Optimized Crop Disease Identification in Bangladesh: A Deep Learning and SVM Hybrid Model for Rice, Potato, and Corn.

Agriculture plays a vital role in Bangladesh's economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn. The proposed model leverages EfficientNetB0's feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This unified approach streamlines data processing and feature extraction, potentially improving model generalizability across diverse crops and diseases. It also aims to address the challenges of computational efficiency and accuracy that are often encountered in precision agriculture applications. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3, and EfficientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, and 96.14% respectively, demonstrated the superior performance of our proposed model.

A neuro-evolution approach to infer a Boolean network from time-series gene expressions.

SUMMARY: In systems biology, it is challenging to accurately infer a regulatory network from time-series gene expression data, and a variety of methods have been proposed. Most of them were computationally inefficient in inferring very large networks, though, because of the increasing number of candidate regulatory genes. Although a recent approach called GABNI (genetic algorithm-based Boolean network inference) was presented to resolve this problem using a genetic algorithm, there is room for performance improvement because it employed a limited representation model of regulatory functions.In this regard, we devised a novel genetic algorithm combined with a neural network for the Boolean network inference, where a neural network is used to represent the regulatory function instead of an incomplete Boolean truth table used in the GABNI. In addition, our new method extended the range of the time-step lag parameter value between the regulatory and the target genes for more flexible representation of the regulatory function. Extensive simulations with the gene expression datasets of the artificial and real networks were conducted to compare our method with five well-known existing methods including GABNI. Our proposed method significantly outperformed them in terms of both structural and dynamics accuracy. CONCLUSION: Our method can be a promising tool to infer a large-scale Boolean regulatory network from time-series gene expression data. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at https://github.com/kwon-uou/NNBNI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Boolean network inference from time-series gene expression data using a genetic algorithm.

Motivation: Inferring a gene regulatory network from time-series gene expression data is a fundamental problem in systems biology, and many methods have been proposed. However, most of them were not efficient in inferring regulatory relations involved by a large number of genes because they limited the number of regulatory genes or computed an approximated reliability of multivariate relations. Therefore, an improved method is needed to efficiently search more generalized and scalable regulatory relations. Results: In this study, we propose a genetic algorithm-based Boolean network inference (GABNI) method which can search an optimal Boolean regulatory function of a large number of regulatory genes. For an efficient search, it solves the problem in two stages. GABNI first exploits an existing method, a mutual information-based Boolean network inference (MIBNI), because it can quickly find an optimal solution in a small-scale inference problem. When MIBNI fails to find an optimal solution, a genetic algorithm (GA) is applied to search an optimal set of regulatory genes in a wider solution space. In particular, we modified a typical GA framework to efficiently reduce a search space. We compared GABNI with four well-known inference methods through extensive simulations on both the artificial and the real gene expression datasets. Our results demonstrated that GABNI significantly outperformed them in both structural and dynamics accuracies. Conclusion: The proposed method is an efficient and scalable tool to infer a Boolean network from time-series gene expression data. Supplementary information: Supplementary data are available at Bioinformatics online.

A novel mutual information-based Boolean network inference method from time-series gene expression data.

BACKGROUND: Inferring a gene regulatory network from time-series gene expression data in systems biology is a challenging problem. Many methods have been suggested, most of which have a scalability limitation due to the combinatorial cost of searching a regulatory set of genes. In addition, they have focused on the accurate inference of a network structure only. Therefore, there is a pressing need to develop a network inference method to search regulatory genes efficiently and to predict the network dynamics accurately. RESULTS: In this study, we employed a Boolean network model with a restricted update rule scheme to capture coarse-grained dynamics, and propose a novel mutual information-based Boolean network inference (MIBNI) method. Given time-series gene expression data as an input, the method first identifies a set of initial regulatory genes using mutual information-based feature selection, and then improves the dynamics prediction accuracy by iteratively swapping a pair of genes between sets of the selected regulatory genes and the other genes. Through extensive simulations with artificial datasets, MIBNI showed consistently better performance than six well-known existing methods, REVEAL, Best-Fit, RelNet, CST, CLR, and BIBN in terms of both structural and dynamics prediction accuracy. We further tested the proposed method with two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network, and also observed better results using MIBNI compared to the six other methods. CONCLUSIONS: Taken together, MIBNI is a promising tool for predicting both the structure and the dynamics of a gene regulatory network.