Paddy seed variety identification using T20-HOG and Haralick textural features

Uddin, Md. Romij; Islam, Mohammad Aminul; Shajalal, Md; Hossain, Mohammad Afzal; Yousuf, Md. Sayeed Iftekhar

doi:10.1007/s40747-021-00545-0

Cited by 7 publications

(5 citation statements)

References 52 publications

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“…[43]. Although there are laboratories for purity testing of seeds, automatic seed classification systems using computer vision and image processing are desired [44]. Furthermore, methods of improving the accuracy of models are needed [45].…”

Section: Discussionmentioning

confidence: 99%

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Sabancı

2023

Sustainability

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In the present study, a deep learning-based two-scenario method is proposed to distinguish tomato seed cultivars. First, images of seeds of four different tomato cultivars (Sacher F1, Green Zebra, Pineapple, and Ozarowski) were taken. Each seed was then cropped on the raw image and saved as a new image. The number of images in the dataset was increased using data augmentation techniques. In the first scenario, these seed images were classified with four different CNN (convolutional neural network) models (ResNet18, ResNet50, GoogleNet, and MobileNetv2). The highest classification accuracy of 93.44% was obtained with the MobileNetv2 model. In the second scenario, 1280 deep features obtained from MobileNetv2 fed the inputs of the Bidirectional Long Short-Term Memory (BiLSTM) network. In the classification made using the BiLSTM network, 96.09% accuracy was obtained. The results show that different tomato seed cultivars can be distinguished quickly and accurately by the proposed deep learning-based method. The performed study is a great novelty in distinguishing seed cultivars and the developed innovative approach involving deep learning in tomato seed image analysis, and can be used as a comprehensive procedure for practical tomato seed classification.

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Section: Discussionmentioning

confidence: 99%

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Sabancı

2023

Sustainability

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“…Ref. [24] used T-20 HOG (Top-20 Histogram of Oriented Gradient) features and Haralick features for the identification of paddy varieties and achieved a precision of 99% on the BDRICE (private) dataset. Ref.…”

Section: Related Workmentioning

confidence: 99%

Efficient Paddy Grain Quality Assessment Approach Utilizing Affordable Sensors

Singh,

Raj,

Meghwar

et al. 2024

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Paddy (Oryza sativa) is one of the most consumed food grains in the world. The process from its sowing to consumption via harvesting, processing, storage and management require much effort and expertise. The grain quality of the product is heavily affected by the weather conditions, irrigation frequency, and many other factors. However, quality control is of immense importance, and thus, the evaluation of grain quality is necessary. Since it is necessary and arduous, we try to overcome the limitations and shortcomings of grain quality evaluation using image processing and machine learning (ML) techniques. Most existing methods are designed for rice grain quality assessment, noting that the key characteristics of paddy and rice are different. In addition, they have complex and expensive setups and utilize black-box ML models. To handle these issues, in this paper, we propose a reliable ML-based IoT paddy grain quality assessment system utilizing affordable sensors. It involves a specific data collection procedure followed by image processing with an ML-based model to predict the quality. Different explainable features are used for classifying the grain quality of paddy grain, like the shape, size, moisture, and maturity of the grain. The precision of the system was tested in real-world scenarios. To our knowledge, it is the first automated system to precisely provide an overall quality metric. The main feature of our system is its explainability in terms of utilized features and fuzzy rules, which increases the confidence and trustworthiness of the public toward its use. The grain variety used for experiments majorly belonged to the Indian Subcontinent, but it covered a significant variation in the shape and size of the grain.

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“…It obtained an accuracy of 93% for adulteration recognition. [27] used T-20 HOG (Top-20 Histogram of Oriented Gradient features) and Haralick features for the identification of paddy varieties and achieved a precision of 99 % on the BDRICE (private) dataset. [28] employed an SVM algorithm and image processing techniques to inspect paddy grain varieties.…”

Section: Related Workmentioning

confidence: 99%

Efficient Paddy Grains Quality Assessment Approach Utilizing Affordable Sensors

Kumar,

Singh,

Raj

et al. 2023

Preprint

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In the realm of computer vision, paddy (Oryza Sativa) plays a pivotal role as a globally consumed staple crop. Its cultivation, harvesting, processing, and storage involve intricate quality control. Numerous factors, including weather conditions and irrigation frequency, influence grain quality. To address this, we present an innovative approach that combines image processing and machine learning (ML). Existing methods for rice grain quality assessment, while valuable, are tailored to rice-specific characteristics, employing complex and costly setups and opaque ML models. Our research overcomes these limitations with a robust ML-based IoT system for paddy grain quality assessment, using affordable sensors, a comprehensive data collection process, and an ML-driven image processing model. Importantly, our approach utilizes interpretable features like Shape, Size, Moisture, and Maturity for paddy grain classification. Rigorous real-world testing confirms its precision, marking it as the first automated system capable of providing a reliable overall quality metric. Our system’s unique feature lies in its transparency, with clear features and fuzzy rules, inspiring confidence and trust. While our experiments primarily feature Indian Subcontinent grain varieties, the system’s adaptability to diverse paddy types is evident, contributing significantly to computer vision.

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Paddy seed variety identification using T20-HOG and Haralick textural features

Cited by 7 publications

References 52 publications

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Efficient Paddy Grain Quality Assessment Approach Utilizing Affordable Sensors

Efficient Paddy Grains Quality Assessment Approach Utilizing Affordable Sensors

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