Performance analysis of deep learning CNN models for disease detection in plants using image segmentation

Sharma, Parul; Berwal, Yash Paul Singh; Ghai, Wiqas

doi:10.1016/j.inpa.2019.11.001

Cited by 227 publications

(130 citation statements)

References 23 publications

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“…Their advantages over self-acquired images include thousands of labeled image data, multiple varieties of crop species, and different aspects of plant diseases at various levels of severity. This warrants the adoption of available datasets by several studies as a benchmark image database in this context [34][35][36]. The PV dataset collects images of diseased and healthy plant leaves spread across 38 assigned labels, each with disease pair (diseased or healthy).…”

Section: Research Methods 21 Experimental Set-up and Datasetmentioning

confidence: 99%

Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning

Abdu

Mokji

Sheikh

2020

IJ-AI

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Image-based plant disease detection is among the essential activities in precision agriculture for observing incidence and measuring the severity of variability in crops. 70% to 80% of the variabilities are attributed to diseases caused by pathogens, and 60% to 70% appear on the leaves in comparison to the stem and fruits. This work provides a comparative analysis through the model implementation of the two renowned machine learning models, the support vector machine (SVM) and deep learning (DL), for plant disease detection using leaf image data. Until recently, most of these image processing techniques had been, and some still are, exploiting what some considered as "shallow" machine learning architectures. The DL network is fast becoming the benchmark for research in the field of image recognition and pattern analysis. Regardless, there is a lack of studies concerning its application in plant leaves disease detection. Thus, both models have been implemented in this research on a large plant leaf disease image dataset using standard settings and in consideration of the three crucial factors of architecture, computational power, and amount of training data to compare the duos. Results obtained indicated scenarios by which each model best performs in this context, and within a particular domain of factors suggests improvements and which model would be more preferred. It is also envisaged that this research would provide meaningful insight into the critical current and future role of machine learning in food security

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Section: Research Methods 21 Experimental Set-up and Datasetmentioning

confidence: 99%

Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning

Abdu

Mokji

Sheikh

2020

IJ-AI

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“…Recent advances in computer vision enable us to analyze each pixel of an image. For identifying the pixel region as an individual fruit, leaf, flower or twig, image segmentation approaches are required [76,77]. Some of the image-segmentation approaches for machine vision system have been reviewed in this article.…”

Section: Crop Image Segmentationmentioning

confidence: 99%

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

et al. 2021

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Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

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“…low coefficient value means low dispersion. Whereas, Q 3 represents the observations that have upper quartile, Q 1 represents the observations that have lower quartile [ 30 ].…”

Section: Evaluation Measuresmentioning

confidence: 99%

“…The detection of multiple infections in single or multiple leaves is another challenge in this field. One study [ 30 ] proposed two CNN models: one trained using full leaf sample images, and the second trained using segmented leaf samples containing different symptoms from the same training dataset of the first model. The findings indicate that the second model showed superior performance over the first model based on measurements of both final classification accuracy and the Quartile Coefficient of Dispersion (QCoD) of confidence difference between the models.…”

Section: Introductionmentioning

confidence: 99%

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

151

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Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

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Performance analysis of deep learning CNN models for disease detection in plants using image segmentation

Cited by 227 publications

References 23 publications

Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning

Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

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