Plant disease identification from individual lesions and spots using deep learning

Barbedo, Jayme Garcia Arnal

doi:10.1016/j.biosystemseng.2019.02.002

Cited by 506 publications

(210 citation statements)

References 16 publications

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“…The classification results of biotic stress were consistent for most of the stresses except cercospora leaf spot which presented a considerable amount of classification errors. This result corroborates with the experiments carried out in Barbedo (2019) whose class with the largest number of samples misclassified was also the cercospora leaf spot. These misclassifications may be associated with similarity with other diseases and also with the dataset imbalance.…”

Section: Leaf Dataset Resultssupporting

confidence: 91%

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Deep learning for classification and severity estimation of coffee leaf biotic stress

Esgario

Krohling

Ventura

2020

Computers and Electronics in Agriculture

222

109

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Biotic stress consists of damage to plants through other living organisms. Efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increase on productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. The experimental results obtained for classification as well as for severity estimation indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic stresses in coffee plantations.

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Section: Leaf Dataset Resultssupporting

confidence: 91%

“…A total of 2147 symptom images were cropped. In addition to our images, 575 images made available by Barbedo (2019) were added to our dataset, accounting for 2722 symptom images. Fig.…”

Section: Image Datasetmentioning

confidence: 99%

Deep learning for classification and severity estimation of coffee leaf biotic stress

Esgario

Krohling

Ventura

2020

Computers and Electronics in Agriculture

222

109

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“…CNN have achieved state-of-the-art recognition accuracies in many classification problems including plant disease detection [13], cancer [14,15], and skin burns assessment [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar¹,

Ajuji²,

Yahya³

2020

Preprint

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities, and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use pre-trained deep learning models due to deficient dataset to train a new model from scratch. Experiments were extensively conducted using three stateof-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies: fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers, and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy of approximately 99.9%.

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“…In addition, if the seeds are not severely wet (seed color does not change and seeds do not swell), the relative humidity has no influence on the method of hyperspectral imaging combined deep learning. At present, deep networks have been successfully applied to plant disease identification [36][37][38], drought monitoring [39], land type classification [40], weed detection [41], and other areas of agriculture. To date, there are few reports on the identification of soybean seed varieties by deep learning, and whether it has advantages that is also unknown.…”

mentioning

confidence: 99%

A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning

et al. 2019

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Convolutional neural network (CNN) can be used to quickly identify crop seed varieties. 1200 seeds of ten soybean varieties were selected, hyperspectral images of both the front and the back of the seeds were collected, and the reflectance of soybean was derived from the hyperspectral images. A total of 9600 images were obtained after data augmentation, and the images were divided into a training set, validation set, and test set with a 3:1:1 ratio. Pretrained models (AlexNet, ResNet18, Xception, InceptionV3, DenseNet201, and NASNetLarge) after fine-tuning were used for transfer training. The optimal CNN model for soybean seed variety identification was selected. Furthermore, the traditional machine learning models for soybean seed variety identification were established by using reflectance as input. The results show that the six models all achieved 91% accuracy in the validation set and achieved accuracy values of 90.6%, 94.5%, 95.4%, 95.6%, 96.8%, and 97.2%, respectively, in the test set. This method is better than the identification of soybean seed varieties based on hyperspectral reflectance. The experimental results support a novel method for identifying soybean seeds rapidly and accurately, and this method also provides a good reference for the identification of other crop seeds.

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Plant disease identification from individual lesions and spots using deep learning

Cited by 506 publications

References 16 publications

Deep learning for classification and severity estimation of coffee leaf biotic stress

Deep learning for classification and severity estimation of coffee leaf biotic stress

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning

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