Lightweight Inception Networks for the Recognition and Detection of Rice Plant Diseases

Chen, Junde; Chen, Weirong; Zeb, Adnan; Yang, Shuangyuan; Zhang, Defu

doi:10.1109/jsen.2022.3182304

Cited by 61 publications

(22 citation statements)

References 38 publications

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“…In the future, the fusion process will be optimized for less computational time by employing new techniques. Moreover, in the future, plant and rice disease detection and classifcation problems will be considered [43][44][45][46][47].…”

Section: Discussionmentioning

confidence: 99%

Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification

Fatima

Khan

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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Image processing is an important domain for identifying various crop varieties. Due to the large amount of rice and its varieties, manually detecting its qualities is a very tedious and time-consuming task. In this work, we propose a two-stage deep learning framework for detecting and classifying multiclass rice grain varieties. A series of steps is included in the proposed framework. The first step is to perform preprocessing on the selected dataset. The second step involves selecting and fine-tuning pretrained deep models from Darknet19 and SqueezeNet. Transfer learning is used to train the fine-tuned models on the selected dataset. The 50% sample images are employed for the training and rest 50% are used for the testing. Features are extracted and fused using a maximum correlation-based approach. This approach improved the classification performance; however, redundant information has also been included. An improved butterfly optimization algorithm (BOA) is proposed, in the next step, for the selection of the best features that are finally classified using several machine learning classifiers. The experimental process was conducted on selected rice datasets that include five types of rice varieties and achieves a maximum accuracy of 100% that was improved than the recent method. The average accuracy of the proposed method is obtained at 99.2%, through confidence interval-based analysis that shows the significance of this work.

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Section: Discussionmentioning

confidence: 99%

Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification

Fatima

Khan

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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“…Experiments indicated that the model achieved a good balance between memory efficiency and recognition accuracy. Junde Chen et al designed a convolutional neural network called MobInc-Net for rice disease recognition (Chen J. et al, 2022). To reduce the complexity of the model, the original convolution methods were replaced by deep convolution and point convolution.…”

Section: Figurementioning

confidence: 99%

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

Ning,

Liu,

Zhu

et al. 2023

Front. Plant Sci.

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There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human experience. But the method often could not achieve the desired effect, and was difficult to popularize on a large scale. Convolutional neural networks are good at extracting localized features from input data, converting low-level shape and texture features into high-level semantic features. Models trained by convolutional neural network technology based on existing data can extract common features of data and make the framework have generalization ability. Applying ensemble learning or transfer learning techniques to convolutional neural network can further improve the performance of the model. In recent years, convolutional neural network technology has been applied to the automatic recognition of rice diseases, which reduces the manpower burden and ensures the accuracy of recognition. In this paper, the applications of convolutional neural network technology in rice disease recognition are summarized, and the fruitful achievements in rice disease recognition accuracy, speed, and mobile device deployment are described. This paper also elaborates on the lightweighting of convolutional neural networks for real-time applications as well as mobile deployments, and the various improvements in the dataset and model structure to enhance the model recognition performance.

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“…Both the Plant Village dataset, which contains 55,448 images, and the expanded dataset, which has 61,486 images, were used to train the model. In the studies proposed in [8,9,10,11,12,13 [14,15,16,17,18] categorized numerous leaf diseases (SSD). Images of diseases seen on the leaves of commercially produced banana, sugarcane, cotton, potato, brinjal, carrot, Chile, rice, and wheat are included in this collection.…”

Section: Literature Surveymentioning

confidence: 99%

ECDBMFA: Design of an Ensemble engine for Crop Disease detection via Bioinspired Multidomain Feature Analysis

Malathi¹,

Sheela²

2023

Preprint

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Crop disease detection models require integration of multimodal tasks including contextual segmentation, feature representation & selection, classification & post-processing operations. A wide variety of machine learning & deep learning techniques are proposed by researchers to design such models, but most of them are either highly complex, or have limited efficiency when applied to real-time farm fields. To overcome these issues, this text proposes design of an efficient & novel ensemble engine for crop disease detection via bioinspired multidomain feature analysis. The proposed model uses Fuzzy Saliency Maps for identification of contextual disease regions, which are represented into multidomain feature sets. These sets use Fourier, Gabor, Convolutional, Wavelet and Cosine operations, which assist in extraction of multiple high-density features. The extracted features are reduced via a Bacterial Foraging Optimization (BFO) Model, which retains only higher variance feature sets. The retained features are classified by an ensemble model, that combines Naïve Bayes (NB), Multilayer Perceptron (MLP), Logistic Regression (LR), k Nearest Neighbors (kNN) and Support Vector Machine (SVM) classifiers. These classifiers are combined using a boosting model, that assists in retaining unique-correct class training information sets. Due to which the proposed model is able to improve the classification accuracy for Wheat, Tomato, Rice & Corn diseases by 9.4%, while improving precision by 3.5%, recall by 5.9% and reducing delay by 4.3% when compared with existing classification models under similar disease types.

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Lightweight Inception Networks for the Recognition and Detection of Rice Plant Diseases

Cited by 61 publications

References 38 publications

Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification

Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

ECDBMFA: Design of an Ensemble engine for Crop Disease detection via Bioinspired Multidomain Feature Analysis

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