A Review on Advances in Automated Plant Disease Detection

Bhagwat, Radhika; Dandawate, Yogesh H.

doi:10.46604/ijeti.2021.8244

Cited by 11 publications

(13 citation statements)

References 56 publications

(66 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the proposed approach, the SVM model classified RMFD with 99.64%, SMFD with 99.49%, and LWF with 100% accuracy. Bhagwat et al [26] presented a review of traditional machine learning and deep learning techniques for early and accurate detection of plant diseases. Early detection of plant diseases is a challenging task that needs to be addressed for precision Training a deep learning model with large data of diverse examples is highly desirable to avoid over-fitting.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA

Sharma

Singh

2022

Adv. technol. innov.

View full text Add to dashboard Cite

In image processing, developing efficient, automated, and accurate techniques to classify images with varying intensity level, resolution, aspect ratio, orientation, contrast, sharpness, etc. is a challenging task. This study presents an integrated approach for image classification by employing transfer learning for feature selection and using principal component analysis (PCA) for feature reduction. The PCA algorithm is employed for reducing the dimensionality of the features extracted by the VGG16 model to obtain a handful of features for speeding up image reorganization. For multilayer perceptron classifiers, support vector machine (SVM) and random forest (RF) algorithms are used. The performance of the proposed approach is compared with other classifiers. The experimental results establish the supremacy of the VGG16-PCA-Multilayer perceptron model integrated approach and achieve a reorganization accuracy of 91.145%, 95.0%, 92.33%, and 98.59% on Fashion-MNIST dataset, ORL dataset of faces, corn leaf disease dataset, and rice leaf disease datasets, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA

Sharma

Singh

2022

Adv. technol. innov.

View full text Add to dashboard Cite

show abstract

“…They face many problems yearly as they try to raise their healthy harvests to keep the profits from farming and raising livestock. Bhagwat and Dandawate [3] mentioned some countries have a gross domestic product (GDP) of up to 25%.…”

Section: Introductionmentioning

confidence: 99%

An Improved MobileNet for Disease Detection on Tomato Leaves

Nguyen

Luong

Huynh

et al. 2023

Adv. technol. innov.

View full text Add to dashboard Cite

Tomatoes are widely grown vegetables, and farmers face challenges in caring for them, particularly regarding plant diseases. The MobileNet architecture is renowned for its simplicity and compatibility with mobile devices. This study introduces MobileNet as a deep learning model to enhance disease detection efficiency in tomato plants. The model is evaluated on a dataset of 2,064 tomato leaf images, encompassing early blight, leaf spot, yellow curl, and healthy leaves. Results demonstrate promising accuracy, exceeding 0.980 for disease classification and 0.975 for distinguishing between diseases and healthy cases. Moreover, the proposed model outperforms existing approaches in terms of accuracy and training time for plant leaf disease detection.

show abstract

“…Therefore, hyperspectral imaging plays an important role in non-destructive detection of plant diseases. However, the high cost of devices is one of the important limitation factors for the usage of hyperspectral imaging technology (Bhagwat and Dandawate, 2021). Consequently, low-cost and non-destructive detection technology is the current research direction.…”

Section: Introductionmentioning

confidence: 99%

An automatic fluorescence phenotyping platform to evaluate dynamic infection process of Tobacco mosaic virus-green fluorescent protein in tobacco leaves

Song

Gao

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

Tobacco is one of the important economic crops all over the world. Tobacco mosaic virus (TMV) seriously affects the yield and quality of tobacco leaves. The expression of TMV in tobacco leaves can be analyzed by detecting green fluorescence-related traits after inoculation with the infectious clone of TMV-GFP (Tobacco mosaic virus - green fluorescent protein). However, traditional methods for detecting TMV-GFP are time-consuming and laborious, and mostly require a lot of manual procedures. In this study, we develop a low-cost machine-vision-based phenotyping platform for the automatic evaluation of fluorescence-related traits in tobacco leaf based on digital camera and image processing. A dynamic monitoring experiment lasting 7 days was conducted to evaluate the efficiency of this platform using Nicotiana tabacum L. with a total of 14 samples, including the wild-type strain SR1 and 4 mutant lines generated by RNA interference technology. As a result, we found that green fluorescence area and brightness generally showed an increasing trend over time, and the trends were different among these SR1 and 4 mutant lines samples, where the maximum and minimum of green fluorescence area and brightness were mutant-4 and mutant-1 respectively. In conclusion, the platform can full-automatically extract fluorescence-related traits with the advantage of low-cost and high accuracy, which could be used in detecting dynamic changes of TMV-GFP in tobacco leaves.

show abstract

A Review on Advances in Automated Plant Disease Detection

Cited by 11 publications

References 56 publications

An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA

An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA

An Improved MobileNet for Disease Detection on Tomato Leaves

An automatic fluorescence phenotyping platform to evaluate dynamic infection process of Tobacco mosaic virus-green fluorescent protein in tobacco leaves

Contact Info

Product

Resources

About