International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 16, No. 2, 8 Apr. 2024
Cover page and Table of Contents: PDF (size: 2080KB)
Modified Political Optimization Algorithm Adapted Deep Neural Networks for Early Plant Disease Detection
PDF (2080KB), PP.96-121
Views: 0 Downloads: 0
Author(s)
Rina Bora
1,*
Deepa Parasar
1
Shrikant Charhate
1
Index Terms
Plant diseases, early detection, modified political optimization, neural network, complex relationships
Abstract
To prevent the loss of the yield of food crops and to attain sustainable agricultural growth, accurate detection of plant disease at an early stage is crucial. However, the extraction of crucial features from infected plant leaves to differentiate the properties associated with different diseases is a complex task, as the diseases exhibit huge variations, which insists on the need for developing precise disease detection. Hence in this research, the early detection of plant disease is performed by utilizing a Modified political optimization adapted deep Neural Network (MPO-adapted deep NN) model, in which the continuous learning capability of the deep NN classifier helps in the deeper analysis of the information in the image and identifies the plant disease more accurately. Identification of the plant disease posse’s challenges due to complexities present in the image and the neural networks effectively dwells with the complex relationships and the non-linear characteristics of the network help in achieving adaptability and makes the system more suitable for real-time applications. The main contribution relies on the modified political optimization algorithm that efficiently tunes the parameters of the deep NN classifier to analyze the disease patterns effectively and provides disease detection with high accuracy. Further, the Adaptive K-means algorithm is utilized for the effective segmentation of diseased parts, and the Grey level co-occurrence matrix (GLCM) features are extracted in the method that enhances the accuracy of the detection. When compared to the existing techniques, the MPO-adapted deep NN model attains high accuracy, sensitivity, and specificity values of 98.95%, 97.45%, and 98.95% for cotton leaf, 94.47%, 94.58%, 94.54% for cotton root, 99.10%, 99.10%, 99.10% for cotton stem, respectively concerning the k-fold. Analysis demonstrating the superiority of the research's metrics values measurement. When compared to existing methods, detecting the disease in cotton stems is very effective.
Cite This Paper
Rina Bora, Deepa Parasar, Shrikant Charhate, "Modified Political Optimization Algorithm Adapted Deep Neural Networks for Early Plant Disease Detection", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.16, No.2, pp. 96-121, 2024. DOI:10.5815/ijigsp.2024.02.08
Reference
[1]A. Umamageswari, N. Bharathiraja, and D.S. Irene. “A novel fuzzy C-means based chameleon swarm algorithm for segmentation and progressive neural architecture search for plant disease classification,” ICT Express, 9(2), pp.160-167, 2023.
[2]Food Safety, “https://www.who.int/news-room/fact-sheets/detail/food-safety”, accessed on May 2022.
[3]M. Ahmad, M. Abdullah, H. Moon, and D. Han. "Plant disease detection in imbalanced datasets using efficient convolutional neural networks with stepwise transfer learning", IEEE Access, vol.9, pp.140565-140580, 2021.
[4]D. Shah, V. Trivedi, V. Sheth, A. Shah, and U. Chauhan. "ResTS: Residual deep interpretable architecture for plant disease detection", Information Processing in Agriculture, 2021.
[5]M.A. Altieri. "Agroecology: the science of sustainable agriculture", CRC Press, 2018.
[6]M. Mishra, P. Choudhury, and B. Pati. "Modified ride-NN optimizer for the IoT based plant disease detection", Journal of Ambient Intelligence and Humanized Computing, vol.12, no.1, pp.691-703, 2021.
[7]I. Mat, M.R.M. Kassim, A.N. Harun, and I.M. Yusoff. "IoT in precision agriculture applications using wireless moisture sensor network", In proceedings of 2016 IEEE Conference on Open Systems (ICOS), pp. 24-29, 2016.
[8]Z. Pang, Q. Chen, W. Han, and L. Zheng. "Value-centric design of the internet-of-things solution for food supply chain: Value creation, sensor portfolio and information fusion", Information Systems Frontiers, vol.17, no.2, pp.289-319, 2015.
[9]M. Arsenovic, M. Karanovic, S. Sladojevic, A. Anderla, and D. Stefanovic. "Solving current limitations of DL based approaches for plant disease detection", Symmetry, vol.11, no.7, pp.939, 2019.
