Classification of Leaf Disease Using Texture Feature and Neural Network Classifier

Kurale, Neha G.; Vaidya, Madhav

doi:10.1109/icirca.2018.8597434

Cited by 18 publications

(4 citation statements)

References 7 publications

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“…In addition, considering the centimeter-level high-resolution RGB images obtained by UAVs, the fusion of texture features and color features can lead to information complementarity and extracting more meaningful information from the imagery. Previous studies have shown that image texture features extracted based on gray level co-occurrence matrix (GLMC) are effective in nitrogen content estimation ( Zheng et al, 2020 ) and the classification of diseases ( Kurale and Vaidya, 2018 ), the results of which showed good performance.…”

Section: Introductionmentioning

confidence: 99%

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Zhang

et al. 2022

Front. Plant Sci.

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Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R2 was 0.9109, and RMSE was 0.91277 t.ha−1. rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.

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Section: Introductionmentioning

confidence: 99%

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Zhang

et al. 2022

Front. Plant Sci.

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“…Finally, the binary images will be obtained by using the optimal thresholding. Guo et al (2018) Pulse coupled neural network with shuffle frog leap algorithm Plant diseases Kurale and Vaidya (2018) NN classifier Leaf diseases Prajwala et al (2018) Convolutional neural networks Tomato leaf diseases Anand et al (2016) KCM Brinjal leaf diseases Trongtorkid and Pramokchon (2018) Rule-based model Mango diseases Korkut et al (2018) Machine learning methods Plant leaf diseases…”

Section: Methodsmentioning

confidence: 99%

“…Another method, Neural Network (NN) algorithm, coupled with shuffle frog leap algorithm, is used by (Guo et al, 2018) for classifying plant diseases. Other researchers using NN for their classifications of plant diseases include (Kurale and Vaidya, 2018;Wang et al, 2012;Prajwala et al, 2018). In addition, (Anand et al, 2016) uses standard K-means clustering method for identifying Brinjal leave diseases.…”

Section: Related Workmentioning

confidence: 99%

Detection of Plant Leaf Diseases Using K?mean++ Intermeans Thresholding Algorithm

Wisaeng¹,

Sa-ngiamvibool

2020

Journal of Computer Science

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In the field of agricultural information, the plant leaf disease detection is highly important for both farmer life and environment. To improve the accuracy of plant leaf disease detection and reduce the image processing time, the improved K-mean++ clustering and intermeans thresholding method are proposed in this study. The proposed algorithms are used for training and testing diseases in plant leaf images in two different databases. Of the proposed methods, the intermeans algorithm will be selected based on different thresholding values. The optimal value of thresholding-i.e., the intermeans algorithm-will help increase the accuracy and speed of classifying diseases in plant leaf images. This method will be also used with unseen images of plant leaf. The experimental result of the detection of plant leaf diseases achieves an average detection accuracy of 98.10%. When compared with the results based on standard K-mean clustering, the current method gives better results around 23.20%. The proposed algorithm is more effective than the standard algorithms for detecting plant leaf diseases, as well as the reduction in cots in the computational power of computers.

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“…K-means clustering is used for segmentation of images, GLCM is also used for texture feature extraction, comprising the mean for color feature extraction and KNN, Neural Network, and lastly SVM is for the final analysis of disease. [1] Paper by Raghottam Kulkarni and Dr. A.V Sutagundar titled Plant Leaf Disease Management System (IEEE 2017). The main aim of the system was to analyze the plant species from its leaf and classify whether the leaf is in good physical condition or not and then if the leaf is detrimental, it warns about the disease the plant has and if it is in a good physical condition then it shows it is normal.…”

Section: Introductionmentioning

confidence: 99%

Leaf Disease Detection of Cucurbits Using CNN

et al. 2021

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Identification of plant disease is tough in agribusiness arena. If it is inaccurate, there occurs a tremendous damage in the production and economical price. Leaf Disease detection requires huge amount of work, knowledge, processing time in plant disease. The most used and edible vegetable all over the world is from cucurbitaceous family. The crops under this family have great economic value in the food industry and its production is done in large scale. This family consists of 965 species. If any of these plants catch disease then there would be a tremendous loss in the production of this field yields. Thus, treating them at early stage is best way to prevent such losses. Hence, Deep Learning Algorithm like CNN can be used to detect the diseases of the plants. The leaves of the plants would be used as primary material for identification of the disease, as they are much more visible on the leaves.

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Classification of Leaf Disease Using Texture Feature and Neural Network Classifier

Cited by 18 publications

References 7 publications

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Detection of Plant Leaf Diseases Using K?mean++ Intermeans Thresholding Algorithm

Leaf Disease Detection of Cucurbits Using CNN

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