An Advanced Method of Identification Fresh and Rotten Fruits using Different Convolutional Neural Networks

Miah, Sohel; Tasnuva, Tayeeba; Islam, Saiful; Keya, Mumenunnessa; Rahman, Md. Riazur; Hossain, Syed Akhter

doi:10.1109/icccnt51525.2021.9580117

Cited by 13 publications

(2 citation statements)

References 16 publications

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“…The system consists of hardware [4] components like an Arduino Nano microcontroller, a stepper motor with a conveyor belt, a robotic arm for picking and placing apples, and a USB camera connected to a Windows system. Figure 4 shows Actual Model Setup.…”

Section: Figure 3 Circuit Diagrammentioning

confidence: 99%

Robotic Arm for Segregation Using Image Processing

Drashti Parmar,

Sumeet Rajak,

Sourabh Patil

et al. 2024

Int Res J Adv Engg Hub

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The recent surge in industrial expansion can be attributed significantly to the advancements in automation technology. For better performance of industrial processes, automated systems are used. Image processing has played a great role in the applications of robotics and embedded systems. Sorting of objects is usually done by humans which takes a lot of time and effort. By employing image processing methods alongside appropriate sensors, object detection becomes feasible, enabling the utilization of robotic arms for sorting diverse items. This reduces human effort and improves the time to market the product. The model includes the conveyor system, capturing and detecting the objects and placing the objects. Using image processing techniques, the captured image is compared with its texture and color. Based on image processing, the robotic arm will be controlled and place the objects in desired locations. An Arduino model is used to process the captured image. The captured image is other than a pre-specified object, it involves the conveyor belt starting to move and the object is collected at the end of the conveyor system.

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Section: Figure 3 Circuit Diagrammentioning

confidence: 99%

Robotic Arm for Segregation Using Image Processing

Drashti Parmar,

Sumeet Rajak,

Sourabh Patil

et al. 2024

Int Res J Adv Engg Hub

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“…Previous studies have shown that the Xception architecture generally outperforms other architectures in freshness classification tasks for fruits and vegetables [10][11], therefore this is the architecture we adopt to apply transfer learning and fine-tuning. Xception's architecture is tailored to specific configurations, including a 100x100x3 input layer, and frozen weights using pre-trained ones from ImageNet, a database structured according to the WordNet hierarchy [12].…”

Section: Transfer Learning Through Xception Architecturementioning

confidence: 99%

FRESHNets: Highly Accurate and Efficient Food Freshness Assessment Based on Deep Convolutional Neural Networks

Martínez Pazos,

Gulín González,

Batard Lorenzo

et al. 2024

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Food freshness classification is a growing concern in the food industry, mainly to protect consumer health and prevent illness and poisoning from consuming spoiled food. Intending to take a significant step towards improving food safety and quality control measures in the industry, this study presents two models based on deep learning for the classification of fruit and vegetable freshness: a robust model and an efficient model. Models’ performance evaluation shows remarkable results; in terms of accuracy, the robust model and the efficient model achieved 97.6% and 94.0% respectively, while in terms of Area Under the Curve (AUC) score, both models achieved more than 99%, with the difference in inference time between each model over 844 images being 13 seconds.

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