Efficient deep learning-based semantic mapping approach using monocular vision for resource-limited mobile robots

Singh, Aditya; Narula, Raghav; Rashwan, Hatem A.; Abdel-Nasser, Mohamed; Puig, Domènec; Nandi, Gora Chand

doi:10.1007/s00521-022-07273-7

Cited by 7 publications

(1 citation statement)

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“…In [16], many machine vision-based [17] methods for food grain quality measurements were compared. The study discussed using artificial intelligence (AI) in grading and identifying foreign matter, insect infestation, microbial infection, and discolored grains.…”

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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Section: Related Workmentioning

confidence: 99%