Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Hassan, Sk Mahmudul; Maji, Arnab Kumar; Jasiński, Michał; Leonowicz, Zbigniew; Jasińska, Elżbieta

doi:10.3390/electronics10121388

Cited by 275 publications

(111 citation statements)

References 51 publications

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“…ResNet50 showed the lowest accuracy for all the datasets and was about 20% less accurate than GoogLeNet using the top view dataset in the test step. In previous studies, ResNet50 showed a relatively low performance to classify homogenous or highly similar images (Rudakov et al, 2018;Hassan et al, 2021). Therefore, the low accuracy of ResNet50 in our experiments might have been caused by a high degree of similarity among the images.…”

Section: Discussionmentioning

confidence: 61%

Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images

Jung

Song²,

Hong

et al. 2021

Front. Plant Sci.

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Efficient and accurate methods of analysis are needed for the huge amount of biological data that have accumulated in various research fields, including genomics, phenomics, and genetics. Artificial intelligence (AI)-based analysis is one promising method to manipulate biological data. To this end, various algorithms have been developed and applied in fields such as disease diagnosis, species classification, and object prediction. In the field of phenomics, classification of accessions and variants is important for basic science and industrial applications. To construct AI-based classification models, three types of phenotypic image data were generated from 156 Brassica rapa core collections, and classification analyses were carried out using four different convolutional neural network architectures. The results of lateral view data showed higher accuracy compared with top view data. Furthermore, the relatively low accuracy of ResNet50 architecture suggested that definition and estimation of similarity index of phenotypic data were required before the selection of deep learning architectures.

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Section: Discussionmentioning

confidence: 61%

Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images

Jung

Song²,

Hong

et al. 2021

Front. Plant Sci.

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“…The dataset used are taken from Central Punjab and Pakistan which can represent a specific region and plant diseases have a significant environmental factor and the model would not outperform in datasets that collected in other regions. In (20) plant disease identification using transfer learning and CNN is conducted in which authors replace the standard convolution with separable convolution to minimize the number of parameters that is trained on 14 various plant species and 38 different disease classes. The implemented model achieves 99.56%, 98.42%, 99.11%, and 97.02% using EfficientNetB0, inception-v3, inceptionResNetV2, and MobileNetV2 respectively.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Potato Plant Leaf Diseases Identification Using Transfer Learning

Gardie¹,

Asemie²,

Azezew³

et al. 2022

IJST

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Background/Objectives: Agriculture is a major food source for Ethiopian population. Plant diseases contribute a great production loss, which can be addressed with continuous monitoring. Early plant disease identification using computer vision and Artificial Intelligence (AI) helps the farmers to take preventive course of action to increase production quality. Manual plant disease identification is strenuous and error-prone. Methods: In this study, we present a convolutional neural network architecture inception-v3 model to detect potato leaf diseases using a deep learning-based transfer learning technique. We used separable convolution in the inception block that can minimize the number of parameters by an outsized margin and to utilize resource efficiently. The inception-V3 model have a higher training accuracy and needs less training time than the main CNN architecture, as the used parameters are fewer. Findings: In this study, there is an improvement on the little noisy on sample images which leads to misidentification of diseases. In our experiment, we have used an RGB color channel image dataset to train model, which yields an overall accuracy performance of 98.7% on the heldout test set. Novelty: In order to identify potato leave diseases, we conducted transfer learning for high performance classification with pixel-wise operation to enhance the number of leaf images. A model based on inception-v3 transfer learning approach is presented in this study for disease identification of potato leave images, thus provide an effective computer-aided recognition model for potato disease classification in the absence of large data.

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“…Many research studies related to crop management found that different variants of deep learning models (InceptionV3, InceptionResNetV2, EfficientNet, MobileNet, DenseNet and others) performed better in terms of accuracy and training time [34]. Furthermore, studies showed that when the proper parameters were employed, the MobileNetV2 architecture was found to be compatible with mobile devices [35]. Table 1 summarizes the state of art research in the domain of nutrient deficiency identification in plants using various CNN based architectures.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Sharma¹,

Nath

Sharma

et al. 2022

Electronics

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Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.

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Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Cited by 275 publications

References 51 publications

Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images

Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images

Potato Plant Leaf Diseases Identification Using Transfer Learning

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

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