Leaf image analysis-based crop diseases classification

Kurmi, Yashwant; Gangwar, Suchi; Agrawal, Dheeraj; Kumar, Satrughan; Srivastava, Hari Shanker

doi:10.1007/s11760-020-01780-7

Cited by 39 publications

(13 citation statements)

References 41 publications

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“…Kurmi et al [33] have the localization-specific classification method classifies the leaf images of three crops to identify different crops. Pathological features of the image, such as localized image dots and leaf area, allow the keys to be separated from healthy images that can be easily restored by obtaining a Fisher vector.…”

Section: Research Gapmentioning

confidence: 99%

“…The big challenge here is to increase productivity in natural conditions. The main weakness of the SVM classifier is its poor accumulation rate [21] - [33] which causes local minimal and flat point problems. In the following variation, instead of adding the output sound to the network, the network grows in both positive and negative directions.…”

Section: Research Gapmentioning

confidence: 99%

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An automatic IoT based crop disease detection technique for early warning system using hybrid soft computing techniques

Chandrasekar

Pandian

Selvaganesh

2023

Preprint

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The Indian economy is directly dependent on agricultural production. As the world grows and moves rapidly, identifying crop leaf diseases plays an important role in agriculture. Most of them are related to automation, and you also need to maintain the crop leaf with a little automation, and each crop has a specific need. To survive, it must be met, so companies need to develop systems that can communicate with their users. Currently, precision agriculture is being introduced to increase yields using the latest cutting-edge agriculture technology. An automatic disease detection system used to make instant and accurate decisions about plant diseases for farmers. This speeds up the diagnostic process. But in recent episodes, the show seemed a bit out of focus. One of these methods does not work well with the most important method, system diagnostics. In this paper, we propose an IoT based disease detection technique for crop using hybrid soft computing techniques (CDD-HSC). First, we segment the disease area from test leaf image using improved sunflower optimization (ISO) algorithm which is an important aspect for disease classification. Second, we introduce a multi-swarm snake optimization (MSSO) algorithm for optimal feature selection among multiple features in feature extraction stage. Then, we illustrates coach-learning induced capsule neural network (CL-CNN) for diseases classification in crop leaf with multi-classes. IoT concept used to transfer classification results to the corresponding former through mobile for immediate prevention in crop leaf diseases detecting, which limits the unwanted human delay. Finally, the performance of proposed CDD-HSC technique can analyze with different datasets and the results should sows the effectiveness of proposed method over existing methods in terms of accuracy, precision, F-measure and precision.

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Section: Research Gapmentioning

confidence: 99%

Section: Research Gapmentioning

confidence: 99%

An automatic IoT based crop disease detection technique for early warning system using hybrid soft computing techniques

Chandrasekar

Pandian

Selvaganesh

2023

Preprint

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“…Using the Support Vector Machine (SVM) approach, they were able to attain average accuracy of 0.93 and 0.97. Overall, the categorization approach provided by them outperforms state-ofthe-art techniques [5]. The detection of antimicrobial illnesses in bell pepper plants was studied by a group of researchers from the Universidade Federal de Viçosa in Brazil.…”

Section: Literature Surveymentioning

confidence: 99%

Image-based classification of healthy and diseased bell pepper using the transfer learning method

Dhanalakshmi¹,

Sivakumar

Kumari

et al. 2022

ijhs

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The bell pepper is a fruit that is often misunderstood as a vegetable due to its usage. The plant which grows bell pepper grows for about three to six feet. These plants do not require intensive care but some necessary steps have to be taken to increase the productivity of the plant. Plant diseases like bacterial spots can heavily affect growth. The consequences of such diseases can be reduced if they’re detected in their early stages. This research aims the comparison of two Deep Learning (DL) models which were developed using VGG-16 architecture and the AlexNet. For training and testing the efficiency of the models, images of the bell pepper plant’s leaf are obtained from Kaggle. This data acquired further undergoes some pre-processing techniques like resizing and rescaling to reach the desired format. The cleaned data is then split into three divisions. One of them is used to train both DL models and the second one is used to validate the models. The last part is used to test the accuracy and loss of the two models. In the end, the accuracy and loss of both the models are compared and the results are tabulated.

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“…As an important branch in the field of computer vision, image classification has been widely used in target recognition, defect detection and other application scenarios [1][2][3][4][5][6][7][8]. By the number of tags in the image, there are two types of image classification: single-label image classification and multi-label image classification.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Cluster Analysis Method for Large-Scale Multi-Label Images

Xu¹

2022

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Large-scale multi-label image classification requires determining the presence or absence of a target object in a large number of sample images. For highly specialized and complex multi-label image sets, it is especially important to ensure the accuracy of image classification. Traditional deep learning models usually don’t take into account image-label correlation constraints when classifying multi-label images, and the strategy of classifying images based only on their own features greatly limits the model performance. In this context, this paper focuses a deep learning-based cluster analysis method for large-scale multi-label images. We constructed a model for large-scale multi-label image category recognition, which consists of a global image feature extraction module, a feature activation vector generation module and an image category inter-label connection module. Using a graph convolutional network (GCN), we aggregated the information of image category label nodes in the constructed multi-label graph structure, while exploring the correlation between image category labels. A detailed description is presented on how to introduce the attention mechanism into the constructed model mentioned above for image category recognition. Experimental results have validated the effectiveness of the constructed model.

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Leaf image analysis-based crop diseases classification

Cited by 39 publications

References 41 publications

An automatic IoT based crop disease detection technique for early warning system using hybrid soft computing techniques

An automatic IoT based crop disease detection technique for early warning system using hybrid soft computing techniques

Image-based classification of healthy and diseased bell pepper using the transfer learning method

A Deep Learning-Based Cluster Analysis Method for Large-Scale Multi-Label Images

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