H2K – A robust and optimum approach for detection and classification of groundnut leaf diseases

Devi, K. Suganya; Srinivasan, Padmini; Bandhopadhyay, Sivaji

doi:10.1016/j.compag.2020.105749

Cited by 29 publications

(9 citation statements)

References 5 publications

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“…The author [18] created the method for precise detection and classification of groundnut leaf diseases, the method which is proposed by the author, algorithms named H2K which combines the strengths of the three-concept called Harris corner detector, the HOG, and the K-Nearest Neighbor classifier.…”

Section: B Classification Of Leaf Diseasesmentioning

confidence: 99%

Leaf Diseases Identification and Classification of Self-Collected Dataset on Groundnut Crop using Progressive Convolutional Neural Network (PGCNN)

Anbumozhi¹,

A²

2023

IJACSA

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A healthy crop is required to provide high-quality food for daily consumption. Crop leaf diseases have more influence on agronomic production and our country. Earlier, many scholars relied on traditional techniques to detect and classify leaf diseases. Furthermore, classification at an early stage is impossible when there are not enough experts and inadequate research facilities. As technology progresses into our day to day life, an Artificial Intelligence subset called Deep Learning (DL) models plays a vital role in the automatic identification of groundnut leaf diseases. The essential for controlling diseases that are spread to the healthy development of groundnut farming. Deep Learning can resolve the issues of finding leaf diseases early and effectively. Most of the researchers concentrate on detecting leaf diseases by doing research in Machine Learning (ML) approaches, which leads to low accuracy and high loss. To achieve better accuracy and decreases the loss in the DL model by identifying the leaf diseases of groundnut crops at an early stage, we propose the Progressive Groundnut Convolutional Neural Network (PGCNN) model. This paper mainly focuses on identifying and classifying groundnut leaf diseases with a self-collected dataset which is collected from the various climatic conditions around the village located nearby Pudukkottai district, Tamil Nadu, India. The common diseases that occurred in those areas were gathered namely early spot, late spot, rust, and rosette. Model Performance metrics analysis was done to evaluate the model performance and also compared with the various DL architectures like AlexNet, VGG11, VGG13, VGG16, and VGG19. The proposed models have achieved a training accuracy of 99.39% and a validation accuracy of 97.58%, continuing with an overall accuracy of 97.58%.

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Section: B Classification Of Leaf Diseasesmentioning

confidence: 99%

Leaf Diseases Identification and Classification of Self-Collected Dataset on Groundnut Crop using Progressive Convolutional Neural Network (PGCNN)

Anbumozhi¹,

A²

2023

IJACSA

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“…An improved RPN network detects and removes negative anchors, which reduces search engine rankings and provides better initial information to identify the network. Devi et al [26] presented a picture-based paper on automatic diagnosis and classification of peanut leaf diseases. H2K is a special method for accurately controlling and distributing the Harris angle detector, HOG (histogram-based gradient), and leaf peanut diseases.…”

Section: Related Workmentioning

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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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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“…A basic technique for groundnut leaf disease categorization was discussed in [18]. Along with the late blight spot, it was able to categorise five main leaf diseases.…”

Section: Literature Surveymentioning

confidence: 99%

CRUN-Based Leaf Disease Segmentation and Morphological-Based Stage Identification

Sujithra

Ukrit

2022

Mathematical Problems in Engineering

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Natural growth is eliminated by the process of globalization in today’s globe owing to the development of technology and landscapes. The majority of today’s youngsters, as well as our seniors, lack an appropriate understanding of natural species such as plant names, tree names, and medicinal plants. This is attributed to technological advancements and a decline in gardening interest. To close this gap, horticulture may employ technology that aids in the improvement of plant understanding and growth. This method is implemented in the existing system for diagnosing leaf diseases using image processing and machine learning techniques. In the existing process, the classification of leaf disease is performed using image processing steps, such as preprocessing, segmentation, feature extraction, feature reduction, and classification. Even though it utilizes multiple processing steps and region-based classification, it identifies only the type of disease. In this paper, a combined approach of regional-based convolutional neural networks and U-Net (CRUN) is proposed for segmenting the leaf diseases from the augmented leaf dataset. Then, the segmented images are subjected to a morphological process to identify the level of disease in the leaf. This identification helps to identify the leaf’s nature and suggests a process to reduce the disease’s spread to other leaves through the proper use of fertilizers. The proposed method is applied to real-time images of sugarcane leaf diseases, such as bacterial blight and red rot, and banana leaf diseases, such as yellow and black sigatoka. This method is also applied to public sugarcane and banana leaf datasets from the Kaggle website. The proposed CRUN algorithm effectively segments the disease region. The morphological process helps to identify the disease level and protect the plant from further spread of disease. As a result, the proposed CRUN and morphological tests are most effective for automating leaf disease detection and prevention.

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H2K – A robust and optimum approach for detection and classification of groundnut leaf diseases

Cited by 29 publications

References 5 publications

Leaf Diseases Identification and Classification of Self-Collected Dataset on Groundnut Crop using Progressive Convolutional Neural Network (PGCNN)

Leaf Diseases Identification and Classification of Self-Collected Dataset on Groundnut Crop using Progressive Convolutional Neural Network (PGCNN)

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

CRUN-Based Leaf Disease Segmentation and Morphological-Based Stage Identification

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