A Leaf Disease Detection Mechanism Based on L1-Norm Minimization Extreme Learning Machine

Dwivedi, Rudresh; Dutta, Tanima; Hu, Yu‐Chen

doi:10.1109/lgrs.2021.3110287

Cited by 18 publications

(11 citation statements)

References 17 publications

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“…This model used Kuan filtering for preprocessing and different feature computation. Benchmark plant dataset was used for evaluation which confirms that l1-ELM has optimal learning and better generalization [16] III. METHODOLOGY…”

Section: Related Workmentioning

confidence: 79%

Plant Disease Classification using Alex Net

Kewat¹

2023

Preprint

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—In modern agriculture field, pest and disease identification is a major role of crop cultivation. Image classification by the use of deep convolutional neural networks of training and methodology used the facilitate a quick and easy system implementation. Pests and diseases are a threat to plant production, especially in India, but identification remains to be a challenge in massive scale and automatically. Collecting images from Image Net dataset. The results show that we can effectively detect and recognize the rice diseases and pests including healthy plant class using a deep convolutional neural network, with the best accuracy of 96.50%. The significantly high success rate makes the model a really useful advisory or early warning tool, and an approach that would be further expanded to support an integrated plant disease identification system to work in real cultivation conditions.

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Section: Related Workmentioning

confidence: 79%

Plant Disease Classification using Alex Net

Kewat¹

2023

Preprint

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“…Here the detailed description of groundnut leaves and total number of leaves collected with various diseases occurred in the leaf with healthy leaf as on category. [20] explains about the disease-free plant growth which produces more productivity with ELM algorithm with normalization using the benchmark dataset and obtains the optimal learning and better generalization. The dataset is collected with the five types of diseases including healthy leaves to identify whether the leaves are affected by disease or not.…”

Section: Groundnut Datasetsmentioning

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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“…Globally, the incidence of plant disease epidemics is on the rise, posing a threat to food security in susceptible regions [ 1 ]. Data from the United Nations Food and Agriculture Organization survey data, pests and diseases destroy between 20% and 40% of worldwide crop yields each year [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Deep transfer learning with gravitational search algorithm for enhanced plant disease classification

Al-Gaashani,

Samee,

Alkanhel

et al. 2024

Heliyon

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A Leaf Disease Detection Mechanism Based on L1-Norm Minimization Extreme Learning Machine

Cited by 18 publications

References 17 publications

Plant Disease Classification using Alex Net

Plant Disease Classification using Alex Net

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

Deep transfer learning with gravitational search algorithm for enhanced plant disease classification

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