An Enhanced Feature Extraction Technique for Diagnosis of Pathological Problems in Mango Crop

Ullagaddi, S. B.; Raju, S. Viswanadha

doi:10.5815/ijigsp.2017.09.04

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…The results gained are quite substantial, yet the proposed system is only applicable to one ailment. They proposed an enhanced Ullagaddi & Raju [10] to more accurately recognize diseases. The authors present an improved Wavelet-PCA-based Statistical Feature Extraction technique for plant disease identification using MRKT-based directional characteristics.…”

Section: -Literature Reviewmentioning

confidence: 99%

Light-Weight Deep Learning Model for Accelerating the Classification of Mango-Leaf Disease

Mahmud,

Al Mamun,

Hossen

et al. 2024

Emerg Sci J

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Mango leaf diseases represent a serious threat to world agriculture, necessitating prompt and accurate detection to avert catastrophic effects. In response, this study suggests a light-weight, deep learning-based method for automatically classifying mango leaf diseases. The model is based on the original DenseNet architecture, which is well known for its effectiveness in image classification tasks. Custom layers have been added over the existing layer of the original DenseNet model. The proposed model has been compared with other existing pre-trained models. Based on comparisons, the proposed model, DenseNet78, proved to be efficient even on a relatively small dataset, where the conventional model failed. The proposed model ensured generalization across regions, disease variants, and diverse datasets of mango leaves. The results demonstrate that the fine-tuned DenseNet architecture (DenseNet78), along with an ideal growth rate, modifying block size, and a number of layers, provides optimum accuracy, with 99.47% accuracy in identifying healthy mango leaves and 99.44% accuracy in detecting various mango leaf diseases. The results also demonstrate that the model is effective in accelerating the training process because of careful comparative analysis of all the available alternatives, including the most effective combination of optimizers, learning rate schedulers, and loss functions. The study's conclusion is an automated approach for diagnosing mango leaf disease using an improved and optimized DenseNet architecture (DenseNet78). Doi: 10.28991/ESJ-2024-08-01-03 Full Text: PDF

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Section: -Literature Reviewmentioning

confidence: 99%

Light-Weight Deep Learning Model for Accelerating the Classification of Mango-Leaf Disease

Mahmud,

Al Mamun,

Hossen

et al. 2024

Emerg Sci J

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“…The performances obtained are very significant but the proposed system is only limited to anthracnose disease. [13] is an improvement of [12] in order to recognize diseases with more accuracy. Authors introduce an enhanced Wavelet-PCA based Statistical Feature Extraction technique with MRKT based directional features for plant disease recognition.…”

Section: Number Of Timesmentioning

confidence: 99%

Mango Diseases Classification Solutions Using Machine Learning or Deep Learning: A Review

Faye¹,

Diop²,

Dione³

2022

JCC

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The mango crop suffers from several diseases that reduce both the production and the quality of the mangoes. This also reduces its price on the international market. Diagnosis of these diseases remains difficult in many countries due to poverty and lack of infrastructure. Plant pathologists use several techniques to identify these diseases. But these techniques are time consuming and relatively expensive for mango growers and the solutions proposed are often not very accurate and sometimes biased. In the last decade, researchers have proposed several solutions in the field of automatic diagnosis of mango diseases. Such solutions are based on Machine Learning (ML) and Deep Learning (DL) algorithms. In this paper, we divided these solutions into two groups: solutions based on classical ML algorithms, and those based on DL. In recent years, DL, especially Convolutional Neural Network (CNN) has become the most widely used method by researchers because of its impressive performance. The critical analysis of the proposed solutions has allowed us to identify their limits and potential challenges in mango disease automatic diagnosis.

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“…Leaf diseases usually carried out by agriculture, in India an automated system was used to classify and identify mango leaf diseases, images were acquired using digital camera, averaging filter was used to remove noise, color transformation (RGB to HSI) and histogram equalization, image segmentation techniques were used [12]. 500 images with resolution of 4320x3240 for each leaf, fruit and flower with healthy/diseased leaf's parts were captured using digital camera in [14] per-processing such as: edge enhancement, gamma correction methodology and segmentation were performed. 3500 leaf images were captured using "digital camera" and "mobile camera", the system was using to detect four diseases of mango leaf [15].…”

Section: Preprocessingmentioning

confidence: 99%

Classification and Diseases Identification of Mango Based on Artificial Intelligence: A Review

Hassoon

2022

J. Al-Qadisiyah Comp. Sci. Math.

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Mango is a drupe fruit which plays an active role in the economy of different countries. Classification process is a fundamental process in: diseases detection domain, sorting and grading. Previously, farmers can detect mango's diseases, identification ripe and unripe mango by their eyes, but it is inaccurate, waste of time and effort. AI technology helping farmers get high quality agricultural crops, the essential idea of AI in agriculture is its flexibility, reliability, speedy performance and applicability. AI technology improves enterprise performance and productivity by automating processes or tasks that once required human skill. AI can also understand data on a scale that no human can achieve, this ability can bring great advantages in the field of agriculture. In this paper, a review for application of artificial intelligence in mango classification and mango diseases identification have been presented.

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An Enhanced Feature Extraction Technique for Diagnosis of Pathological Problems in Mango Crop

Abstract: Abstract-Lack of apparent shape and

Cited by 8 publications

References 18 publications

Light-Weight Deep Learning Model for Accelerating the Classification of Mango-Leaf Disease

Light-Weight Deep Learning Model for Accelerating the Classification of Mango-Leaf Disease

Mango Diseases Classification Solutions Using Machine Learning or Deep Learning: A Review

Classification and Diseases Identification of Mango Based on Artificial Intelligence: A Review

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