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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Mango Classification and Disease Prediction Using VGG16 and Transfer Learning |
| Authors Name: | Mamata Vinayak Yeul , Ravi V. Mante , Dr. K. A. Waghmare |
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IJRTI_203718
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| Published Paper Id: | IJRTI2505138 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | Agriculture has driven India’s economic development significantly.Automation in agriculture enhances quality, productivity, and national economic growth. The manual grading of fruits is inefficient, inconsistent, and prone to bias. Mangoes are cherished not just for their taste but also for their nutritional value. Often called the “King of fruits”, the mango's appealing aroma, flavorful pulp, and high nutritional content make it popular worldwide. In 2021, mangoes were the third most exported tropical fruit by volume after pineapples and avocados. Asia was the world's largest mango producer in 2021, exporting 912,510 tons. Mango crops are susceptible to various diseases throughout their lifespan, which significantly affects the quality and quantity of fruit production. These diseases are caused by a range of pathogens, including bacteria, fungi, viruses, algae, and insects. These pathogens can attack all parts of the mango plant, from the trunk and branches to the leaves, twigs, petioles, flowers, and fruits. Pests and diseases are major challenges in mango cultivation, often resulting in degraded yields. Artificial intelligence (AI) is a promising solution for improving pest and disease management in mango orchards. Relying solely on manual observation is often insufficient and typically requires expert guidance. To address this issue, image-processing techniques can be implemented to create automated systems. Specifically, deep learning algorithms can be employed to train these systems to distinguish between images of healthy and diseased mangoes. This allows researchers and farmers to accurately identify diseases at an early stage of development.Through the analysis of mango fruit images, it is possible to classify and identify both healthy and diseased specimens. In the proposed system, the following four feature vectors were utilized: color, morphology, texture, and hole structure on the fruit. The following is a summary of the techniques and challenges associated with fruit disease detection, as discussed in the abstract. In recent years, deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as the most popular method among researchers owing to its impressive results. This research compares the performance of VGG16, ResNet50, Inception-V3, AlexNet, and MobileNet architectures in identifying pests and diseases, with a focus on using VGG16. It is crucial to approach this task with care, respect, and truth, ensuring secure and positive responses that promote fairness and avoid any harmful, unethical, prejudiced, or negative content. A Convolutional Neural Network (CNN) utilizing the VGG16 architecture achieved a high accuracy of 92.50% when trained over 50 epochs, surpassing the performance of AlexNet. Mangoes are nutritional powerhouses that boast vitamins A and C, which are crucial for healthy skin and a robust immune system. Imagine mangoes as concentrated sunshine from Miami—they're bright, energizing, and essential for overall wellness.These fruits are also an excellent source of dietary fiber, promoting a healthy digestive system.Mangoes are abundant in antioxidants, remarkable compounds that combat free radicals and contribute to overall vitality.Think of these fruits as your personal tropical protectors, diligently working to keep your body in optimal condition.This paper aims to classify mangoes based on three categories: whether it’s a mango or not mango, whether it’s healthy or unhealthy and whether it’s ripe and unripe. For classification, we have used three different deep learning models. 1.Diseased detection model:predicts if the mango has any disease. 2.Health detection model:classifies the mango as healthy or unhealthy. 3.Ripeness detection model:determines whether the mango is ripe or unripe. Method of Classification: A.Convolutional Neural Network(CNNs) have been used to process the image data of mangoes.CNNs are a class of deep neural networks that excel at image classification tasks.These models are trained on a large dataset of mango images to predict the various categories mentioned above. Transfer Learning with VGG16 :We used a well-known pretrained model, which has been fine-tuned for mango classification tasks to improve accuracy and reduce training time. |
| Keywords: | CNN, VGG16, Transfer learning |
| Cite Article: | "Mango Classification and Disease Prediction Using VGG16 and Transfer Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.b335-b340, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505138.pdf |
| Downloads: | 000439 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2505138
Registration ID:203718
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: b335-b340 Country: Dist:Akola, MH, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505138 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505138 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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