Then, performance metrics are used for the classification of images (type of particular plant disease), and finally, visualization techniques/mappings [55] are used to detect/localize/classify the ...

Kan HX, Jin L, Zhou FL (2017) Classification of medicinal plant leaf image based on multi-feature extraction. Proc J Pattern Recogn Image Anal 27(3):581–587. Google Scholar Turkoglu M, Hanbay D (2019) Recognition of plant leaves: an approach with hybrid features produced by dividing leaf images into two and four parts.

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p>To facilitate the recognition and classification of medicinal plants that are commonly used by Mauritians, a mobile application which can recognise seventy different medicinal plants has been ...

In the agricultural sector, identification of plant diseases is extremely crucial as they hamper robustness and health of the plant which play a vital role in agricultural productivity. These problems are common in plants, if proper prevention methods are not taken it might seriously affect the cultivation. The current method of detecting disease is done by an …

In deep learning-based medicinal plant classification, CNN [6, 8, 10] is the most popular choice for an automated classification and feature extraction of images. ... (2019) Enhancing classifier accuracy in ayurvedic medicinal plants using WO-DNN. Int J Eng Adv Technol (IJEAT) 9(1):2249–8958.

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India is a country and around 70% of our population relies on development. 33% of our national pay starts from agribusiness. So to identify the plant diseases is a difficult way in the agriculture ...

The issues related to the automatic identification and classification of plant diseases with images is discussed in this section. The challenges conventional plant disease diagnosis systems face are discussed in ... P, Baranowski K, Mbilinyi N, Mrisho L, Ndalahwa M, Hughes DP (2019) A mobile-based deep learning model for …

Elangovan K, Nalini S (2017) Plant disease classification using image segmentation and SVM techniques. Int J Comput Intell Res 13(7):1821–1828. Google Scholar Rima H, Siburian S, Rahmi K, Phong T, Nguyen EL, Lydia KS (2019) Leaf disease classification using advanced SVM algorithm. Int J Eng Adv Technol 8(6S)

Type: Spiral Separator Voltage: 380V Weight: 10T Material: Stainless Steel Material Feature: Heat Resistant Certification: ISO9001:2008, ISO9001:2000

Plant growth is inevitably affected by diseases, and one effective method of disease detection is through the observation of leaf changes. To solve the problem of disease detection in complex backgrounds, where the distinction between plant diseases is hindered by large intra-class differences and small inter-class differences, a complete …

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research …

A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier ... Date Added to IEEE Xplore: 04 April 2019 ISBN Information: Electronic ISBN: 978-1-5386-9111-3 Print on Demand(PoD) ISBN: 978-1-5386-9112-0 INSPEC Accession Number: ...

In this article, we start our study by surveying current DL approaches for plant identification and disease classification. We categorise the approaches into multi-model, multi-label, …

Dileep & Pournami (2019) uses Alexnet model to extract features from data sets and classifies them by SoftMax and SVM classifier, which has great practical value for classification and recognition ...

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The classifiers used are linear discriminant analysis (LDA), multinomial logistic regression (LR), Naive Bayes (NB), classification and regression tree (CART), k-nearest neighbour (KNN), random forest classifier (RFC), bagging classifier (BC), and multi-layer perceptron (MLP) [36, 37]. Performance analysis is carried out for four datasets.

Abstract. Evergreen broad-leaved forest (EBLF) is climax vegetation in East China, and Jiangxi Province is located at the center of EBLF. Although many phytosociological studies have provided detailed descriptions on this forest at local and regional scales, knowledge on the high-level classification of EBLF and factors …

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Posted by Daniel Gleeson on 14th January 2019 FL has looked to spell out to the mining industry the benefits of its REFLUX™ Classifier (RC™) technology. ... Material from the process plant enters through the slurry inlet and passes over an internal over-size protection screen, removing any tramp or debris that may cause …

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The works of Tian et al., 2019, Liang et al., 2019, Long and Liu, 2019, Abade et al., 2019 highlight the use of images as one of the most advanced methods of detection and recognition of plant diseases. Despite the significant advances provided by deep learning, numerous challenges still hinder the recognition and identification of plant …

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School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China. 2. Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China ... Jun Zhang, and Bing Wang. 2019. "Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network" Sensors 19, …

Results revealed that the random forest classifier outperformed the rest. In contrast, Xue et al. [3] studied the classification of medicinal plants common in China. The proponents used different ...

Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural …

The state-of-the-art YOLOv8n model for maize plant disease detection and classification is used. The best-trained model is integrated with the user-friendly …