Rice seed classification using machine learning and deep learning

Anamisa, Devie Rosa; Yusuf, Mohammad; Sophan, Mohammad Kautsar; Khairunnisa, Siti Oryza; Irmawati, Budi

doi:10.1109/icic56845.2022.10006960

Cited by 4 publications

(2 citation statements)

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“…There are many publications on regular PNG or RGB rice seed variety classifications in the literature using machine learning and deep learning methods [17][18][19][20]. Table 7 summarizes the rice seed HSI classification.…”

Section: Comparison With State-of-the-art Rice Seed Hsi Classificationmentioning

confidence: 99%

A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures

Díaz-Martínez

Orozco-Sandoval

Manian

et al. 2023

Sensors

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A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars.

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Section: Comparison With State-of-the-art Rice Seed Hsi Classificationmentioning

confidence: 99%

A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures

Díaz-Martínez

Orozco-Sandoval

Manian

et al. 2023

Sensors

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“…An efficacy of ML is its capability to examine a vast quantity of data [8]. ML approaches were a prevalent way of improving the final performances with the ability to derive many physical features from particular data in rice classification [9].…”

Section: Introductionmentioning

confidence: 99%

Quantum-Inspired Moth Flame Optimizer Enhanced Deep Learning for Automated Rice Variety Classification

Alshahrani,

Saeed,

Alotaibi

et al. 2023

IEEE Access

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Automated rice variety detection and classification is a task that includes automatically categorizing and identifying varieties or different types of rice based on different characteristics namely grain texture, shape, colour, and size. This process is essential for quality assessment, agricultural management, and research purposes. Deep learning (DL) is a subfield of machine leaching (ML) that focuses on training an artificial neural network (ANN) with multiple layers to learn hierarchical representations of data. Convolutional Neural Network (CNN) was widely applied in image-based tasks such as rice variety detection, as they could efficiently capture visual features and patterns. In this study, we propose an Automated Rice Variety Detection and Classification using Quantum Inspired Moth Flame Optimizer with Deep Learning (ARVDC-QIMFODL) technique. The presented ARVDC-QIMFODL technique focuses on the automated identification and classification of distinct kinds of rice varieties. To accomplish this, the ARVDC-QIMFODL technique uses the Median modified wiener filter (MMWF) technique for the noise removal process. Followed by, the feature extraction process takes place by an improved ShuffleNet model. For rice variety detection and classification, the long short-term memory (LSTM) approach was applied. At last, the QIMFO algorithm-based hyperparameter selection process is performed to optimize the detection results of the LSTM system. The simulation outcome of the ARVDC-QIMFODL method is tested on a rice image dataset. An extensive set of experiments showed the remarkable efficiency of the ARVDC-QIMFODL system over other models.

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