Rice-net: an efficient artificial fish swarm optimization applied deep convolutional neural network model for identifying the Oryza sativa diseases

Goluguri, N. V. Raja Reddy; Devi, K. Suganya; Srinivasan, Padmini

doi:10.1007/s00521-020-05364-x

Cited by 39 publications

(17 citation statements)

References 14 publications

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“…Long short-term memory (LSTM) networks, a variant of RNNs [35], solved the drawbacks of the traditional RNNs. There has been plenty of research in various realms that utilized the LSTM networks to process the time series data successfully, for instance, natural language processing (NLP) [36][37][38], water or air quality forecasting [39][40][41], and disease diagnoses [42,43]. The LSTM network also has some variants; one is the gated recurrent unit (GRU) network [44], which needs fewer calculations than an LSTM cell.…”

Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

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Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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“…Research on LSTM improvement based on meta-heuristic algorithms is currently expanding, and these algorithms are used for weight optimization, parameter optimization, and deep learning network threshold. For example, the research conducted in this field follows: LSTM and Lion Swarm Optimization (LSTM-LSO) to predict the optimal problems [16,17]; improved LSTM based on Ant Colony Optimization (ACO-LSTM) [18]; Fireworks Algorithm (FWA), and LSTM to solve optimization problems [19]; Artificial Fish Swarm Optimization (AFSO) algorithm and LSTM for disease diagnosis [20]; Grasshopper Optimization Algorithm (GOA) and LSTM network for wind speed prediction [21]; GOA and LSTM network for detection of defective gears [22]; PSO and LSTM for wind energy prediction [23]; PSO and RNN-LSTM for detection of objects in medical images [24]; Differential Evolution (DE) and LSTM for prediction [25]; face recognition based on deep learning and Cat Swarm Optimization (CSO) [26]; disease diagnosis (cancer and heart) based on CNN-PSO [27]; and use of Improved Crossover-Based Monarch Butterfly Optimization (ICRMBO) to improve CNN [28].…”

Section: Motivationmentioning

confidence: 99%

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

et al. 2022

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An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively.

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“…According to research, the suggested system was more effective in detecting untrained rice plant video in our experimental scenario than VGG16, ResNet-50, ResNet-101, and YOLOv3. To categorize plant diseases affecting Oryza Sativa, GNV Rajareddy et al [22] have written a study paper (LB, BS, Healthy and also the Hispa). The research mainly concentrated on using a DCNN architecture together with several classifiers including SVM, ANN, and LSTM.…”

Section: Related Workmentioning

confidence: 99%

“…In this optimization, initially, virtual features are used for finding food. The sparrow's [22] position is updated by the below matrix:…”

Section: Weighted Ssa (Wssa)mentioning

confidence: 99%

Infectious diseases of Rice plants classified using a deep learning-powered Least Squares Support Vector Machine Model

Goluguri¹,

Devi²,

CH³

2022

INDJCSE

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In the field of agriculture, identifying and classifying Oryza Sativa diseases is always a hot research topic. Almost in all the states of India, Oryza Sativa (rice) is cultivated as a necessary food crop. However, rice plants are highly caused due to diseases that drastically affect the agricultural sectors. Researchers are searching for solutions for reliable and exact detection approaches for crop leaf diseases. A Oryza Sativa plant leaf disease classification system that is modelled relying on the concept of deep learning-driven Least Square Support Vector Machine is investigated in the present study. This work focus on the Oryza Sativa plant leaf diseases like Brown Leaf spot(BS), Bacterial leaf blight (BLB), Leaf smut(LS), Tungro(LT), and Leaf Blast(LB). To create a contrast-enhanced image dataset, the input Oryza Sativa plant disease image dataset is resized as well as histogram equalization and the Kuwahara filter are applied. The diseased part is segmented using spatial fuzzy clustering (SFC). Then, a multi-model LeNet5 is used in extracting features, and the least square support vector machine(LSSVM) is employed to classify the Oryza Sativa plant leaf diseases images. Further, to improve the accuracy of the disease classification Weighted Sparrow Search Optimization (WSSO) is utilized. The proposed multi-model LSSVM-WSSO outperformed the existing models on the two benchmark datasets when evaluated on the terms of accuracy, precision, recall, and F1-measure values.

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Rice-net: an efficient artificial fish swarm optimization applied deep convolutional neural network model for identifying the Oryza sativa diseases

Cited by 39 publications

References 14 publications

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

Infectious diseases of Rice plants classified using a deep learning-powered Least Squares Support Vector Machine Model

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