scholarly journals Characterization of soils conducive and non-conducive to Prunus replant disease

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260394
Author(s):  
Abdur R. Khan ◽  
Wisnu A. Wicaksono ◽  
Natalia J. Ott ◽  
Amisha T. Poret-Peterson ◽  
Greg T. Browne
Keyword(s):  
Random Forest ◽  
Growth And Yield ◽  
Random Forest Regression ◽  
Total N ◽  
Exchangeable K ◽  
Random Forest Classification ◽  
Anaerobic Soil Disinfestation ◽  
Replant Disease ◽  
Forest Classification

Successive orchard plantings of almond and other Prunus species exhibit reduced growth and yield in many California soils. This phenomenon, known as Prunus replant disease (PRD), can be prevented by preplant soil fumigation or anaerobic soil disinfestation, but its etiology is poorly understood and its incidence and severity are hard to predict. We report here on relationships among physicochemical variables, microbial community structure, and PRD induction in 25 diverse replant soils from California. In a greenhouse bioassay, soil was considered to be “PRD-inducing” when growth of peach seedlings in it was significantly increased by preplant fumigation and pasteurization, compared to an untreated control. PRD was induced in 18 of the 25 soils, and PRD severity correlated positively with soil exchangeable-K, pH, %clay, total %N, and electrical conductivity. The structure of bacterial, fungal, and oomycete communities differed significantly between the PRD-inducing and non-inducing soils, based on PERMANOVA of Bray Curtis dissimilarities. Bacterial class MB-A2-108 of phylum Actinobacteria had high relative abundances among PRD-inducing soils, while Bacteroidia were relatively abundant among non-inducing soils. Among fungi, many ASVs classified only to kingdom level were relatively abundant among PRD-inducing soils whereas ASVs of Trichoderma were relatively abundant among non-inducing soils. Random forest classification effectively discriminated between PRD-inducing and non-inducing soils, revealing many bacterial ASVs with high explanatory values. Random forest regression effectively accounted for PRD severity, with soil exchangeable-K and pH having high predictive value. Our work revealed several biotic and abiotic variables worthy of further examination in PRD etiology.

scholarly journals Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification

2016 ◽  
Vol 146 ◽  
pp. 370-385 ◽  
Author(s):  
Adam Hedberg-Buenz ◽  
Mark A. Christopher ◽  
Carly J. Lewis ◽  
Kimberly A. Fernandes ◽  
Laura M. Dutca ◽  
...  
Keyword(s):  
Random Forest ◽  
Ganglion Cell ◽  
Retinal Ganglion Cell ◽  
Quantitative Measurement ◽  
Cell Populations ◽  
Retinal Ganglion ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Estimation of Wheat Area using Sentinel-1 and Sentinel-2 Datasets (A Comparative Analysis)

2019 ◽  
Author(s):  
Ayesha Behzad ◽  
Muneeb Aamir ◽  
Syed Ahmed Raza ◽  
Ansab Qaiser ◽  
Syeda Yuman Fatima ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Random Forest ◽  
Water Body ◽  
Supervised Classification ◽  
Wheat Crop ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Wheat Cultivation ◽  
Erdas Imagine ◽  
Sentinel 2

Wheat is the basic staple food, largely grown, widely used and highly demanded. It is used in multiple food products which are served as fundamental constituent to human body. Various regional economies are partially or fully dependent upon wheat production. Estimation of wheat area is essential to predict its contribution in regional economy. This study presents a comparative analysis of optical and active imagery for estimation of area under wheat cultivation. Sentinel-1 data was downloaded in Ground Range Detection (GRD) format and applied the Random Forest Classification using Sentinel Application Platform (SNAP) tools. We obtained a Sentinel-2 image for the month of March and applied supervised classification in Erdas Imagine 14. The random forest classification results of Sentinel-1 show that the total area under investigation was 1089km2 which was further subdivided in three classes including wheat (551km2), built-up (450 km2) and the water body (89 km2). Supervised classification results of Sentinel-2 data show that the area under wheat crop was 510 km2, however the built-up and waterbody were 477 km2, 102 km2 respectively. The integrated map of Sentinel-1 and Sentinel-2 show that the area under wheat was 531 km2 and the other features including water body and the built-up area were 95 km2 and 463 km2 respectively. We applied a Kappa coefficient to Sentinel-2, Sentinel-1 and Integrated Maps and found an accuracy of 71%, 78% and 85% respectively. We found that remotely sensed algorithms of classifications are reliable for future predictions.

