Identification of rice seed varieties using neural network.
A digital image analysis algorithm based color and morphological features was developed to identify the six varieties (ey7954, syz3, xs11, xy5968, xy9308, z903) rice seeds which are widely planted in Zhejiang Province. Seven color and fourteen morphological features were used for discriminant analysis. Two hundred and forty kernels used as the training data set and sixty kernels as the test data set in the neural network used to identify rice seed varieties. When the model was tested on the test data set, the identification accuracies were 90.00%, 88.00%, 95.00%, 82.00%, 74.00%, 80.00% for ey7954, syz3, xs11, xy5968, xy9308, z903 respectively.
Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network
The feasibility of using hyperspectral imaging with convolutional neural network (CNN) to identify rice seed varieties was studied. Hyperspectral images of 4 rice seed varieties at two different spectral ranges (380–1030 nm and 874–1734 nm) were acquired. The spectral data at the ranges of 441–948 nm (Spectral range 1) and 975–1646 nm (Spectral range 2) were extracted. K nearest neighbors (KNN), support vector machine (SVM) and CNN models were built using different number of training samples (100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 and 3000). KNN, SVM and CNN models in the Spectral range 2 performed slightly better than those in the Spectral range 1. The model performances improved with the increase in the number of training samples. The improvements were not significant when the number of training samples was large. CNN model performed better than the corresponding KNN and SVM models in most cases, which indicated the effectiveness of using CNN to analyze spectral data. The results of this study showed that CNN could be adopted in spectral data analysis with promising results. More varieties of rice need to be studied in future research to extend the use of CNNs in spectral data analysis.
neural network sensor network wireless sensor network wireless sensor deep learning comparative study base station information retrieval feature extraction sensor node programming language cellular network random field digital video number theory rate control network lifetime river basin hyperspectral imaging distributed algorithm chemical reaction carnegie mellon university fly ash visual feature boundary detection video retrieval diabetes mellitu semantic indexing oryza sativa water storage user association efficient wireles shot boundary shot boundary detection data assimilation system retrieval task controlled trial terrestrial television video search gps network sensor network consist efficient wireless sensor information retrieval task concept detection video captioning retrieval evaluation rice seed safety equipment endangered species station operation case study involving dublin city university high-level feature seed germination brown coal high plain study involving structure recognition climate experiment gravity recovery table structure land data assimilation instance search combinatorial number randomised controlled trial recovery and climate randomised controlled combinatorial number theory adult male high-level feature extraction complete proof music perception robust computation optimization-based method perception and cognition global land datum social perception terrestrial water storage trec video retrieval terrestrial water object-oriented conceptual video retrieval evaluation trec video seed variety base station operation table structure recognition transgenic rice concept detector total water storage groundwater storage regional gp grace gravity randomized distributed algorithm ibm tivoli workload scheduler cerebrovascular accident case study united state