[10]S. Mirjalili, and A. Lewis. "The whale optimization algorithm". Advances in engineering software, vol.95, pp.51-67, 2016.
[11]Q. Askari, I. Younas, and M. Saeed, "Political Optimizer: A novel socio-inspired meta-heuristic for global optimization", Knowledge-Based Systems, vol.195, pp.105709, 2020.
[12]P. Bedi, and P. Gole. "Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network", Artificial Intelligence in Agriculture, vol.5, pp.90-101, 2021.
[13]M. Turkoglu, B. Yanikoğlu, and D. Hanbay, "PlantDiseaseNet: Convolutional neural network ensemble for plant disease and pest detection", Signal, Image and Video Processing, vol.16, no.2, pp.301-309, 2022.
[14]A.H. Elsheikh, A.E. Mohamed, R.D. Sudhansu, T. Muthuramalingam, and L. Songfeng. "A new optimized predictive model based on political optimizer for eco-friendly MQL-turning of AISI 4340 alloy with nano-lubricants," Journal of Manufacturing Processes, vol. 67, pp. 562-578, 2021.
[15]S. Chakraborty, A.K. Saha, S. Sharma, S. Mirjalili, and R. Chakraborty. "A novel enhanced whale optimization algorithm for global optimization," Computers & Industrial Engineering, vol. 153, no.107086, 2021.
[16]B.V. Patil, and P.S. Patil. "Computational method for Cotton Plant disease detection of crop management using DL and internet of things platforms", In Evolutionary Computing and Mobile Sustainable Networks, pp. 875-885, 2021.
[17]S. Ashwinkumar, S. Rajagopal, V. Manimaran, and B. Jegajothi. "Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks." Materials Today: Proceedings 51: 480-487, 2022.
[18]R. Dwivedi, S. Dey, C. Chakraborty, and S. Tiwari. "Grape disease detection network based on multi-task learning and attention features", IEEE Sensors Journal, vol.21, no.16, pp.17573-17580, 2021.
[19]R. Bora, D. Parasar, S, Charhate. “A detection of tomato plant diseases using DL MNDLNN classifier,” SIVP (2023), https://doi.org/10.1007/s11760-023-02498-y.
[20]M. Jalayer, C. Orsenigo, and C. Vercellis, "Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms." Computers in Industry 125: 103378, 2021.
[21]Y. Li, M. Jia, X. Han, and X.S. Bai, "Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA) ". Energy 225, p.120331, 2021.
[22]Z.A.A. Hammouri, M.F. Delgado, E. Cernadas, and S. Barro. “Fast SVC for large-scale classification problems.” IEEE Transactions on Pattern Analysis & Machine Intelligence, (01), pp.1-1, 2021.
[23]T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz, “Review on Convolutional Neural Networks (CNN) in vegetation remote sensing.” ISPRS journal of photogrammetry and remote sensing, 173, pp.24-49, 2021.
[24]J. Jiang, M. Chen, and J.A. Fan. “Deep neural networks for the evaluation and design of photonic devices.” Nature Reviews Materials, 6(8), pp.679-700, 2021.
[25]A. Brodzicki, M. Piekarski, and J. Jaworek-Korjakowska. “The whale optimization algorithm approach for deep neural networks.” Sensors, 21(23), p.8003, 2021.
[26]S.A. Shehu, A.D. Mohammed, and I.M. Abdullahi. “An Optimized Customers Sentiment Analysis Model Using Pastoralist Optimization Algorithm (POA) and Deep Learning.” In 2snip020 IEEE 2nd International Conference on Cyberspac (CYBER NIGERIA), pp. 132-139, 2021.
[27]R. Bora, D. Parasar and S. Charhate, “Identification of Tomato Leaf Disease Using DL Model,” AIP Conf. Proc. 2755, 020016-1–020016-7; https://doi.org/10.1063/5.0148355
[28]R. Bora, D. Parasar and S. Charhate, "Plant Leaf Disease Detection using DL: A Review," 2022 IEEE 7th International conference for Convergence in Technology (I2CT), pp. 1-6, Mumbai, India, 2022, doi: 10.1109/I2CT54291.2022.9824925.