Pisum sativum classification based on a methodolog (original) (raw)
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PloS one, 2016
Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to a...
Classification of 6 durum wheat cultivars from Sicily (Italy) using artificial neural networks
Chemometrics and Intelligent Laboratory Systems, 2008
The possibility of using two different artificial neural networks architectures (multi-layer feed-forward, MLF-NN, and counterpropagation, CP-NN) for the classification of 255 durum wheat samples from Sicily (Italy) was investigated and the performances of the optimal models were compared both among each others and to those resulting from the application of traditional chemometric pattern recognition techniques. When considering predictive ability over an independent test set, counterpropagation NN performed best, being able to correctly predict about 82% of the external validation samples (the corresponding predictive ability for MLF-NN, LDA and QDA was 72.0%, 50.9% and 52.7%, respectively.
Comparison of Neural Network and Multivariate Discriminant Analysis in Selecting New Cowpea Variety
2010
Adewole, Adetunji Philip * Department of Computer Science, University of Agriculture, Abeokuta philipwole@yahoo.com Sofoluwe, A. B. Department of Computer Science, University of Lagos, Akoka Agwuegbo , Samuel Obi-Nnamdi Department of Statistics, University of Agriculture, Abeokuta E-mail: agwuegbo_son@yahoo.com ABSTRACT In this study, neural networks (NN) algorithm and multivariate discriminant (MDA) based model were developed to classify ten (10) varieties of cowpea which were widely planted in Kano. . In order to demonstrate the validity of our model, we use the case study to build a neural network model using Multilayer Feedforward Neural Network, and compare its classification performance against the Multivariate discriminant analysis. Two groups of data (Spray and Nospray) were used. Twenty kernels were used as training data set and test data to classify cowpea seed varieties. The neural network classified the new cowpea seed varieties based on the information it is trained wit...
Identification of bean varieties according to color features using artificial neural network
Spanish Journal of Agricultural Research, 2013
A machine vision and a multilayer perceptron artificial neural network (MLP-ANN) were applied to identify bean varieties, based on color features. Ten varieties of beans, which were grown in Iran (Khomein1, KS21108, Khomein2, Sarab1, Khomein3, KS21409, Akhtar2, Sarab2, KS21205, and G11870) were collected. Six color features of the bean and six color features of the spots were extracted and used as input for MLP-ANN classifier. In this study, 1000 data sets were used, 70% for training, 15% for validating and 15% for testing. The results showed that the applied machine vision and neural network were able to classify bean varieties with 100% sensibility and specificity, except with Sarab1 with sensibilities of 100%, 73.3%, 60% for the training, validation and testing processes, respectively and KS21108 with specificities of 100%, 79% and 71%, respectively for the aforementioned processes. Considering total sensibilities of 100%, 97.33%, 96% and also specificities of 100%, 97.9% and 97.1% for training, validation and testing of beans, respectively, the ANN could be used as a effective tool for classification of bean varieties.
Analysis of Neural Network and Hybrid Techniques for Plants classification
2021
Today, computer science is increasingly involved in agricultural and food sciences. Various artificial intelligence and soft computing techniques are used to classify plants and detect defects to provide a better quality product to the final consumer. This article focuses on advances in automatic plant classification using soft computing techniques. Various ANN, CNN, PNN as well as Heuristic and meta heuristic optimization techniques are reviewed for plants classification. There are several meta-heuristic optimization algorithms developed on inspiration from nature. The review of Neural networks like ANN, CNN, PNN as well as some of the hybrid artificial neural networks with optimization methods like Genetic Algorithm (GA), Ant Bee Colony (ABC), Differential Evolution (DE), Group Search Particle Swarm Optimization (GSPSO), Firefly method, etc. are applied for benchmark data sets and to specific real-time experiments for plants classification are discussed.
Journal of the American Society for Horticultural Science. American Society for Horticultural Science
In Spain, water for agricultural use represents about 85% of the total water demand, and irrigated crop production constitutes a major contribution to the country's economy. Field studies were conducted to evaluate the potential of multispectral reflectance and seven vegetation indices in the visible and near-infrared spectral range for discriminating and classifying bare soil and several horticultural irrigated crops at different dates. This is the first step of a broader project with the overall goal of using satellite imagery with high spatial and multispectral resolutions for mapping irrigated crops to improve agricultural water use. On-ground reflectance data of bare soil and annual herbaceous crops [garlic (Allium sativum), onion (Allium cepa), sunflower (Helianthus annuus), bean (Vicia faba), maize (Zea mays), potato (Solanum tuberosum), winter wheat (Triticum aestivum), melon (Cucumis melo), watermelon (Citrillus lanatus), and cotton (Gossypium hirsutum)], perennial herb...
International Applied Science
Identification of long bean seed varieties can be used to save plant variety and intellectual property rights. Using digital image processing combined with artificial neural networks (ANN) has a possibility to recognize the seed morphology. The purpose of this research is to identify the image variables that can be used to identify long bean seed varieties so that the best algorithm of artificial neural networks can be arranged and the level of accuracy in expecting the long bean varieties. The samples used in this study were long bean seeds of parade tavi, kanton tavi, branjangan, and petiwi varieties. For each variety, 400 samples were taken for training data and 200 samples for testing data, so the total sample was 2400 long bean seeds. The research stages include image acquisition, image retrieval, image variable estimation, image processing program development, data analysis, ANN training, long bean variety identification program preparation, and program validation. The results...
Classification of Varieties of Grain Species by Artificial Neural Networks
Agronomy, 2018
In this study, an Artificial Neural Network (ANN) model was developed in order to classify varieties belonging to grain species. Varieties of bread wheat, durum wheat, barley, oat and triticale were utilized. 11 physical properties of grains were determined for these varieties as follows: thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters. It was found that these properties had been statistically significant for the varieties. An Artificial Neural Network was developed for classifying varieties. The structure of the ANN model developed was designed to have 11 inputs, 2 hidden and 2 output layers. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour were used as input parameters; and species and varieties as output parameters. While classifying the varieties by the ANN model developed, R2, RMSE and mean ...