Han, H. & Lin, H. Patterns of agricultural diversification in China and its policy implications for agricultural modernization. Int. J. Environ. Res. Public Health 18(9), 4978 (2021).
Google Scholar
Pugoy, R. A. D. & Mariano, V. Y. Automated rice leaf disease detection using color image analysis. In Third International Conference on Digital Image Processing vol. 8009 93–99 (SPIE, 2011).
Naik, B. S., Shashikala, J. & Krishnamurthy, Y. L. ‘Study on the diversity of endophytic communities from rice (Oryza sativa L.) and their antagonistic activities in vitro’. Microbiol. Res. 164(3), 290–296 (2009).
Google Scholar
Jagan, M., Balasubramanian, M. & Palanivel, S. Detection and recognition of diseases from paddy plant leaf images. Int. J. Comput. Appl. 144(12), 34–41 (2016).
Phadikar, S., Sil, J. & Das, A. K. ‘Classification of rice leaf diseases based on morphological changes’. Int. J. Inf. Electron. Eng. 2(3), 460–463 (2012).
India: Yield of rice 1991–2021|Statista. https://www.statista.com/statistics/764299/india-yield-of-rice/. Accessed 16 Feb 2022
Shrivastava, V. K., Pradhan, M. K., Minz, S. & Thakur, M. P. Rice plant disease classification using transfer learning of deep convolution neural network. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 42, 631–635 (2019).
Leelavathy, B. & Rao Kovvur, R. M. Prediction of biotic stress in paddy crop using deep convolutional neural networks. In Proceedings of International Conference on Computational Intelligence and Data Engineering 337–346. https://doi.org/10.1007/978-981-15-8767-2_29 (Springer Singapore, 2020).
Garg, G. et al. CROPCARE: an intelligent real time sustainable IoT system for crop disease detection using mobile vision. IEEE Internet Things J. 10(4), 2840–2851 (2023).
Bhola A, Kumar P (2023) Performance evaluation of different machine learning models in crop selection. In Robotics, control and computer vision: select proceedings of ICRCCV 2022 207–217 (Springer, 2023)
Liu, K. & Zhang, X. PiTLiD: identification of plant disease from leaf images based on convolutional neural network. IEEE/ACM Trans. Comput. Biol. Bioinform. 20(2), 1278–1288 (2022).
Subbarayudu, C. & Kubendiran, M. A comprehensive survey on machine learning and deep learning techniques for crop disease prediction in smart agriculture. Nat. Environ. Pollut. Technol. 23(2), 619–632 (2024).
Rao, U. S. et al. Deep learning precision farming: Grapes and mango leaf disease detection by transfer learning. Global . Transit. Proc. 2(2), 535–544 (2021).
Barburiceanu, S., Meza, S., Orza, B., Malutan, R. & Terebes, R. Convolutional neural networks for texture feature extraction. Applications to leaf disease classification in precision agriculture. IEEE Access 9, 160085–160103 (2021).
Bhavekar, G. S. & Goswami, A. D. A hybrid model for heart disease prediction using recurrent neural network and long short term memory. Int. J. Inf. Technol. 14(4), 1781–1789 (2022).
Haridasan, A., Thomas, J. & Raj, E. D. Deep learning system for paddy plant disease detection and classification. Environ. Monit. Assess. 195(1), 120 (2023).
Manikandan, G. et al. Classification models combined with Boruta feature selection for heart disease prediction. Inform. Med. Unlock. 44, 101442 (2024).
Puranik, S. S. et al. MobileNetV3 for mango leaf disease detection: an efficient deep learning approach for precision agriculture. In 2024 5th International Conference for Emerging Technology (INCET) 1–7 (IEEE, 2024)..
Hu, G., Guo, Y., Wei, G. & Abualigah, L. Genghis Khan shark optimizer: a novel nature-inspired algorithm for engineering optimization. Adv. Eng. Inform. 58, 102210 (2023).
Harish, M. et al. analysis on early prediction of cotton plant leaf diseases Using CatBoost algorithm. In 2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS) 1458–1466 (IEEE, 2024).
Bhola, A. & Kumar, P. Deep feature-support vector machine-based hybrid model for multi-crop leaf disease identification in Corn, Rice, and Wheat. Multimed. Tools Appl. 2024, 1–21 (2024).
Omer, S. M., Ghafoor, K. Z. & Askar, S. K. Lightweight improved yolov5 model for cucumber leaf disease and pest detection based on deep learning. SIViP 18(2), 1329–1342 (2024).
Dubey, R. K. & Choubey, D. K. Adaptive feature selection with deep learning MBi-LSTM model-based paddy plant leaf disease classification. Multimed. Tools Appl. 83(9), 25543–25571 (2024).
