Convolutional Neural Networks for the Automatic Identification of Plant Diseases

Boulent, Justine; Foucher, Samuel; Théau, Jérôme; St-Charles, Pierre-Luc

doi:10.3389/fpls.2019.00941

Cited by 261 publications

(141 citation statements)

References 46 publications

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“…Of the six models, NASNetLarge performed the best with 54 misjudgments (Figure 2), and among them, Nannong 1606 had the most misjudgments while Shangdou 1201 and Zheng 3074 had the least misjudgments. According to the study, the greater the number of deep neural network layers, the better the performance of the identification models, and the conclusion is the same in other fields [49][50][51]. However, there are also different conclusions [46,[52][53][54], as there are several factors that cause this situation, including dataset, network depth, network width, network structure, and parameter settings, and overall, the deep network is indeed slightly better than the shallow network.…”

Section: Test Resultsmentioning

confidence: 91%

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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Section: Test Resultsmentioning

confidence: 91%

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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“…Visualisation techniques observe whether the classifier has selected all the areas of disease in the given image without being affected by the background or noise. In [ 35 , 88 ], the occlusion technique was shown to have an issue in identifying whether a pixel represented symptoms of the right class or part of the background. Additionally, in [ 80 ], it was found that the occlusion technique was time-consuming.…”

Section: Feature Representation In Deep Classifiersmentioning

confidence: 99%

“…The identification process in classifiers is based on how ROIs are localised. Many techniques have been discussed in relation to this issue, including segmentation, object detection and hybrid methods that supply classifiers with contextual information related to the ROI and thereby affect their performance [ 35 ].…”

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

144

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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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“…In [15], Boulent et al use three types of data sets depending on their complexity in the automatic identification of plant diseases: The first image samples are captured under controlled situations where the leaves are placed on the same background and brightness. The second image samples are taken under unrestrained conditions and focus is made on specific plant organ.…”

Section: Review Of Related Workmentioning

confidence: 99%

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Peter¹,

Damuut²,

Abdulkadir³

2020

AJNNA

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In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of pasture with resultant conflicts with local farm communities in the region. The inability to find a lasting solution to these problems has led to persistent cases of deteriorating relationships amongst crop farmers and herders which has continued to precipitate hostile consequences including the loss of lives, interruption and annihilation of the family units and in some cases, whole communities are destroyed. This research attempts to address the problem of inadequate grazing resources by the use of advances in Computational Intelligence Techniques in the determination of the optimum maturity of maize, so as to complement for the grazing of livestock in the region. Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. This paper leverages on the use of Artificial Neural Networks (ANN) interfaced with image processing and Convolutional Neural Networks (pre-trained ResNet50 Network) in determining the optimum ripeness of the maize crop grown in Sub-Saharan Africa. Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. Therefore, this research posits that farmers could be sensitized on the possibility of utilizing image processing and neural networks technique in the determination of the maturity of maize in the nearest future when made operational.

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Convolutional Neural Networks for the Automatic Identification of Plant Diseases

Cited by 261 publications

References 46 publications

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

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

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

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

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