Identification of Canola Seeds through Computer Vision Image Processing

The objectives of this research are to present the automatic organization of agricultural seeds with the explosion of digital information through compute image vision processing. In this research paper CVIP (computer vision image processing) tool has been applied on different varieties and categorized of canola seeds. We had the 4 different varieties of canola seeds which were named as Gobhi Sarson (A), Barassica comp (B), Sathri (C) and Rocket Herbof (D). Each variety had 10 images and total 10*4 =40 images of canola seeds. We took the train data results of all kinds of canola seeds. After that the train data results were compared for pattern classification and apply the nearest neighbor and k-nearest neighbor algorithms for final classification. We achieved in nearest neighbor 85% and 76% average and k-nearest neighbor 90% and 73% average as a final pattern classification results. These are the best percentage for classification and provide more accuracy. The formers can select best healthy seed variety with the help of the results of this research. Keywords:  Features, Pattern classification, nearest neighbor, k-nearest neighbo

Identification of Canola Seeds using Nearest Neighbor and K-Nearest Neighbor Algorithms

Agriculture plays an important role on Pakistan economy. Canola is the major crop of Pakistan. There are different varieties of canola crop. It fulfills the requirement of oil. It is the difficult task to identify best canola seeds for sowing due to different varieties of canola seeds. In this paper we are try to introduce different machine learning approaches for classification of different canola seeds which provide opportunity to people or farmer to identify different canola seeds. Canola seeds verities implementing by the computer vision image processing tool. We have the 4 different varieties which names as Gobhi Sarson (A), Barassica comp (B), Sathri (C) and Rocket Herbof (D) canola seeds and take the images of canola seeds from these different varieties. Each variety has 10 images and we have total 10*4 =40 images of canola seeds. we take the train and test data results of all kinds of canola seeds. then train and test data results will be compare for pattern classification and apply the nearest neighbor and k-nearest neighbor algorithms for final classification in computer image processing tool. We achieved in nearest neighbor 85% and 76% average and k-nearest neighbor 90% and 73% average as a final pattern classification results. These are the best percentage for classification and provide more accuracy. These are important for farmer and other people for identify the different canola seeds. Keywords:  Features, Pattern classification, nearest neighbor, k-nearest neighbo

Non-destructive classification and quality evaluation of proso millet cultivars using NIR hyperspectral imaging with machine learning

Millet is a small-seeded cereal crop with big potential and remarkable characteristics such as high drought resistance, short growing time, low water footprint, and the ability to grow in acidic soil. There is a need to develop nondestructive methods for differentiation and evaluation of the quality attributes of different of proso millet cultivars grown in the U.S. Current methods of cultivar classification are either subjective or destructive, time consuming, not allowing for the whole population to be tested, and requiring trained operators and special equipment. In this study, the feasibility of using near-infrared (NIR) hyperspectral imaging (900-1700 nm) to predict the quality attributes of proso millet (Panicum miliaceum L.) seeds as well to classify its different cultivars was demonstrated. Ten different cultivars of proso millet variety, which are the most popular in the US, investigated in this study included Cerise, Cope, Earlybird, Huntsman, Minco, Plateau, Rise, Snowbird, Sunrise, and Sunup. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied, and the first two principal components were used as imaging features for building the classification models. The Classification performance showed a test accuracy rates as high as 99% for classifying the different cultivars of proso millet using gradient tree boosting ensemble machine learning algorithm. Moreover, using the partial least squares regression (PLSR) the coefficient of determination (R2) for quality prediction of proso millet seeds were 0.87, 0.80, 0.83, 0.93, and 0.92 for moisture content, crude protein, crude fat, ash, and carbohydrate, respectively. The overall results indicate that NIR hyperspectral imaging could be used to non-destructively classify and predict the quality of proso millet seeds

A new hybridized dimensionality reduction approach using genetic algorithm and folded linear discriminant analysis applied to hyperspectral imaging for effective rice seed classification

Hyperspectral imaging (HSI) has been reported to produce promising results in the classification of rice seeds. However, HSI data often require the use of dimensionality reduction techniques for the removal of redundant data. Folded linear discriminant analysis (F-LDA) is an extension of linear discriminant analysis (LDA, a commonly used technique for dimensionality reduction), and was recently proposed to address the limitations of LDA, particularly its poor performance when dealing with a small number of training samples which is a usual scenario in HSI applications. This article presents an improved version of F-LDA, exploring the feasibility of hybridizing a genetic algorithm (GA) and F-LDA for effective dimensionality reduction in HSI-based rice seeds classification. The proposed approach, inspired by the previous combination of GA with principle component analysis, is evaluated on rice seed datasets containing 256 spectral bands. Experimental results show that, in addition to attaining promising classification accuracies of up to 96.21%, this novel combination of GA and F-LDA (GA + F-LDA) can further reduce the computational complexity and memory requirement in the standalone F-LDA. It is worth noting that these benefits are not without a slight reduction in classification accuracy when evaluated against those reported for the standard F-LDA (up to 96.99%)

