Keywords and phrases: agriculture sector, median value imputation, LeNet-DLV3 model, Tsallis entropy based conditional mutual information, Tri-bridNet disease classifier
Received: April 12, 2024; Revised: May 22, 2024; Accepted: May 30, 2024; Published: July 18, 2024
How to cite this article: Sultan Almotairi, Shailendra Mishra, Olayan Alharbi, Zaid Alzaid, Yasser M. Hausawi and Jaber Almutairi, A deep learning approach for enhancing crop disease detection and pesticide recommendation: Tri-bridNet with collaborative filtering, Advances and Applications in Discrete Mathematics 41(6) (2024), 449-476. https://doi.org/10.17654/0974165824031
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
References: [1] W. Albattah, M. Nawaz, A. Javed, M. Masood and S. Albahli, A novel deep learning method for detection and classification of plant diseases, Complex and Intelligent Systems 8 (2022), 507-524. [2] G. Sakkarvarthi, G. W. Sathianesan, V. S. Murugan, A. J. Reddy, P. Jayagopal and M. Elsisi, Detection and classification of tomato crop disease using convolutional neural network, Electronics 11(21) (2022), 3618. [3] İ. Yağ and A. Altan, Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments, Biology 11(12) (2022), 1732. [4] G. Storey, Q. Meng and B. Li, Leaf disease segmentation and detection in apple orchards for precise smart spraying in sustainable agriculture, Sustainability 14(3) (2022), 1458. [5] A. S. Baghel, A. Bhardwaj and W. Ibrahim, Optimization of pesticides spray on crops in agriculture using machine learning, Computational Intelligence and Neuroscience, 2022. [6] V. Balaska, Z. Adamidou, Z. Vryzas and A. Gasteratos, Sustainable crop protection via robotics and artificial intelligence solutions, Machines 11(8) (2023), 774. [7] M. Tholkapiyan, B. Aruna Devi, D. Bhatt, E. Saravana Kumar, S. Kirubakaran and R. Kumar, Performance analysis of rice plant diseases identification and classification methodology, Wireless Personal Communications 130(2) (2023), 1317-1341. [8] M. Nandhini, K. U. Kala, M. Thangadarshini and S. M. Verma, Deep learning model of sequential image classifier for crop disease detection in plantain tree cultivation, Computers and Electronics in Agriculture 197 (2022), 106915. [9] L. C. Ngugi, M. Abdelwahab and M. Abo-Zahhad, A new approach to learning and recognizing leaf diseases from individual lesions using convolutional neural networks, Information Processing in Agriculture 10(1) (2023), 11-27. [10] V. Gautam, N. K. Trivedi, A. Singh, H. G. Mohamed, I. D. Noya, P. Kaur and N. Goyal, A transfer learning-based artificial intelligence model for leaf disease assessment, Sustainability 14(20) (2022), 13610. [11] P. Kaur, S. Harnal, R. Tiwari, S. Upadhyay, S. Bhatia, A. Mashat and A. M. Alabdali, Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction, Sensors 22(2) (2022), 575. [12] S. Kendler, R. Aharoni, S. Young, H. Sela, T. Kis-Papo, T. Fahima and B. Fishbain, Detection of crop diseases using enhanced variability imagery data and convolutional neural networks, Computers and Electronics in Agriculture 193 (2022), 106732. [13] S. M. Hassan and A. K. Maji, Plant disease identification using a novel convolutional neural network, IEEE Access 10 (2022), 5390-5401. [14] A. Venkataramana, K. S. Kumar, N. Suganthi and R. Rajeswari, Prediction of brinjal plant disease using support vector machine and convolutional neural network algorithm based on deep learning, Journal of Mobile Multimedia 18(3) (2022), 771-788. [15] Y. Resti, C. Irsan, M. Amini, I. Yani and R. Passarella, Performance improvement of decision tree model using fuzzy membership function for classification of corn plant diseases and pests, Science and Technology Indonesia 