Classification of Maize leaf diseases from healthy leaves using Deep Forest

Arora, Jatin; Agrawal, Utkarsh; Sharma, Prerna

doi:10.33969/ais.2020.21002

Cited by 51 publications

(19 citation statements)

References 23 publications

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“…Different color spaces (RGB, HSV, YCbCr, and grayscale) were used to evaluate performance; using RGB images, the highest classification accuracy of 99.4% was achieved. In [39], the authors classified maizeleaf diseases from healthy leaves using deep-forest techniques. In their approach, they varied the deep-forest hyperparameters regarding number of trees, forests, and grains, and compared their results with those of traditional machine-learning models such as SVM, RF, LR, and KNN.…”

Section: Deep-learning-based Identificationmentioning

confidence: 99%

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

et al. 2021

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The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

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Section: Deep-learning-based Identificationmentioning

confidence: 99%

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

et al. 2021

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“…The author reported 98.9% and 98.8% accuracy by using the enhanced version of Google Net and CIFAR10, respectively. Similarly, the author in [21] discussed the deep forest method of corn leaf classification. Deep Forest is a method based on decision trees, which can partially build deep models.…”

Section: Literature Reviewmentioning

confidence: 99%

Leaf Blights Detection and Classification in Large Scale Applications

Fayyaz¹,

Al‐Dhlan²,

Rehman³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Crops are very important to the financial needs of a country. Due to various diseases caused by different pathogens, a large number of crops have been destroyed. As humanoids, our basic need is food for survival, and the most basic foundation of our food is agriculture. For many developing countries, it is mainly an important source of income. Bacterial diseases are one of the main diseases that cause improper production and a major economic crisis for the country. Therefore, it is necessary to detect the disease early. However, it is not easy for humans to analyze the different leaves of plants by themselves when recognizing diseases. In this article, a variety of machine learning methods are used to classify and detect leaf blight. We use the fusion of deep convolutional neural network (CNN) models obtained from SqueezeNet and ShuffleNet to improve the accuracy and robustness of large-scale applications. We use entropy to reduce the complexity of the calculation process and reduce the features in the deep learning process. In addition, we use a support vector machine (SVM) classifier to obtain the classification. We use the CIELAB color space to capture the entire color range to improve accuracy. Our results are very promising because we have achieved 98% accuracy in the early detection of leaf blight.

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“…Classification is a step for grouping features based on similarity or proximity. Various classical machine learning methods are used for classification such as Naïve Bayes [4], [30], [32], Decision Tree [4], [27], [30], [32], k-Nearest Neighbor [4], [33], Support Vector Machine with all its variants [4]- [6], [23], [25], [26], [29]- [35], Random Forest [4], [30], [32], Deep Forest [4], [36]. Neural Network [22], [24], [25], [32], and Bag of Features [6].…”

Section: Classificationmentioning

confidence: 99%

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Setiawan

Rochman

Satoto

et al. 2022

J. Phys.: Conf. Ser.

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Image classification of maize disease is an agriculture computer vision application. In general, the application of computer vision uses two methods: machine learning and deep learning. Implementations of machine learning classification cannot stand alone. It needs image processing techniques such as preprocessing, feature extraction, and segmentation. Usually, the features are selected manually. The classification uses k-nearest neighbor, naïve bayes, decision tree, random forest, and support vector machine. On the other side, deep learning is part of machine learning. It is a development of an artificial neural network that performs automatic feature extraction. Deep learning is capable of recognizing large data but requires high-speed computation. This article compare machine learning and deep learning for maize leaf disease classification. There are five research questions: how to get data, how machine learning and deep learning classify images, how the classification result compare both of them and the opportunities & challenges of research on maize leaf disease classification. The number of articles to review was 62, consisting of 18 articles using machine learning, 28 articles applying deep learning, and the rest are supporting articles.

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Classification of Maize leaf diseases from healthy leaves using Deep Forest

Cited by 51 publications

References 23 publications

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Leaf Blights Detection and Classification in Large Scale Applications

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

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