Taste-wise fruit sorting system using thermal image processing

Raka, Suraj; Kamat, Ashutosh; Chavan, Shubhada; Tyagi, Aanchal; Soygaonkar, Pratik

doi:10.1109/punecon46936.2019.9105726

Cited by 11 publications

(3 citation statements)

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“…Using these imaging methods and portable technologies, such as the Raspberry Pi, a hand-held device was developed to categorize the apples on the field itself or in the market. 74 Another study examined the possibility of employing thermal imaging in the 8−13 μm spectral range to find watercore in Gloster apples. Before the experiment, apples were kept at a temperature of 1.5 °C, and they were subsequently put in the thermostat measurement location with a 20 °C ambient temperature.…”

Section: Apple Sorting Based On Various Spectral Imaging Techniquesmentioning

confidence: 99%

“…In an additional study, visual and thermal imaging were coupled to assess the ripeness and freshness of the fruits. Using these imaging methods and portable technologies, such as the Raspberry Pi, a hand-held device was developed to categorize the apples on the field itself or in the market . Another study examined the possibility of employing thermal imaging in the 8–13 μm spectral range to find watercore in Gloster apples.…”

Section: Apple Sorting Using Miscellaneous Techniquesmentioning

confidence: 99%

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New Insights into Recent Trends and Emerging Technologies in Apple Sorting

Meenu,

Choudhary,

Kaushal

et al. 2024

ACS Food Sci. Technol.

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Apple sorting is a crucial step for their grading and determination of their commercial value. Sorting techniques are employed at several stages of production process, including harvest, storage, and packing. Manually apples are sorted and graded according to size and color, which is time-consuming, labor-intensive, and error-prone. Therefore, investigating nondestructive, precise, and effective methods of apple sorting is necessary to surmount the drawbacks connected with this. Technology advancements have led to the invention of several automated sorting techniques based on digital image processing, spectral and optical properties that are rapid and efficient compared to manual sorting. These technologies have advanced significantly for assessing the physical and nutritional quality of apples along with internal defects. In the present review, an overview of these advanced apple sorting methods is provided in detail. The contemporary techniques have improved apple sorting accuracy and efficiency while reducing labor costs and improving the quality. Computer vision systems can achieve high levels of accuracy and consistency by enabling nondestructive and quick assessment of many quality metrics, decreasing the subjectivity involved with human sorting. Spectral analysis and hyperspectral imaging have potential for determining interior characteristics like sugar content and ripeness. Robotics, machine learning, and sensor technologies can improve the efficiency, precision, and adaptability of sorting systems by learning from prior sorting data and adjusting to fresh variations in apple quality. In addition, sorting methods combining different techniques need to be developed to detect internal and external quality of apples.

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Section: Apple Sorting Based On Various Spectral Imaging Techniquesmentioning

confidence: 99%

Section: Apple Sorting Using Miscellaneous Techniquesmentioning

confidence: 99%

New Insights into Recent Trends and Emerging Technologies in Apple Sorting

Meenu,

Choudhary,

Kaushal

et al. 2024

ACS Food Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…During high-speed point-to-point motions, traditional sorting robots face limitations like vibration, discontinuity, and low fault tolerance [14][15][16][17][18][19]. To improve the speed and stability of robot operations, Raka et al [20] established the fitness function through particle swarm optimization (PSO), and formulated a visual servo control system for fruit sorting robot, which does not include the Jacobian matrix of target image and the calibration process. Traditional visual algorithms could hardly classify and position various types of fruits with complex shapes [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Target Positioning and Sorting Strategy of Fruit Sorting Robot Based on Image Processing

Yu¹,

Miao²,

Zhang³

et al. 2021

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To a certain extent, automated fruit sorting systems reflect the degree of automated production in modern food industry, and boast a certain theoretical and application value. The previous studies mostly concentrate on the design of robot structure, and the control of robot motions. There is little report on the feature extraction of fruits in specific applications of fruit sorting. For this reason, this paper explores the target positioning and sorting strategy of fruit sorting robot based on image processing. Firstly, the authors constructed a visual sorting system for fruit sorting robot, and explained the way to recognize objects in three-dimensional (3D) scene and to reconstruct the spatial model based on sorting robot. Next, the maturity of the identified fruits was considered the prerequisite of dynamic sorting of fruit sorting robot. Finally, the program flow of the fruit sorting robot was given. The effectiveness of our strategy was verified through experiments.

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Advancing post-harvest fruit handling through AI-based thermal imaging: applications, challenges, and future trends

Pathmanaban,

Gnanavel,

Anandan

et al. 2023

Discov Food

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Recent advancements in imaging, electronics, and computer science have engendered significant progress in non-destructive testing and quality monitoring within the agro-food industry. This progress is particularly evident in integrating infrared thermal imaging (TI) and artificial intelligence (AI) techniques. As a non-contact method, AI-based TI holds promise in detecting various quality attributes and has found extensive applications in agriculture, food processing, and post-harvest fruit handling. This paper delves into recent applications of AI-based thermal imaging, specifically in post-harvest fruit handling. The introduction provides a comprehensive overview of the challenges faced in the post-harvest fruit handling industry while emphasizing the advantages of AI-driven thermal imaging technology. The detailed thermal imaging system encompasses both passive and active thermography techniques. This paper provides an in-depth exploration of artificial intelligence, focusing on machine learning and deep learning. It highlights the significance of convolutional neural networks (CNNs) and their architectural phases. Subsequently, critical applications of AI-based thermal imaging in post-harvest fruit quality assessment are discussed. These applications encompass bruise detection, maturity identification, condition monitoring, grading and sorting, pest and disease detection, and considerations for packaging and supply chain management. Furthermore, this paper addresses the challenges and limitations of AI-based thermal imaging in post-harvest fruit handling. In conclusion, this paper discusses future trends in AI-based thermal imaging, emphasizing the potential for increased automation and integration with emerging technologies in the post-harvest fruit handling sector. The insights provided contribute to the ongoing dialog surrounding optimizing quality assessment processes in the agro-food industry.

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Taste-wise fruit sorting system using thermal image processing

Cited by 11 publications

References 1 publication

New Insights into Recent Trends and Emerging Technologies in Apple Sorting

New Insights into Recent Trends and Emerging Technologies in Apple Sorting

Target Positioning and Sorting Strategy of Fruit Sorting Robot Based on Image Processing

Advancing post-harvest fruit handling through AI-based thermal imaging: applications, challenges, and future trends

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