Elfatimi, E., Eryiğit, R. & Elfatimi, L. Deep multi-scale convolutional neural networks for automated classification of multi-class leaf diseases in tomatoes. Neural Comput. Appl. 36(2), 803–822 (2024).
Wang, B., Yang, H., Zhang, S. & Li, L. Identification of multiple diseases in apple leaf based on optimized lightweight convolutional neural network. Plants 13(11), 1535 (2024).
Google Scholar
Stephen, A., Punitha, A. & Chandrasekar, A. Optimal deep generative adversarial network and convolutional neural network for rice leaf disease prediction. Vis. Comput. 40(2), 919–936 (2024).
Singh, A., Kaur, J., Singh, K. & Singh, M. L. Deep transfer learning-based automated detection of blast disease in paddy crop. SIViP 18(1), 569–577 (2024).
Dubey, R. K. & Choubey, D. K. An efficient adaptive feature selection with deep learning model-based paddy plant leaf disease classification. Multimed. Tools Appl. 83(8), 22639–22661 (2024).
Dogra, R. et al. Deep learning model for detection of brown spot rice leaf disease with smart agriculture. Comput. Electr. Eng. 109, 108659 (2023).
Bharanidharan, N. et al. Multiclass paddy disease detection using filter based feature transformation technique. IEEE Access (2023).
Latif, G., Abdelhamid, S. E., Mallouhy, R. E., Alghazo, J. & Kazimi, Z. A. Deep learning utilization in agriculture: Detection of rice plant diseases using an improved CNN model. Plants 11(17), 2230 (2022).
Google Scholar
Sethy, P. K., Barpanda, N. K., Rath, A. K. & Behera, S. K. Deep feature-based rice leaf disease identification using support vector machine. Comput. Electron. Agric. 175, 105527 (2020).
Debnath, O. & Saha, H. N. An IoT-based intelligent farming using CNN for early disease detection in rice paddy. Microprocess. Microsyst. 94, 104631 (2022).
Lamba, S., Baliyan, A. & Kukreja, V. A novel GCL hybrid classification model for paddy diseases. Int. J. Inf. Technol. 15(2), 1127–1136 (2023).
Google Scholar
Upadhyay, N. & Gupta, N. Detecting fungi-affected multi-crop disease on heterogeneous region dataset using modified ResNeXt approach. Environ. Monit. Assess. 196(7), 610 (2024).
Google Scholar
Upadhyay, N. & Gupta, N. Mango crop maturity estimation using meta-learning approach. J. Food Process Eng. 47(6), e14649 (2024).
Upadhyay, N. & Gupta, N. Potato leaves disease detection with data augmentation using deep learning approach. In International Conference on Information and Communication Technology for Competitive Strategies 589–599 (Springer Nature Singapore, 2022).
Upadhyay, N. & Gupta, N. Diagnosis of fungi affected apple crop disease using improved ResNeXt deep learning model. Multimed. Tools Appl. 83(24), 64879–64898 (2024).
Upadhyay, N. & Gupta, N. A survey on diseases detection for agriculture crops using artificial intelligence. In 2021 5th International conference on Information Systems and Computer Networks (ISCON) 1–8 (IEEE, 2021).
Paddy Doctor: Paddy Disease Classification | Kaggle. Accessed: Jun. 25, 2024. [Online]. https://www.kaggle.com/competitions/paddy-disease-classification.
Naqvi, S. A. H. Bacterial leaf blight of rice: An overview of epidemiology and management with special reference to Indian sub-continent. Pak. J. Agric. Res. 32(2), 359 (2019).
Google Scholar
Wahab, W. A. et al. Disease development and discovery of anatomically resistant features towards bacterial leaf streak in rice. Agriculture 12(5), 629 (2022).
Mulaw, T., Wamishe, Y. & Jia, Y. Characterization and in plant detection of bacteria that cause bacterial panicle blight of rice. Am. J. Plant Sci. 9(4), 667–684 (2018).
Google Scholar
Lamba, S., Kukreja, V., Baliyan, A., Rani, S. & Ahmed, S. H. A novel hybrid severity prediction model for blast paddy disease using machine learning. Sustainability 15(2), 1502 (2023).
Sunder, S., Singh, R. A. M. & Agarwal, R. Brown spot of rice: An overview. Indian Phytopathol 67(3), 201–215 (2014).
Tripathi, P. P., Anup, C. & Asha, S. Suppression of dead-heart and folded leaf symptoms in paddy by Trichogramma japonicum Ashmead in Seppa area of Arunachal Pradesh. India. Environ. Ecol 35, 1297–1299 (2017).
