Disease classification in aubergine with local symptomatic region using deep learning models

Rangarajan, Aravind Krishnaswamy; Raja, P.; Pérez-Ruiz, M.

doi:10.1016/j.biosystemseng.2021.06.014

Cited by 12 publications

(5 citation statements)

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“…The model attained an accuracy of 91% when estimating the severity of the condition. Reference [147] used a DCGAN to supplement the data on a dataset, including information about tea leaf diseases. They achieved an accuracy rate of 90% when building the classification model with the help of VGG16 and the tea disease dataset.…”

Section: ) Rq7: In What Ways Do Conventional Image Processing and Mac...mentioning

confidence: 99%

“…The AdaBoost classifier was then fed the extracted features to make its determination. Their model performed exceptionally well on the dataset, with 78.6%, 95.7%, and 86.3% on precision, recall, and F1-score, accordingly [147]. Studies like [149] focused on developing a method called FSL to diagnose plant diseases.…”

Section: ) Rq7: In What Ways Do Conventional Image Processing and Mac...mentioning

confidence: 99%

“…After cutting the images to 240 by 240 pixels, random cropping, rotating, shifting, resizing, horizontal and vertical flipping, and intensity transformation-based data augmentation [147], tea leaves were collected with a DJI Phantom 4 Pro UAV at 10 meters using a Canon EOS 80D SLR cam-era147], tea leaves were collected with a DJI Phantom 4 Pro UAV at 10 meters using a Canon EOS 80D SLR camera. Smartphones were used to collect multi-crop data in Spain and Germany [17].…”

Section: ) Rq9: Are Researchers Modeling Plant Disease Detection Tech...mentioning

confidence: 99%

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A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends

et al. 2023

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Plant pests and diseases are a significant threat to almost all major types of plants and global food security. Traditional inspection across different plant fields is time-consuming and impractical for a wider plantation size, thus reducing crop production. Therefore, many smart agricultural practices are deployed to control plant diseases and pests. Most of these approaches, for example, use vision-based artificial intelligence (AI), machine learning (ML), or deep learning (DL) methods and models to provide disease detection solutions. However, existing open issues must be considered and addressed before AI methods can be used. In this study, we conduct a systematic literature review (SLR) and present a detailed survey of the studies employing data collection techniques and publicly available datasets. To begin the review, 1349 papers were chosen from five major academic databases, namely Springer, IEEE Xplore, Scopus, Google Scholar, and ACM library. After deploying a comprehensive screening process, the review considered 176 final studies based on the importance of the method. Several crops, including grapes, rice, apples, cucumbers, maize, tomatoes, wheat, and potatoes, have tested mainly on the hyperspectral imagery and vision-centered approaches. Support Vector Machines (SVMs) and Logistic regression (LR) classifiers demonstrated an increased accuracy in experiments compared to traditional classifiers. Besides the image taxonomy, disease localization is depicted in these approaches as a bottle neck to disease detection. Cognitive CNNs with attention mechanisms and transfer learning are showing an increasing trend. There is no standard model performance assessment though the majority use accuracy, recall, precision, F1 Score, and confusion matrix. The available 11 datasets are laboratory and in-field based, and 9 are publicly available. Some laboratory-based datasets are considerably small, making them impractical in experiments. Finally, there is a need to avail models with fewer parameters, implementable on small devices and large datasets accommodating several crops and diseases to have robust models.INDEX TERMS Convolutional neural networks, deep learning, image processing, machine learning, plant disease detections.

