CNN Based Automatic Computer Vision System for Strain Detection and Quality Identification of Banana

Haque, A S M Farhan Al; Hakim, Md. Azizul; Hafiz, Rubaiya

doi:10.1109/acmi53878.2021.9528269

Cited by 14 publications

(8 citation statements)

References 13 publications

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“…In the field of agriculture, Industry 4.0 comes to contribute dynamically, by promoting autonomy and novel technologies, introducing the corresponding Agriculture 4.0. Monitoring of agricultural conditions (territorial variables, hydro-meteorological), counting, sorting [89], harvesting [76], monitoring and detection of defects in terms of quality control, cultivation, disease control, and more agricultural tasks were automated by employing machine vision [108]. Especially in cases where the decision to accurately estimate the crop load (yield prediction) before each harvest is crucial, machine vision systems in Agriculture 4.0 played a significant role.…”

Section: Contribution Of Machine Vision In Industry 40 521 Main Uses ...mentioning

confidence: 99%

Machine Vision—Moving from Industry 4.0 to Industry 5.0

Tzampazaki,

Zografos,

Vrochidou

et al. 2024

Applied Sciences

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The Fourth Industrial Revolution combined with the advent of artificial intelligence brought significant changes to humans’ daily lives. Extended research in the field has aided in both documenting and presenting these changes, giving a more general picture of this new era. This work reviews the application field of the scientific research literature on the presence of machine vision in the Fourth Industrial Revolution and the changes it brought to each sector to which it contributed, determining the exact extent of its influence. Accordingly, an attempt is made to present an overview of its use in the Fifth Industrial Revolution to identify and present the changes between the two consequent periods. This work uses the PRISMA methodology and follows the form of a Scoping Review using sources from Scopus and Google Scholar. Most publications reveal the emergence of machine vision in almost every field of human life with significant influence and performance results. Undoubtedly, this review highlights the great influence and offer of machine vision in many sectors, establishing its use and searching for more ways to use it. It is also proven that machine vision systems can help industries to gain competitive advantage in terms of better product quality, higher customer satisfaction, and improved productivity.

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Section: Contribution Of Machine Vision In Industry 40 521 Main Uses ...mentioning

confidence: 99%

Machine Vision—Moving from Industry 4.0 to Industry 5.0

Tzampazaki,

Zografos,

Vrochidou

et al. 2024

Applied Sciences

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“…In this paper, we discuss the evolution of fruit maturation classification and quality detection models using DL techniques such as CNN and AlexNet. The authors [4] have developed two DL-based CNN models, where the second performed well with 93.4 ± 0.8% accuracy for classification and achieved 98.3 The main contributions of this research are given below:…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we discuss the evolution of fruit maturation classification and quality detection models using DL techniques such as CNN and AlexNet. The authors [4] have developed two DL-based CNN models, where the second performed well with 93.4 ± 0.8% accuracy for classification and achieved 98.3 ± 0.8% accuracy for rotten banana identification. The authors [6] proposed a five-layer CNN containing a convolution layer, a pooling layer, and a fully connected layer model to identify bananas.…”

Section: Related Workmentioning

confidence: 99%

“…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. The CNN-based model was proposed by authors [4] to identify the rottenest bananas and classify five types of bananas: cavendish, ladyfinger, Sabri, green, and red bananas. The authors created their dataset, where 2000 banana images of each type and a total of 10000 images are available in the dataset.…”

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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“…Also, it has been used to obtain higher accuracy in prediction and for overfitting of data. In this research rescale, horizontal flip, vertical flip, shear and zooming augmentation methods had been used for the training dataset [14]. We applied five image generation…”

Section: B Data Preprocessing and Data Augmentationmentioning

confidence: 99%

Local Rose Breeds Detection System Using Transfer Learning Techniques

Farha

Hakim

Khatun

2022

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Flower breed detection and giving details of that breed with the suggestion of cultivation processes and the way of taking care is important for flower cultivation, breed invention, and the flower business. Among all the local flowers in Bangladesh, the rose is one of the most popular and demanded flowers. Roses are the most desirable flower not only in Bangladesh but also throughout the world. Roses can be used for many other purposes apart from decoration. As roses have a great demand in the flower business so rose breed detection will be very essential. However, there is no remarkable work for breed detection of a particular flower unlike the classification of different flowers. In this research, we have proposed a model to detect rose breeds from images using transfer learning techniques. For such work in flowers, resources are not enough in image processing and classification, so we needed a large dataset of the massive number of images to train our model. we have used 1939 raw images of five different breeds and we have generated 9306 images for the training dataset and 388 images for the testing dataset to validate the model using augmentation. We have applied four transfer learning models in this research, which are Inception V3, ResNet50, Xception, and VGG16. Among these four models, VGG16 achieved the highest accuracy of 99%, which is an excellent outcome. Breed detection of a rose by using transfer learning methods is the first work on breed detection of a particular flower that is publicly available according to the study.

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CNN Based Automatic Computer Vision System for Strain Detection and Quality Identification of Banana

Cited by 14 publications

References 13 publications

Machine Vision—Moving from Industry 4.0 to Industry 5.0

Machine Vision—Moving from Industry 4.0 to Industry 5.0

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

Local Rose Breeds Detection System Using Transfer Learning Techniques

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