scholarly journals Application of hyperspectral imaging and chemometrics for variety classification of maize seeds

RSC Advances ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 1337-1345 ◽  
Author(s):  
Yiying Zhao ◽  
Susu Zhu ◽  
Chu Zhang ◽  
Xuping Feng ◽  
Lei Feng ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Crop Yield ◽  
Maize Seeds ◽  
Variety Classification

Hyperspectral imaging provides an effective way for seed variety classification for assessing variety purity and increasing crop yield.

Variety Classification of Coated Maize Seeds Based on Raman Hyperspectral Imaging

2021 ◽  
pp. 120772
Author(s):  
Qingyun Liu ◽  
Zuchao Wang ◽  
Yuan Long ◽  
Chi Zhang ◽  
Shuxiang Fan ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Maize Seeds ◽  
Variety Classification

scholarly journals Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4391 ◽  
Author(s):  
Aimin Miao ◽  
Jiajun Zhuang ◽  
Yu Tang ◽  
Yong He ◽  
Xuan Chu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Procrustes Analysis ◽  
Waxy Maize ◽  
Maize Seeds ◽  
Variety Classification ◽  
Low Dimensional

Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7–1016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher’s discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis.

<i>Variety classification of maize kernels using near infrared (NIR) hyperspectral imaging</i>

2017 ◽  
Author(s):  
Xin Zhao ◽  
Wei Wang ◽  
Xuan Chu ◽  
Hongzhe Jiang ◽  
Beibei Jia ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Variety Classification ◽  
Maize Kernels

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

Classification of fresh Atlantic salmon (Salmo salar L.) fillets stored under different atmospheres by hyperspectral imaging

2012 ◽  
Vol 109 (3) ◽  
pp. 482-489 ◽  
Author(s):  
Izumi Sone ◽  
Ragnar L. Olsen ◽  
Agnar H. Sivertsen ◽  
Guro Eilertsen ◽  
Karsten Heia
Keyword(s):  
Atlantic Salmon ◽  
Hyperspectral Imaging ◽  
Salmo Salar ◽  
Atlantic Salmon Salmo Salar

Seed Architectural Phenes Prediction and Variety Classification of Dry Beans (Phaseolus vulgaris) Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Christan Hail Mendigoria ◽  
Ronnie Concepcion ◽  
Elmer Dadios ◽  
Heinrick Aquino ◽  
Oliver John Alaias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Phaseolus Vulgaris ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Dry Beans ◽  
Variety Classification
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