultivar

Abstract Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae (Xoo) is one of the limiting factors in rice production. A local cultivar, rice “Pandanwangi” is a superior variety much preferred and cultivated by the farmers in Cianjur, West Java, Indonesia. The information about the response of “Pandangwangi” to Xoo is still poorly understood. This paper reports the results of the evaluation of “Pandanwangi” response against BLB. This research was conducted in a greenhouse with artificial inoculation using Xoo strains III, IV, and VIII with bacterial suspension at 108 cfu.mL−1. The results showed that the response of cv Pandanwangi to Xoo infection was different from the strain of Xoo. “Pandanwangi” cultivar was susceptible to Xoo strain III and VIII and very susceptible to strain IV.

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