Principal Component Analysis for Identification of Superior Castor Bean Hybrids

Piedade, G. N. da; Vieira, Lucas Vasconcelos; Santos, Amanda Rithieli Pereira dos; Amorim, Deoclécio Jardim; Zanotto, Maurício Dutra; Sartori, M. M. P.

doi:10.5539/jas.v11n9p179

Cited by 5 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Pada komponen utama ketiga (PC3) memiliki nilai eigenvalue 3,89 dengan keragaman sebesar 12,16% terkait karakter yang berkontribusi yaitu jumlah buah per tanaman, bobot biji per tanaman, dan jumlah per tanaman (Tabel.1). Penelitian terhadap keragaman pada jarak kepyar karakter yang berkontribusi juga didapatkan pada penelitian Piedade et al, (2019) yaitu jumlah biji per tanaman, jumlah buah per tanaman, panjang tandan utama, tinggi tanaman. Seluruh karakter kuantitatif dan kualitatif berkontribusi pada keragaman PC4 namun kontribusinya kecil sehingga nilai factor loading dari karakter tersebut kurang dari 0,6 begitu pula dengan PC, PC6, PC7, PC8, dan PC9.…”

Section: Hasil Dan Pembahasanunclassified

Karakter Morfologi Genotipe Jarak Kepyar (Ricinus communis L.) Tahan Penyakit Layu Fusarium

Prameswari¹,

Waluyo

2023

agrowiralodra

View full text Add to dashboard Cite

The castor bean (Ricinus communis L.) is a plant with a lot of potential as a vegetable oil producer. Castor bean productivity in Indonesia was 1.36 thousand tons in 2013, but it fell to 1.30 thousand tons in 2014. Fusarium wilt is one of the challenges in increasing castor bean production. Resistant genotypes can be used to control Fusarium wilt in an environmentally friendly manner. The goal of this study was to determine the variability and superior performance of Fusarium wilt-resistant castor beans. The study took place at the Faculty of Agriculture's greenhouse Experimental farm, Universitas Brawijaya in Lowokwaru District, Malang, East Java, from January to July 2021. Twenty two genotypes resistant to Fusarium wilt, urea fertilizer, NPK, and pesticides were used. Augmented design was used as the experimental field design. Following Pearson correlation type and coefficient of variation, variability was investigated using principal component analysis (PCA). Based on PCA, the variability of morphological characters of 22 genotypes of castor bean is divided into first-nine principal components with a cumulative variability of 85.91 percent. The coefficient of variation for the main raceme weight character is 61.15 percent. The TBN 0816-36 genotype succeeds in vegetative characters, while the CT4-10-3-(C864-3)-68 genotype performs best in yield.

show abstract

Section: Hasil Dan Pembahasanunclassified

Karakter Morfologi Genotipe Jarak Kepyar (Ricinus communis L.) Tahan Penyakit Layu Fusarium

Prameswari¹,

Waluyo

2023

agrowiralodra

View full text Add to dashboard Cite

show abstract

“…Handcrafted features include orthogonal subspace projections, Principal Component Analysis (PCA) [ 14 ], and Minimum Noise Fraction (MNF) [ 14 ]. The huge volumes of satellite imageries had to be processed increase the need to big data technologies to be incorporated in agriculture problems [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Investigation on the use of ensemble learning and big data in crop identification

Ahmed¹,

Mahmoud²,

Farg³

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…Examples of physical features include Normalized Difference Vegetative Index (NDVI) [13] and Leaf Area Index (LAI) [14]. On the other hand, Examples of handcrafted features includes orthogonal subspace projections, Principal Component Analysis (PCA) [15], and Minimum Noise Fraction (MNF) [15].…”

Section: Introductionmentioning

confidence: 99%

Investigation on the use of Ensemble Learning and Big Data in Crop Identification

Ahmed

Mahmoud

Farg

et al. 2022

Preprint

View full text Add to dashboard Cite

The agriculture sector in Egypt faces several problems, such as climate change, water storage, and yield variability. The comprehensive capabilities of Big Data (BD) can help in tackling the uncertainty of food supply occurs due to several factors such as soil erosion, water pollution, climate change, socio-cultural growth, governmental regulations, and market fluctuations. Crop identification and monitoring plays a vital role in precision agriculture. Although several machine learning models have been utilized in identifying crops, the performance of ensemble learning has not been investigated extensively. The massive volume of satellite imageries has been established as a big data problem forcing to deploy the proposed solution using big data technologies to manage, store, analyze, and visualize satellite data. In this paper, we have developed weighted voting mechanism for improving crop classification performance in a large scale, based on ensemble learning and big data schema. Based on Apache Spark, the popular DB Framework, the proposed approach was tested on El Salheya, Ismaili governate. Our findings confirm the generalization of the proposed crop identification approach at a large-scale setting.

show abstract

Principal Component Analysis for Identification of Superior Castor Bean Hybrids

Cited by 5 publications

References 18 publications

Karakter Morfologi Genotipe Jarak Kepyar (Ricinus communis L.) Tahan Penyakit Layu Fusarium

Karakter Morfologi Genotipe Jarak Kepyar (Ricinus communis L.) Tahan Penyakit Layu Fusarium

Investigation on the use of ensemble learning and big data in crop identification

Investigation on the use of Ensemble Learning and Big Data in Crop Identification

Contact Info

Product

Resources

About