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Combining machine learning,space-time cloud restoration and phenology for farm-level wheat yield prediction

Artificial Intelligence in Agriculture 2021年第1期5卷 208-222页

作者： andualem aklilu tesfaye Daniel Osgood Berhane Gessesse Aweke Department of Remote sensing at Ethiopian Space Science and Technology Institute Addis Ababa UniversityEthiopia International Research Institute for Climate and Society Earth InstituteColumbia UniversityUnited States of America Department head of the remote sensing unit at Ethiopian Space Science and Technology Institute Addis Ababa UniversityEthiopia

Though studies showed the potential of high-resolution optical sensors for crop yield prediction,several factors have limited their wider *** main factors are obstruction of cloud,identification of phenology,demand for high computing infrastructure and the complexity of statistical *** this research,we created a novel approach by combining four ***,we implemented the cloud restoration algorithm called gapfill to restore missed Normalized Difference Vegetation Index(NDVI)values derived from Sentinel-2 sensor(S2)due to cloud ***,we created square tiles as a solution for high computing infrastructure demand due to the use of high-resolution ***,we implemented gapfill following critical crop phenology ***,observations from restored images combined with original(from cloud-free images)values and applied for winter wheat *** applied seven base machine learning as well as two groups of super learning *** study successfully applied gapfill on high-resolution image to get good quality estimates for cloudy ***,yield prediction accuracy increased due to the incorporation of restored values in the regression *** models such as Generalized Linear Regression(GLM)and Random Forest(RF)showed improved capacity compared to other base and ensemble *** two models revealed RMSE of 0.001 t/ha and 0.136 t/ha on the holdout *** twomodels also revealed consistent and better performance using scatter plot analysis across three *** approach developed is useful to predict wheat yield at field scale,which is a rarely available but vital in many developmental projects,using optical sensors.

关键词： Cloud restoration Ensemble learning Machine learning Phenology Sentinel-2 Wheat yield prediction

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Genotype-by-Environment Interaction and Yield Stability Analysis in Finger Millet (<i>Elucine coracana</i>L. Gaertn) in Ethiopia

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American Journal of Plant Sciences 2011年第3期2卷 408-415页

作者： Asfaw Adugna tesfaye Tesso Erenso Degu Taye Tadesse Feyera Merga Wasihun Legesse Alemu Tirfessa Haileselassie Kidane andualem Wole Chemeda Daba 不详

Finger millet is one of the most neglected and underutilized crops worldwide, yet an important food cereal for millions of poor farmers in Africa. An experiment was carried out to determine adaptation range of diverse set of finger millet accessions and identify superior types with excellent yield potential for use as cultivar or as germplasm source for future breeding endeavors. A total of 44 indigenous accessions selected in previous evaluations and two check varieties were tested in two sets (mixed and colored) each containing 22 entries in a total of 11 environments between 2004 and 2008 seasons. Data were collected on grain yield, days to flowering, and plant height. The result showed that 2.5%, 79.1% and 18.3% of the total sum of squares in the mixed set and 2.1%, 86.9% and 11.0% in the colored set was attributed to genotype, environment, and genotype × environment interaction (GEI) effects, respectively. Furthermore, 54.6% and 46.19% of the GEI sum of squares in the mixed and in the colored set, respectively, were contributed by the first two interaction principal component axes (IPCA1 and IPCA2). A white seed accession (Acc. 203572) from the mixed set and three other accessions (Acc. 229469, Acc. 203410 and Acc. 203539) from the colored set were most stable and also had above average mean grain yield across environment and thus are recommended for release as cultivars to improve finger millet production in these environments.

关键词： AMMI Finger Millet Genotype Environment Stability

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