scholarly journals Penerapan Metode Random Over-Under Sampling dan Random Forest Untuk Klasifikasi Penilaian Kredit

2018 ◽  
Vol 5 (2) ◽  
pp. 175-185
Author(s):  
Akhmad Syukron ◽  
Agus Subekti
Keyword(s):  
Random Forest ◽  
Financial Institution ◽  
Credit Scoring ◽  
Classification Performance ◽  
Random Forest Classification ◽  
Random Forest Method ◽  
Forest Classification ◽  
Improve Accuracy ◽  
Under Sampling ◽  
Accuracy Performance

                                         AbstrakPenilaian kredit telah menjadi salah satu cara utama bagi sebuah lembaga keuangan untuk menilai resiko kredit,  meningkatkan arus kas, mengurangi kemungkinan resiko dan membuat keputusan manajerial. Salah satu permasalahan yang dihadapai pada penilaian kredit yaitu adanya ketidakseimbangan distribusi dataset. Metode untuk mengatasi ketidakseimbangan kelas yaitu dengan metode resampling, seperti menggunakan Oversampling, undersampling dan hibrida yaitu dengan menggabungkan kedua pendekatan sampling. Metode yang diusulkan pada penelitian ini adalah penerapan metode Random Over-Under Sampling Random Forest untuk meningkatkan kinerja akurasi klasifikasi penilaian kredit pada dataset German Credit.  Hasil pengujian menunjukan bahwa klasifikasi tanpa melalui proses resampling menghasilkan kinerja akurasi rata-rata 70 % pada semua classifier. Metode Random Forest memiliki nilai akurasi yang lebih baik dibandingkan dengan beberapa metode lainnya dengan nilai akurasi sebesar 0,76 atau 76%. Sedangkan klasifikasi dengan penerapan metode Random Over-under sampling Random Forest  dapat meningkatkan kinerja akurasi sebesar 14,1% dengan nilai akurasi sebesar 0,901 atau 90,1 %. Hasil penelitian menunjukan bahwa penerapan  resampling dengan metode Random Over-Under Sampling pada algoritma Random Forest dapat meningkatkan kinerja akurasi secara efektif pada klasifikasi  tidak seimbang untuk penilaian kredit pada dataset German Credit. Kata kunci: Penilaian Kredit, Random Forest, Klasifikasi, ketidakseimbangan kelas, Random Over-Under Sampling                                                  AbstractCredit scoring has become one of the main ways for a financial institution to assess credit risk, improve cash flow, reduce the possibility of risk and make managerial decisions. One of the problems faced by credit scoring is the imbalance in the distribution of datasets. The method to overcome class imbalances is the resampling method, such as using Oversampling, undersampling and hybrids by combining both sampling approaches. The method proposed in this study is the application of the Random Over-Under Sampling Random Forest method to improve the accuracy of the credit scoring classification performance on German Credit dataset. The test results show that the classification without going through the resampling process results in an average accuracy performance of 70% for all classifiers. The Random Forest method has a better accuracy value compared to some other methods with an accuracy value of 0.76 or 76%. While classification by applying the Random Over-under sampling + Random Forest method can improve accuracy performance 14.1% with an accuracy value of 0.901 or 90.1%. The results showed that the application of resampling using Random Over-Under Sampling method in the Random Forest algorithm can improve accuracy performance effectively on an unbalanced classification for credit scoring on German Credit dataset. Keywords: Imbalance Class, Credit Scoring, Random Forest, Classification, Resampling

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