Mango Diseases Identification by a Deep Residual Network with Contrast Enhancement and Transfer Learning

Trang, Kien; TonThat, Long; Thao, Nguyen Gia Minh; Thi, Ngoc Tran Ta

doi:10.1109/csudet47057.2019.9214620

Cited by 36 publications

(3 citation statements)

References 12 publications

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“…Color-based segmentation is performed to obtain RGB segments of the input image and is followed by feature extraction. Features like Entropy, Contrast, Smoothness, Correlation, and other metrics which belong to Gray Level Occurrence matrix [13] are extracted and are fed to the classification. algorithms.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning and Computer Vision based Model for Detection of Diseased Mango Leaves

Balasundaram

Sivaraman

2022

IJRITCC

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Mangifera Indica, commonly known as mangoes, is the most commercialized export fruit crop in India, accounting for about 40% of the total global production. Due to its widespread production, it is vulnerable to a variety of diseases that affect its yield and resulting in loss. These diseases like Anthracnose, Powdery Mildew, Leaf blights, etc., occur primarily on leaves. As a result, there is a great need for a system that helps in the detection of diseased mango leaves. In this paper, we propose a system that makes use of pre-trained Convolutional Neural Network architecture, the ResNet-50 for the detection of infected mango leaves. The dataset contains 435 images of mango leaves with binary classification as healthy and diseased. These images are pre-processed by resizing them and applying CLAHE. After applying in-place data augmentation on the dataset, the features are extracted using the ResNet-50 model. For the classification process, we make use of fine-tuned head and Machine Learning classifiers such as Support Vector Machine, Gradient Boosting, Logistic Regression, XGBoost, Decision Tree, and K Nearest Neighbour. Among them, the fine-tuned head classifier achieved an accuracy of 97.7%, and Machine Learning classifiers such as SVM, Logistic Regression obtained an accuracy of 100%. The experimental results obtained validate that the system is efficient in its performance of detecting the two classes of mango leaves.

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Section: Related Workmentioning

confidence: 99%

Deep Learning and Computer Vision based Model for Detection of Diseased Mango Leaves

Balasundaram

Sivaraman

2022

IJRITCC

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“…Finally, the re-searcher concluded that CNN is the best effective feature extraction method for the classification of mango defects. The researcher (Trang et al, 2019) researched mango disease classification Using a deep residual network (ResNet) with contrast enhancement and transfer learning, the research describes a method for locating mango disease. Anthracnose, Cercospora leaf spot, and Powdery mildew were the three illnesses that the authors utilized to identify 300 out of a dataset of 1000 mango photos.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Mango Disease Using Convolutional Neural Network and Histogram Oriented Gradients

Sema,

Yayeh,

Andualem

2023

Preprint

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This study suggests a Convolutional Neural Network (CNN) and Histogram Oriented Gradients (HOG)-based automatic detection and classification system for mango disease. Early detection is essential for efficient disease management since mango disease can have a major influence on fruit quality and yield. The suggested system makes use of the CNN algorithm for extracting features and the HOG technique for capturing shape and texture data. The extracted features are subsequently used to feed a disease classification model for disease detection. The efficiency of the proposed model is demonstrated by experimental findings, which achieve excellent accuracy in both disease detection and classification tasks. The CNN-HOG hybrid model outperforms CNN or HOG alone in terms of performance, demonstrating the complementary nature of these two methods for the detection and classification of mango disease. The system's performance is evaluated using measures for accuracy, precision, and recall and the accuracy of the proposed model is 98.80%. This research helps establish effective and trustworthy tools for managing mango disease by automating the detection and classification process. This enables prompt intervention and reduces crop losses.

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“…Pertanian adalah salah satu sektor ekonomi terpenting di negara-negara Asia Tenggara. Saat ini, pembangunan ekonomi sangat bergantung pada pertanian [1]. Seperti contohnya Mangga, Mangga merupakan tanaman dengan nama latin Mangifera Indica L. yang berasal dari Negara India dan termasuk keluarga Anacardiacea.…”

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Pendiagnosa Daun Mangga Dengan Model Convolutional Neural Network

Ayu

Dwi

Minarno

2021

Com, Engine, Sys, Sci

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Pertanian adalah salah satu sektor ekonomi yang terpenting di negara-negara Asia Tenggara. Saat ini, pembangunan ekonomi sangat bergantung pada pertanian. Seperti contoh Mangga, Manga juga merupakan bahan makanan yang dapat diolah menjadi berbagai jenis makanan yang lezat. Karena banyaknya manfaat pada buah ini tak jarang masyarakat ingin menanam pohon mangga untuk dibudidayakan dengan tujuan komersil maupun pribadi. Salah satu masalah utama yang menurunkan kualitas dan kuantitas manufaktur pertanian adalah penyakit tanaman. Oleh karena itu bidang penelitian pertanian menarik para peneliti dan ilmuwan untuk memberikan teknik untuk mengidentifikasi penyakit tanaman dengan menggunakan pengolahan gambar dan visi komputer seperti dalam kertas ini yang menggunaka model Convolutional Neural Network (CNN) untuk mengklasifikasi jenis daun mangga yang sakit (terserang hama) dan sehat berdasarkan bentuk dan tekstur daun. Pada penelitian yang dihasilkan tingkat akurasi sebesar 0,96.

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Mango Diseases Identification by a Deep Residual Network with Contrast Enhancement and Transfer Learning

Cited by 36 publications

References 12 publications

Deep Learning and Computer Vision based Model for Detection of Diseased Mango Leaves

Deep Learning and Computer Vision based Model for Detection of Diseased Mango Leaves

Automatic Detection and Classification of Mango Disease Using Convolutional Neural Network and Histogram Oriented Gradients

Pendiagnosa Daun Mangga Dengan Model Convolutional Neural Network

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