7(3) (2022), 284-290. [16] T. Thorat, B. K. Patle and S. K. Kashyap, Intelligent insecticide and fertilizer recommendation system based on TPF-CNN for smart farming, Smart Agricultural Technology 3 (2023), 100114. [17] S. Kumar, R. R. Patil and R. Rani, Smart IoT-based pesticides recommendation system for rice diseases, Intelligent Systems and Applications: Select Proceedings of ICISA 2022, Springer Nature Singapore, Singapore, 2023, pp. 17-25. [18] V. R. Sadasivam, S. M. Suhail, M. S. Rajan and R. Tharun, Classification of plant disease and pesticides recommendation using deep-learning, Journal of Population Therapeutics and Clinical Pharmacology 30(6) (2023), 391-397. [19] S. Patil, R. Saha and A. Sangole, Rice plant disease detection and remedies recommendation using machine learning, International Research Journal of Engineering and Technology (IRJET) (2022), 1431-1494. [20] B. Unhelkar and P. Chakrabarti, A novel deep learning models for efficient insect pest detection and recommending an organic pesticide for smart farming, International Journal of Intelligent Systems and Applications in Engineering 12(9s) (2024), 15-31. [21] M. Rahaman, M. Chowdhury, M. A. Rahman, H. Ahmed, M. Hossain, M. H. Rahman, M. Biswas, M. Kader, T. A. Noyan and M. Biswas, A deep learning based smartphone application for detecting mango diseases and pesticide suggestions, International Journal of Computing and Digital Systems 13(1) (2023), 1273-1286. [22] J. G. Esgario, P. B. de Castro, L. M. Tassis and R. A. Krohling, An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning, Information Processing in Agriculture 9(1) (2022), 38-47. [23] A. V. Panchal, S. C. Patel, K. Bagyalakshmi, P. Kumar, I. R. Khan and M. Soni, Image-based plant diseases detection using deep learning, Materials Today: Proceedings 80 (2023), 3500-3506. [24] S. Vallabhajosyula, V. Sistla and V. K. K. Kolli, Transfer learning-based deep ensemble neural network for plant leaf disease detection, Journal of Plant Diseases and Protection 129(3) (2022), 545-558. [25] C. Murugamani, S. Shitharth, S. Hemalatha, P. R. Kshirsagar, K. Riyazuddin, Q. N. Naveed, S. Islam, S. P. Mazher Ali and A. Batu, Machine learning technique for precision agriculture applications in 5G-based internet of things, Wireless Communications and Mobile Computing, 2022. [26] Dataset 1 is taken from https://www.kaggle.com/datasets/suhelahamed/drone camera-image-dataset-of-agriculture-fields dated on 15/04/2024. [27] Dataset 2 is taken from https://www.kaggle.com/datasets/akshatgupta7/cropyield- in-indian-states-dataset dated on 15/04/2024. [28] A. Vulli, P. N. Srinivasu, M. S. K. Sashank, J. Shafi, J. Choi and M. F. Ijaz, Fine-tuned DenseNet-169 for breast cancer metastasis prediction using FastAI and 1-cycle policy, Sensors 22(8) (2022), 2988. [29] S. S. Sajid, M. Shahhosseini, I. Huber, G. Hu and S. V. Archontoulis, County-scale crop yield prediction by integrating crop simulation with machine learning models, Frontiers in Plant Science 13 (2022), 1000224. [30] Y. Lu, S. Young, H. Wang and N. Wijewardane, Robust plant segmentation of color images based on image contrast optimization, Computers and Electronics in Agriculture 193 (2022), 106711. [31] S. P. Raja, B. Sawicka, Z. Stamenkovic and G. Mariammal, Crop prediction based on characteristics of the agricultural environment using various feature selection techniques and classifiers, IEEE Access 10 (2022), 23625-23641. [32] W. Yuan, N. K. Wijewardane, S. Jenkins, G. Bai, Y. Ge and G. L. Graef, Early prediction of soybean traits through color and texture features of canopy RGB imagery, Scientific Reports 9(1) (2019), 14089.
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