Computer Vision for Agro-Foods: Investigating a Method for Grading Rice Grain Quality in Sri Lanka

Author:

Herath H. M. K. K. M. B.,Karunasena G. M. K. B.,Prematilake R. D. D.

Publisher

Springer Singapore

Reference30 articles.

1. Aki O, Güllü A, Uçar E (2015) Classification of rice grains using image processing and machine learning techniques

2. Anami B, Malvade N, Hanamaratti N (2015) Behavior of HSI color co-occurrence features in variety recognition from bulk paddy grain image samples. Int J Signal Process Image Process Pattern Recogn 8:19–30

3. Anchan A, Shedthi S (2016) Classification and Identification of rice grains using neural network. Int J Innov Res Comput Commun Eng 4(4):5160–5167

4. Chatnuntawech I, Tantisantisom K, Khanchaitit P, Boonkoom T, Bilgic B, Chuangsuwanich E (2018) Rice classification using spatio-spectral deep convolutional neural network. arXiv preprint arXiv:1805.11491

5. Chaugule A, Mali SN (2014) Evaluation of texture and shape features for classification of four paddy varieties. J Eng 2014:1–8

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1. Survey on Customized Diet Assisted System based on Food Recognition;2023 Second International Conference on Electronics and Renewable Systems (ICEARS);2023-03-02

2. UGVs for Agri Spray with AI assisted Paddy Crop disease Identification;Procedia Computer Science;2023

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