IOT Based Real-Time Control and Monitoring System for Food Grain Procurement and Storage

Hema, L. K.; Velmurugan, S; Sunil, D N; Aziz, Sher; Thirunavkarasu, S

doi:10.1088/1757-899x/993/1/012079

Cited by 9 publications

(7 citation statements)

References 3 publications

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“…The major issue with these quality assessment systems is their high cost, which hinders their use for mass. In the literature, IoT technologies were used in grain procurement [38,39] and grain distribution [40] in public centers. [38] analyzed India's challenges in grain procurement and its solutions based on IoT.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…[38] analyzed India's challenges in grain procurement and its solutions based on IoT. [39] discussed the use of mobile technology in food collection and storage. In [40], a public distribution system is designed in an automated way by using different sensors, which provides ease to the flow of information.…”

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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“…A stickiness sensor is utilized to gauge and report both humidity and temperature of the air content. The proportion of humidity noticeable all around to the most noteworthy measure of humidity at an air temperature is called relative mugginess 19 . Relative mugginess turns into a significant factor for fish feeders to keep up with the nature of the nourishment in the compartment.…”

Section: Humidity Sensormentioning

confidence: 99%

IoT System on Dynamic Fish Feeder Based on Fish Existence for Agriculture Aquaponic Breeders

et al. 2021

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Maintaining and breeding fish in a pond are a crucial task for a large fish breeder. The main issues for fish breeders are pond management such as the production of food for fishes and to maintain the pond water quality. The dynamic or technological system for breeders has been invented and becomes important to get maximum profit return for aquaponic breeders in maintaining fishes. This research presents a developed prototype of a dynamic fish feeder based on fish existence. The dynamic fish feeder is programmed to feed where sensors detected the fish's existence. A microcontroller board NodeMCU ESP8266 is programmed for the developed hardware. The controller controls the feeding and feedback mechanism based on attached sensors. An ultrasonic sensor is programmed with the controller to detect the level of food and waterproof ultrasonic to detect existing fish. The humidity sensor was used to measure the humidity in the food container to control the food freshness. Two servo motors were used to move the waterproof sensor to attract the fish and to dispense the food to the fish when existed. The result presents four measured levels that are the temperature of the food container, the quality of food based on humidity measured, fish detection counter and level of fish food in the container. Data analytics on all the measured levels was presented on the ThingSpeak platform by using Blynk to get data collections from all sensors. This research is significant for fish breeders that support IR4.0 system connected online and mobile apps which also contribute to today’s agriculture.

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“…Paddy has such wide varieties, and it varies in the form of changes in length, width, color, and gelatinization temperature [2,3]. To evaluate all of these parameters, there is a demand [4] for an integrated system connected through the internet that can detect the variation in the quality of different grain lots and update information automatically to a common server. Such an IoT systems would help in integrating all the procurement centers through a common server for centralized monitoring.…”

Section: Introductionmentioning

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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IOT Based Real-Time Control and Monitoring System for Food Grain Procurement and Storage

Cited by 9 publications

References 3 publications

Efficient Paddy Grains Quality Assessment Approach Utilizing Affordable Sensors

Efficient Paddy Grains Quality Assessment Approach Utilizing Affordable Sensors

IoT System on Dynamic Fish Feeder Based on Fish Existence for Agriculture Aquaponic Breeders

Efficient Paddy Grain Quality Assessment Approach Utilizing Affordable Sensors

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