Mango (Mangifera indica L.) Classification Using Convolutional Neural Network and Linear Classifiers

Naik, Sapan; Desai, Purva

doi:10.1007/978-981-16-4538-9_17

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…A CNN is a powerful deep learning algorithm that can deal with millions of variables, input 2D images to reduce computational costs, and apply convolutional filters [24]. It can learn the filters while other algorithms use hand-engineering filters with enough training [25]. The convolutional layer is the linear operation in which filters (kernel) are applied to real images [25].…”

Section: Introductionmentioning

confidence: 99%

“…It can learn the filters while other algorithms use hand-engineering filters with enough training [25]. The convolutional layer is the linear operation in which filters (kernel) are applied to real images [25]. Transfer learning (TL) is a machine learning technique used for transferring knowledge from one deep model to another fine-tuned model [23,26].…”

Section: Introductionmentioning

confidence: 99%

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A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Rehman¹,

Khan²,

Alhaisoni³

et al. 2023

Computers, Materials &Amp; Continua

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Identifying fruit disease manually is time-consuming, expertrequired, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.

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Section: Introductionmentioning

confidence: 99%