Automatic identification of diseases in grains crops through computational approaches: A review

Manavalan, R.

doi:10.1016/j.compag.2020.105802

Cited by 59 publications

(21 citation statements)

References 114 publications

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“…R Manavalan et al [ 25 ] proposed a study to explore the association between COVID-19 transmission rates and meteorological parameters by implementing a gradient boosting model (GBM) on Indian data. GBM model was optimized after tuning its parameters.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 detection using federated machine learning

2021

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The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient’s private data violates patient privacy and traditional machine learning model requires accessing or transferring whole data to train the model. In recent years, there has been increasing interest in federated machine learning, as it provides an effective solution for data privacy, centralized computation, and high computation power. In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model)using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID-19 patients. During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models’ loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.

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Section: Related Workmentioning

confidence: 99%

COVID-19 detection using federated machine learning

2021

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“…Computer vision systems in food and agricultural products industries have been applied in the areas of quality inspection, grading, insect infestation detection, damage detection and disease detection [5,6]. These systems can provide an accurate, accelerated,, no subjective and non-destructive metrics tools [7].…”

Section: Related Workmentioning

confidence: 99%

“…These systems can provide an accurate, accelerated,, no subjective and non-destructive metrics tools [7]. More specifically, computer vision in food grains industries has been applied in the areas of sorting, grading, examining the effect of humidity, detection of defects such as insect and pest detection, microbial and fungi infection, and foreign matter [6].…”

Section: Related Workmentioning

confidence: 99%

Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features

Ali¹,

Mohammed²,

Nour³

2021

Kafrelsheikh Journal of Information Sciences

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The goal of this research is to compare between the performance of the traditional machine learning classification algorithm using Bag of Visual Words (BoVW) method and off-the-shelf deep features extracted by VGG-19, and Inception-V3 models and trained SVMs using the extracted features. By comparing the AUC, sensitivity, and specificity of SVM with VGG-19 and Inception-V3, we can conclude that off-the-shelf deep features has an important impact on food grains image classification.

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“…Fruit trees play an important role in any state's economic development. One of the most well-known fruit plant species is the citrus plant, which is high in vitamin C and widely used in the Indian sub-Continent, Middle East and Africa [2]. Citrus plants are associated with many health advantages, as well as being used as a raw material in the agricultural industry for the production of several types of other agri-products including jams, sweets, ice cream, and confectionery etc.…”

Section: Introductionmentioning

confidence: 99%

“…Citrus plants are associated with many health advantages, as well as being used as a raw material in the agricultural industry for the production of several types of other agri-products including jams, sweets, ice cream, and confectionery etc. [2,3]. Citrus, Pakistan's most important fruit crop, accounts for a significant portion of the country's horticultural exports.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

et al. 2021

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Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. The proposed CNN model extracts complementary discriminative features by integrating multiple layers. The CNN model was checked against many state-of-the-art deep learning models on the Citrus and PlantVillage datasets. The experimental results indicate that the CNN Model outperforms the competitors on a number of measurement metrics. The CNN Model has a test accuracy of 94.55 percent, making it a valuable decision support tool for farmers looking to classify citrus fruit/leaf diseases.

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Automatic identification of diseases in grains crops through computational approaches: A review

Cited by 59 publications

References 114 publications

COVID-19 detection using federated machine learning

COVID-19 detection using federated machine learning

Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

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