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Section: ) Rq7: In What Ways Do Conventional Image Processing and Mac...mentioning

confidence: 99%

Section: ) Rq7: In What Ways Do Conventional Image Processing and Mac...mentioning

confidence: 99%

Section: ) Rq9: Are Researchers Modeling Plant Disease Detection Tech...mentioning

confidence: 99%

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A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends

et al. 2023

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“…In the crop disease detection domain [12] a number of deep learning techniques have been demonstrating considerate potential in image classification. These algorithms have been proposed towards the classification of diseases in crops such as apples [16], tomato [17], tea [18], aubergine [19] and wheat [20. The works of [12], [21] have demonstrated the potential of CNN's towards crop disease detection by using two of the most popular architectures: AlexNet [22] and GoogLeNet [23] using as input 54.306 images of the PlantVillage [24] dataset that are distributed into 26 diseases types across 14 crop plants species. In an innovative approach, rather than resizing images to a reduced size and training a standard forward model end-to-end, DeChant et al [25] created a pipeline of several CNNs that had prediction combine into heat maps that were fed into a final CNN trained to classify the presence of leaf blight in maize plants.…”

Section: Introductionmentioning

confidence: 99%

From Lab to Field: An Empirical Study on the Generalization of Convolutional Neural Networks towards Crop Disease Detection

Guth

Ward

McDonnell

2023

EJENG

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Due to complex feature abstraction and learning power, CNNs have been the most successful machine learning algorithms for image classification tasks. The objective of this work was to evaluate the potential of convolutional neural networks (CNNs) for extracting underlying complex features and recognize these patterns towards the task of detecting healthy and diseased crop plants. The generalization of these algorithms was assessed on different situations of training and testing scenarios using images from controlled lab conditions and real field environments. Results have shown that when presented with sufficient data variability in training, englobing images with similar conditions faced in testing, the deep learning architectures delivered accurate results of over 90%. In contrast, the same architectures were not able to generalize the accuracy of training towards the detection of new unseen images that were not extracted in the same settings as the ones from the training set, delivering, in this case, a general accuracy of around 50%. The deployment of practical automated support systems for disease detection depends on the provision of robust datasets for training CNNs which contemplate the spectral variability conditions found in numerous crop cultivation environments encountered in diverse field sites across the globe.

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“…Alshammari et al [16] introduced the whale optimization algorithm to optimize an artificial neural network for automated detection of olive leaf diseases with a high accuracy of 98.9%. Rangarajan et al [17] first extracted local features of eggplant diseases using a multi-class support vector machine algorithm, which was combined with an optimized VGG16 model to obtain a recognition accuracy of 94.3%. Alsubai et al [18] proposed to first remove the image noise based on median filtering technique and use a modified convolutional neural networks (CNN) model based on Salp Swarm optimization to extract the features and the final gated loop unit outputs all the corresponding grape disease categories with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

TeenyNet: a novel lightweight attention model for sunflower disease detection

Zhong,

Tong

2023

Meas. Sci. Technol.

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Sunflower is one of the oilseed crops which is popularly and widely cultivated globally and contributes significantly to human health. Leaf diseases of sunflower seriously affect the growth and yield of sunflower, which directly leads to the loss of agricultural economy. However, existing machine learning algorithms and deep learning techniques are mainly based on large models with attention mechanisms, which lack considerations in computational performance, especially model size. Therefore, this study proposes a lightweight model called TeenyNet to break through the dilemma. First, the designed global multi-frequency feature extraction module decomposes the image to extract multi-frequency multi-scale features. Then, a parameter-free maximum pooling layer further extracts edge and texture features and simplifies the network complexity through downsampling, after which the proposed lightweight dual fusion attention and multi-branching structure fuses all the feature vectors to enhance multidimensional feature learning and accelerate the model convergence. Finally, the fully connected linear layer solves the multi-classification problem of sunflower disease under natural illumination background conditions. The experimental results show that TeenyNet obtains the highest accuracy of 98.94% for sunflower disease recognition with a minimum size of 143 KB and has better recognition performance in comparison experiments. TeenyNet can be effectively used for the detection of sunflower leaf diseases to achieve disease prevention and control.

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Disease classification in aubergine with local symptomatic region using deep learning models

Cited by 12 publications

References 20 publications

A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends

A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends

From Lab to Field: An Empirical Study on the Generalization of Convolutional Neural Networks towards Crop Disease Detection

TeenyNet: a novel lightweight attention model for sunflower disease detection

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