Apple Ripeness Identification Using Deep Learning

Xiao, Bingjie; Nguyen, Minh; Yan, Wei Qi

doi:10.1007/978-3-030-72073-5_5

Cited by 19 publications

(6 citation statements)

References 58 publications

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“…Faster R-CNN has high accuracy, but with slow speed. In our previous experiments, Faster R-CNN model combined with ResNet-50 model achieved a precision 93%, while YOLOv3 model combined with Darknet model achieved a precision 99.96% [34]. Wan and Goudos [29] improved the convolutional layer and pooling layer of Faster R-CNN network to increase the speed of visual object detection and obtained a mean average precision 86.41%.…”

Section: Fruit Detectio Nmentioning

confidence: 95%

Fruit ripeness identification using YOLOv8 model

Xiao,

Nguyen,

Yan

2023

Multimed Tools Appl

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Deep learning-based visual object detection is a fundamental aspect of computer vision. These models not only locate and classify multiple objects within an image, but they also identify bounding boxes. The focus of this paper's research work is to classify fruits as ripe or overripe using digital images. Our proposed model extracts visual features from fruit images and analyzes fruit peel characteristics to predict the fruit's class. We utilize our own datasets to train two "anchor-free" models: YOLOv8 and CenterNet, aiming to produce accurate predictions. The CenterNet network primarily incorporates ResNet-50 and employs the deconvolution module DeConv for feature map upsampling. The final three branches of convolutional neural networks are applied to predict the heatmap. The YOLOv8 model leverages CSP and C2f modules for lightweight processing. After analyzing and comparing the two models, we found that the C2f module of the YOLOv8 model significantly enhances classification results, achieving an impressive accuracy rate of 99.5%.

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Section: Fruit Detectio Nmentioning

confidence: 95%

Fruit ripeness identification using YOLOv8 model

Xiao,

Nguyen,

Yan

2023

Multimed Tools Appl

Self Cite

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“…CNNs are classified as digital images. Study steps: image preprocessing, object detection, ripeness categorization, and outcome evaluation using deep learning . Another example uses fuzzy-C-means clustering to identify the defective region after using histogram equalization to smooth the image.…”

Section: Apple Sorting Based On Digital Image Processingmentioning

confidence: 99%

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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“…In addition, coffee beans are classified based on the standards, category, defects, and nature of the beverage produced. The types of Arabic espresso are numbered from the grouping by type or imperfection, from two to eight [87].…”

Section: Product Sortingmentioning

confidence: 99%

Artificial Intelligence: Implications for the Agri-Food Sector

et al. 2023

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Artificial intelligence (AI) involves the development of algorithms and computational models that enable machines to process and analyze large amounts of data, identify patterns and relationships, and make predictions or decisions based on that analysis. AI has become increasingly pervasive across a wide range of industries and sectors, with healthcare, finance, transportation, manufacturing, retail, education, and agriculture are a few examples to mention. As AI technology continues to advance, it is expected to have an even greater impact on industries in the future. For instance, AI is being increasingly used in the agri-food sector to improve productivity, efficiency, and sustainability. It has the potential to revolutionize the agri-food sector in several ways, including but not limited to precision agriculture, crop monitoring, predictive analytics, supply chain optimization, food processing, quality control, personalized nutrition, and food safety. This review emphasizes how recent developments in AI technology have transformed the agri-food sector by improving efficiency, reducing waste, and enhancing food safety and quality, providing particular examples. Furthermore, the challenges, limitations, and future prospects of AI in the field of food and agriculture are summarized.

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Apple Ripeness Identification Using Deep Learning

Cited by 19 publications

References 58 publications

Fruit ripeness identification using YOLOv8 model

Fruit ripeness identification using YOLOv8 model

New Insights into Recent Trends and Emerging Technologies in Apple Sorting

Artificial Intelligence: Implications for the Agri-Food Sector

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