Thakur, R. P. & Mathur, K. Downy mildews of India. Crop Prot. 21(4), 333–345 (2002).
Ali, M. A., Sharma, A. K. & Dhanaraj, R. K. Heterogeneous features and deep learning networks fusion-based pest detection, prevention and controlling system using IoT and pest sound analytics in a vast agriculture system. Comput. Electr. Eng. 116, 109146 (2024).
Sharma, M. & Kumar, C. J. Improving rice disease diagnosis using ensemble transfer learning techniques. Int. J. Artif. Intell. Tools 31(08), 2250040 (2022).
Liu, J., Zhu, F., Chai, C., Luo, Y. & Tang, N. Automatic data acquisition for deep learning. Proc. VLDB Endowment 14(12), 2739–2742 (2021).
Nagaraju, M. et al. Systematic review of deep learning techniques in plant disease detection. Int. J. Syst. Assur. Eng. Manag. 11, 547–560 (2020).
Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J. Big Data 6, 1–48 (2019).
Lopes, L. A., Machado, V. P., Rabelo, R. A., Fernandes, R. A. & Lima, B. V. Automatic labelling of clusters of discrete and continuous data with supervised machine learning. Knowl.-Based Syst. 106, 231–241 (2016).
Thomas, J. & Raj, E. D. Effectual single image dehazing with color correction transform and dark channel prior. In International Conference on Information Processing 29–41 (Springer, 2021).
Khirade, S. D. & Patil, A. B. Plant disease detection using image processing. In 2015 International Conference on Computing Communication Control and Automation 768–771 (2015).
Chaki, J. & Dey, N. A Beginner’s Guide to Image Shape Feature Extraction Techniques (CRC Press, 2019).
Rosipal, R., Girolami, M., Trejo, L. J. & Cichocki, A. Kernel PCA for feature extraction and de-noising in nonlinear regression. Neural Comput. Appl. 10, 231–243 (2001).
Sony, S., Dunphy, K., Sadhu, A. & Capretz, M. A systematic review of convolutional neural network-based structural condition assessment techniques. Eng. Struct. 226, 111347 (2021).
Lu, J., Tan, L. & Jiang, H. Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture 11, 707 (2021).
Joppa, L. N. The Case for Technology Investments in the Environment (2017).
Nixon, M. & Aguado, A. Feature Extraction and Image Processing for Computer Vision (Academic Press, 2019).
Barga, R., Fontama, V., & Tok, W. H. Introducing Microsoft Azure machine learning. In Predictive Analytics with Microsoft Azure Machine Learning 21–43 (Springer, 2015).
Barga, R., Fontama, V., Tok, W. H. & Cabrera-Cordon, L. Predictive Analytics with Microsoft Azure Machine Learning (Springer, 2015).
Brownlee, J. Deep learning for computer vision: image classification, object detection, and face recognition in python. Machine Learning Mastery (2019).
Benesty, J., Chen, J. & Huang, Y. Classical Optimal Filtering. Microphone Array Signal Processing 7–37 (2008).
Ilesanmi, A. E., Idowu, O. P., Chaumrattanakul, U. & Makhanov, S. S. Multiscale hybrid algorithm for pre-processing of ultrasound images. Biomed. Signal Process. Control 66, 102396. https://doi.org/10.1016/j.bspc.2020.102396 (2021).
Chandra, T. B. & Verma, K. Analysis of quantum noise reducing filters on chest X-ray images: A review. Measurement 153, 107426. https://doi.org/10.1016/j.measurement.2019.107426 (2020).
Thangadurai, K. & Padmavathi, K. Computer vision image enhancement for plant leaves disease detection. In 2014 World Congress on Computing and Communication Technologies 173–175 (IEEE, 2014).
Mikołajczyk, A. & Grochowski, M. Data augmentation for improving deep learning in image classification problem. In 2018 international Interdisciplinary PhD Workshop (IIPhDW) 117–122 (IEEE, 2018).
Kaur, D. & Kaur, Y. Various image segmentation techniques: a review. Int. J. Comput. Sci. Mob. Comput. 3(5), 809–814 (2014).
Saha, P. K. & Udupa, J. K. Optimum image thresholding via class uncertainty and region homogeneity. IEEE Trans. Pattern Anal. Mach. Intell. 23(7), 689–706 (2001).
Issac, A., Sarathi, M. P. & Dutta, M. K. An adaptive threshold based image processing technique for improved glaucoma detection and classification. Comput. Methods Programs Biomed. 122(2), 229–244 (2015).