A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

Hyperspectral imaging (HSI) has been reported to produce promising results in the classification of rice seeds. However, HSI data often require the use of dimensionality reduction techniques for the removal of redundant data. Folded linear discriminant analysis (F-LDA) is an extension of linear discriminant analysis (LDA, a commonly used technique for dimensionality reduction), and was recently proposed to address the limitations of LDA, particularly its poor performance when dealing with a small number of training samples which is a usual scenario in HSI applications. This article presents an improved version of F-LDA, exploring the feasibility of hybridizing a genetic algorithm (GA) and F-LDA for effective dimensionality reduction in HSI-based rice seeds classification. The proposed approach, inspired by the previous combination of GA with principle component analysis, is evaluated on rice seed datasets containing 256 spectral bands. Experimental results show that, in addition to attaining promising classification accuracies of up to 96.21%, this novel combination of GA and F-LDA (GA + F-LDA) can further reduce the computational complexity and memory requirement in the standalone F-LDA. It is worth noting that these benefits are not without a slight reduction in classification accuracy when evaluated against those reported for the standard F-LDA (up to 96.99%)

Comparative study on vision based rice seed varieties identification

Abstract-This paper presents a system for automated classification of rice variety for rice seed production using computer vision and image processing techniques. Rice seeds of different varieties are visually very similar in color, shape and texture that make the classification of rice seed varieties at high accuracy challenging. We investigated various feature extraction techniques for efficient rice seed image representation. We analyzed the performance of powerful classifiers on the extracted features for finding the robust one. Images of six different rice seed varieties in northern Vietnam were acquired and analyzed. Our experiments have demonstrated that the average accuracy of our classification system can reach 90.54% using Random Forest method with a simple feature extraction technique. This result can be used for developing a computer-aided machine vision system for automated assessment of rice seeds purity

Classifying Wheat Genotypes using Machine Learning Models for Single Kernel Characterization System Measurements

The properties related to market value, milling, classification, storage, and transportation of bread wheat are determined by using some important physical quality characteristics such as weight, shape, dimensions, and hardness of wheat kernels. It is possible to measure all these features using single kernel characterization system (SKCS). Classification of wheat genotypes using computer-based algorithms is crucial to determine the most accurate physical quality classification for breeding studies. In this paper, four commercial wheat cultivars (Altay-2000, Bezostaja-1, Harmankaya-99, and Kate A-1) and six doubled haploid (DH) wheat genotypes are studied to classify wheat cultivars and DH wheat genotypes separately. In the classification stage, feature vectors constructed from measured characters namely, kernel weight, diameter, hardness, and moisture are applied to well-known classifiers such as Common Vector Approach (CVA), Support Vector Machines (SVM) and K-Nearest Neighbor (KNN). Satisfactory results especially for the training set are obtained from the experimental studies. Classification results are compared with single linkage hierarchical cluster (SLHC) analysis, which is the most widely used in breeding studies. Recognition of clustered genotypes in all three classification methods and dendrograms present similar results. The SVM model is found to be outperformed over other methods for studied characters and could therefore effectively be utilized for characterizing, classifying and/or identifying the wheat genotypes

Classifying Wheat Genotypes using Machine Learning Models for Single Kernel Characterization System Measurements

985-991The properties related to market value, milling, classification, storage, and transportation of bread wheat are determined by using some important physical quality characteristics such as weight, shape, dimensions, and hardness of wheat kernels. It is possible to measure all these features using single kernel characterization system (SKCS). Classification of wheat genotypes using computer-based algorithms is crucial to determine the most accurate physical quality classification for breeding studies. In this paper, four commercial wheat cultivars (Altay-2000, Bezostaja-1, Harmankaya-99, and Kate A-1) and six doubled haploid (DH) wheat genotypes are studied to classify wheat cultivars and DH wheat genotypes separately. In the classification stage, feature vectors constructed from measured characters namely, kernel weight, diameter, hardness, and moisture are applied to well-known classifiers such as Common Vector Approach (CVA), Support Vector Machines (SVM) and K-Nearest Neighbor (KNN). Satisfactory results especially for the training set are obtained from the experimental studies. Classification results are compared with single linkage hierarchical cluster (SLHC) analysis, which is the most widely used in breeding studies. Recognition of clustered genotypes in all three classification methods and dendrograms present similar results. The SVM model is found to be outperformed over other methods for studied characters and could therefore effectively be utilized for characterizing, classifying and/or identifying the wheat genotypes