Implementation of Deep Learning Methods to Identify Rotten Fruits

Chakraborty, Sovon; Shamrat, F. M. Javed Mehedi; Billah, Md. Masum; Jubair, Md. Al; Alauddin, Md.; Ranjan, Rumesh

doi:10.1109/icoei51242.2021.9453004

Cited by 37 publications

(7 citation statements)

References 16 publications

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“…These DL-based RF and CNN algorithms were compared with current systems, where the deep feature RF combination algorithms achieved 96.98% accuracy over others. The model proposed by the authors [3] achieved 99.46% accuracy on training and 99.61% accuracy on the validation set using the MobileNetV2 method. Max pooling and average pooling achieved 94.49%, and 93.06% accuracy for training, respectively, and 94.97% and 93.72% accuracy for validation, respectively.…”

Section: Related Workmentioning

confidence: 98%

“…It improves kidney health and reduces amounts of fat. To reduce human efforts and automate rotten fruit identification, the authors proposed a model [3]. Apple, banana, and orange image datasets were used, and input image features were integrated using the CNN algorithm, and images were classified by max pooling, average pooling, and MobileNetV2 techniques.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms

et al. 2022

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Fruit that has reached maturity is ready to be harvested. The prediction of fruit maturity and quality is important not only for farmers or the food industry but also for small retail stores and supermarkets where fruits are sold and purchased. Fruit maturity classification is the process by which fruits are classified according to their maturity in their life cycle. Nowadays, deep learning (DL) has been applied in many applications of smart agriculture such as water and soil management, crop planting, crop disease detection, weed removal, crop distribution, strong fruit counting, crop harvesting, and production forecasting. This study aims to find the best deep learning algorithms which can be used for the prediction of fruit maturity and quality for the shelf life of fruit. In this study, two datasets of banana fruit are used, where we create the first dataset, and the second dataset is taken from Kaggle, named Fruit 360. Our dataset contains 2100 images in 3 categories: ripe, unripe, and over-ripe, each of 700 images. An image augmentation technique is used to maximize the dataset size to 18,900. Convolutional neural networks (CNN) and AlexNet techniques are used for building the model for both datasets. The original dataset achieved an accuracy of 98.25% for the CNN model and 81.75% for the AlexNet model, while the augmented dataset achieved an accuracy of 99.36% for the CNN model and 99.44% for the AlexNet model. The Fruit 360 dataset achieved an accuracy of 81.96% for CNN and 81.75% for the AlexNet model. We concluded that for all three datasets of banana images, the proposed CNN model is the best suitable DL algorithm for bananas’ fruit maturity classification and quality detection.

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Section: Related Workmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms

et al. 2022

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“…Aside from these layers, there are several hidden layers, as well as an input layer. In this study, two pooling layers: Max Pooling 2D and Average Pooling 2D, are implemented [18]. Finally, for the classification of image data MobileNetV2 classifier is used.…”

Section: Cnn Classifiermentioning

confidence: 99%

Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

Tasnim¹,

Chakraborty²,

Shamrat³

et al. 2021

IJACSA

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In recent years, the area of Medicine and Healthcare has made significant advances with the assistance of computational technology. During this time, new diagnostic techniques were developed. Cancer is the world's second-largest cause of mortality, claiming the lives of one out of every six individuals. The colon cancer variation is the most frequent and lethal of the numerous kinds of cancer. Identifying the illness at an early stage, on the other hand, substantially increases the odds of survival. A cancer diagnosis may be automated by using the power of Artificial Intelligence (AI), allowing us to evaluate more cases in less time and at a lower cost. In this research, CNN models are employed to analyse imaging data of colon cells. For colon cell image classification, CNN with max pooling and average pooling layers and MobileNetV2 models are utilized. To determine the learning rate, the models are trained and evaluated at various Epochs. It's found that the accuracy of the max pooling and average pooling layers is 97.49% and 95.48%, respectively. And MobileNetV2 outperforms the other two models with the most remarkable accuracy of 99.67% with a data loss rate of 1.24.

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“…One of the limitations of the dataset is the same plain background. Deep-learning (DL) techniques and CNNs have achieved remarkable success in object detection and recognition [ 4 , 5 ] owing to the rapid advances in DL and CNN in recent years. Using a mix of CNN and SVM, Dias et al [ 6 ] extracted features of apple blossoms from a complicated background, with a decent performance of 0.822 F1-score.…”

Section: Introductionmentioning

confidence: 99%

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning

Mukhiddinov

Muminov

2022

Sensors

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Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables. Owing to similarities in color, texture, and external environmental changes, such as shadows, lighting, and complex backgrounds, the automatic recognition and classification of fruits and vegetables using machine vision is challenging. This study presents a deep-learning system for multiclass fruit and vegetable categorization based on an improved YOLOv4 model that first recognizes the object type in an image before classifying it into one of two categories: fresh or rotten. The proposed system involves the development of an optimized YOLOv4 model, creating an image dataset of fruits and vegetables, data argumentation, and performance evaluation. Furthermore, the backbone of the proposed model was enhanced using the Mish activation function for more precise and rapid detection. Compared with the previous YOLO series, a complete experimental evaluation of the proposed method can obtain a higher average precision than the original YOLOv4 and YOLOv3 with 50.4%, 49.3%, and 41.7%, respectively. The proposed system has outstanding prospects for the construction of an autonomous and real-time fruit and vegetable classification system for the food industry and marketplaces and can also help visually impaired people to choose fresh food and avoid food poisoning.

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Implementation of Deep Learning Methods to Identify Rotten Fruits

Cited by 37 publications

References 16 publications

Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms

Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms

Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning

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