Classification System to Detect Diseases in Apples by Using a Convolutional Neural Network

Acosta, Alejandro; Ochoa, Alberto; Rodriguez-Eparza, Erick; Oliva, Diego; Juan, Ángel A.; Pajares, Gonzalo

doi:10.1007/978-3-030-68663-5_21

Cited by 2 publications

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“…Powdery mildew is a serious disease that wreaks havoc on buds, new shoots, and leaves. A white powdery coating on the leaves distinguishes this disease [9,10]. Cedar apple or yellow patches on the leaf undersides are surrounded by a red band, and small black spots in the center are called aecia form [11].…”

Section: Introductionmentioning

confidence: 99%

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%