Google Scholar
Lofroth, M. & Avci, E. Auto-focusing approach on multiple micro objects using the prewitt operator. Int. J. Intell. Robot. Appl. 2(4), 413–424 (2018).
Jumb, V., Sohani, M. & Shrivas, A. Color image segmentation using K-means clustering and Otsu’s adaptive thresholding. Int. J. Innov. Technol. Explor. Eng. 3(9), 72–76 (2014).
Mairal, J., Koniusz, P., Harchaoui, Z. & Schmid, C. Convolutional kernel networks. Adv. Neural Inf. Process. Syst. 27, 332 (2014).
Ahmed, A. S. Comparative study among Sobel, Prewitt and Canny edge detection operators used in image processing. J. Theor. Appl. Inf. Technol 96(19), 6517–6525 (2018).
Abildayeva, T. & Shamoi, P. Fuzzy logic approach for visual analysis of websites with K-means clustering-based color extraction. arXiv preprint. arXiv:2408.00774 (2024).
Mustafa, W. A., Khairunizam, W., Ibrahim, Z., Shahriman, A. & Razlan, Z. M. A review of different segmentation approach on non uniform images. In 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA) 1–6 (IEEE, 2018).
Yin, X., Li, W., Li, Z. & Yi, L. Recognition of grape leaf diseases using MobileNetV3 and deep transfer learning. Int. J. Agric. Biol. Eng. 15(3), 184–194 (2022).
Abdel-Salam, M., Alzahrani, A. I., Alblehai, F., Zitar, R. A. & Abualigah, L. An improved Genghis Khan optimizer based on enhanced solution quality strategy for global optimization and feature selection problems. Knowl.-Based Syst. 302, 112347 (2024).
Ibrahim, H. T., Mazher, W. J. & Yaseen, Z. F. Hybrid feature selection approach based on firefly algorithm and simulated annealing for cancer datasets. Univ. Thi-Qar J. Eng. Sci. 14(1), 1–9 (2024).
Thota, K. K. et al. A Model for Predicting Chronic Renal Failure using CatBoost Classifier Algorithm and XGBClassifier. In 2024 Second International Conference on Inventive Computing and Informatics (ICICI) 96–102 (IEEE, 2024).
Modak, S. K. S. & Jha, V. K. Diabetes prediction model using machine learning techniques. Multimed. Tools Appl. 83(13), 38523–38549 (2024).
Elaziz, M. A., Dahou, A., El-Sappagh, S., Mabrouk, A. & Gaber, M. M. AHA-AO: Artificial hummingbird algorithm with aquila optimization for efficient feature selection in medical image classification. Appl. Sci. 12, 9710. https://doi.org/10.3390/app12199710 (2022).
Google Scholar
Ramachandran, P., Zoph, B. & Le, Q.V. 2017 Searching for activation functions. arXiv. arXiv:1710.05941.
Elfwing, S., Uchibe, E. & Doya, K. Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw. 107, 3–11 (2018).
Google Scholar
Wang, C., Yao, X., Ding, F. & Yu, Z. A trajectory planning method for a casting sorting robotic arm based on a nature-inspired Genghis Khan shark optimized algorithm. Math. Biosci. Eng. 21(2), 3364–3390 (2024).
Google Scholar
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: Unbiased boosting with categorical features. Adv. Neural Inf. Process. Syst. 31, 1–11 (2018).
Dorogush, A. V., Ershov, V. & Gulin, A. CatBoost: Gradient boosting with categorical features support. ArXiv Preprint arXiv:1810.11363 (2018).
Hancock, J. T. & Khoshgoftaar, T. M. Survey on categorical data for neural networks. J. Big Data 7(1), 28. https://doi.org/10.1186/s40537-020-00305-w (2020).
Hao, D., Xiaoqi, Y. & Taoyu, Q. Hybrid machine learning models based on CATBoost classifier for assessing students’ academic performance. Int. J. Adv. Comput. Sci. Appl. 15(7), 94–106 (2024).
Asmar, E., Vahidnia, M. H., Rezaei, M. & Amiri, E. Remote sensing-based paddy yield estimation using physical and FCNN deep learning models in Gilan province, Iran. Remote Sens. Appl. 34, 101199 (2024).
Jeyanathan, J. S. et al. Pesticide Recommender System for Detecting the Paddy Crop Diseases through SVM. In 2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) 1–6 (IEEE, 2024).
Babu, B. A., & Dass, P. Detection of disease in fresh fruits using convolution neural network by comparing with KNN to maximize the accuracy and sensitivity. In AIP Conference Proceedings Vol. 2853, No. 1) (AIP Publishing, 2024).
