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Deep learning-based dynamic agricultural monitoring system: an analysis of the impact of climate variability on crop growth cycles

  
Nov 25, 2024

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Zhao, H., Chen, Z., Jiang, H., **g, W., Sun, L., & Feng, M. (2019). Evaluation of three deep learning models for early crop classification using sentinel-1A imagery time series—A case study in Zhanjiang, China. Remote Sensing, 11(22), 2673. Search in Google Scholar

Canisius, F., Shang, J., Liu, J., Huang, X., Ma, B., Jiao, X., ... & Walters, D. (2018). Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data. Remote Sensing of Environment, 210, 508-518. Search in Google Scholar

Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90. Search in Google Scholar

Karthikeyan, L., Chawla, I., & Mishra, A. K. (2020). A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses. Journal of Hydrology, 586, 124905. Search in Google Scholar

Anami, B. S., Malvade, N. N., & Palaiah, S. (2020). Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images. Artificial intelligence in agriculture, 4, 12-20. Search in Google Scholar

Hassan, M. A., Yang, M., Rasheed, A., Yang, G., Reynolds, M., **a, X., ... & He, Z. (2019). A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant science, 282, 95-103. Search in Google Scholar

Zheng, Y. Y., Kong, J. L., **, X. B., Wang, X. Y., Su, T. L., & Zuo, M. (2019). CropDeep: The crop vision dataset for deep-learning-based classification and detection in precision agriculture. Sensors, 19(5), 1058. Search in Google Scholar

Chang, A., Jung, J., Maeda, M. M., & Landivar, J. (2017). Crop height monitoring with digital imagery from Unmanned Aerial System (UAS). Computers and electronics in agriculture, 141, 232-237. Search in Google Scholar

Wang, D., Cao, W., Zhang, F., Li, Z., Xu, S., & Wu, X. (2022). A review of deep learning in multiscale agricultural sensing. Remote Sensing, 14(3), 559. Search in Google Scholar

Seo, B., Lee, J., Lee, K. D., Hong, S., & Kang, S. (2019). Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA. Field Crops Research, 238, 113-128. Search in Google Scholar

Zhong, L., Hu, L., & Zhou, H. (2019). Deep learning based multi-temporal crop classification. Remote sensing of environment, 221, 430-443. Search in Google Scholar

Hufkens, K., Melaas, E. K., Mann, M. L., Foster, T., Ceballos, F., Robles, M., & Kramer, B. (2019). Monitoring crop phenology using a smartphone based near-surface remote sensing approach. Agricultural and forest meteorology, 265, 327-337. Search in Google Scholar

Kulkarni, O. (2018, August). Crop disease detection using deep learning. In 2018 fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1-4). IEEE. Search in Google Scholar

Shammi, S. A., & Meng, Q. (2021). Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecological Indicators, 121, 107124. Search in Google Scholar

Mandal, D., Kumar, V., Ratha, D., Dey, S., Bhattacharya, A., Lopez-Sanchez, J. M., ... & Rao, Y. S. (2020). Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sensing of Environment, 247, 111954. Search in Google Scholar

Attri, I., Awasthi, L. K., Sharma, T. P., & Rathee, P. (2023). A review of deep learning techniques used in agriculture. Ecological Informatics, 102217. Search in Google Scholar

Gao, F., & Zhang, X. (2021). Map** crop phenology in near real-time using satellite remote sensing: Challenges and opportunities. Journal of Remote Sensing. Search in Google Scholar

Sreekantha, D. K., & Kavya, A. M. (2017, January). Agricultural crop monitoring using IOT-a study. In 2017 11th International conference on intelligent systems and control (ISCO) (pp. 134-139). IEEE. Search in Google Scholar

Khabbazan, S., Vermunt, P., Steele-Dunne, S., Ratering Arntz, L., Marinetti, C., van der Valk, D., ... & van der Sande, C. (2019). Crop monitoring using Sentinel-1 data: A case study from The Netherlands. Remote Sensing, 11(16), 1887. Search in Google Scholar

Tian, H., Qin, Y., Niu, Z., Wang, L., & Ge, S. (2021). Summer maize map** by compositing time series sentinel-1A imagery based on crop growth cycles. Journal of the Indian Society of Remote Sensing, 49, 2863-2874. Search in Google Scholar

Fu, Z., Jiang, J., Gao, Y., Krienke, B., Wang, M., Zhong, K., ... & Liu, X. (2020). Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sensing, 12(3), 508. Search in Google Scholar

Lee, G., Wei, Q., & Zhu, Y. (2021). Emerging wearable sensors for plant health monitoring. Advanced Functional Materials, 31(52), 2106475. Search in Google Scholar

Muruganantham, P., Wibowo, S., Grandhi, S., Samrat, N. H., & Islam, N. (2022). A systematic literature review on crop yield prediction with deep learning and remote sensing. Remote Sensing, 14(9), 1990. Search in Google Scholar

Alhnaity, B., Pearson, S., Leontidis, G., & Kollias, S. (2019, June). Using deep learning to predict plant growth and yield in greenhouse environments. In International Symposium on Advanced Technologies and Management for Innovative Greenhouses: GreenSys2019 1296 (pp. 425-432). Search in Google Scholar

Gong, L., Yu, M., Jiang, S., Cutsuridis, V., & Pearson, S. (2021). Deep learning based prediction on greenhouse crop yield combined TCN and RNN. Sensors, 21(13), 4537. Search in Google Scholar

Chandel, N. S., Chakraborty, S. K., Rajwade, Y. A., Dubey, K., Tiwari, M. K., & Jat, D. (2021). Identifying crop water stress using deep learning models. Neural Computing and Applications, 33, 5353-5367. Search in Google Scholar

Dong Hee Kang,Na Kyong Kim,Wonoh Lee & Hyun Wook Kang. (2024). Geometric feature extraction in nanofiber membrane image based on convolution neural network for surface roughness prediction. Heliyon(15),e35358-e35358. Search in Google Scholar

Farhad Pourkamali Anaraki,Tahamina Nasrin,Robert E. Jensen,Amy M. Peterson & Christopher J. Hansen. (2024). Adaptive activation functions for predictive modeling with sparse experimental data. Neural Computing and Applications(prepublish),1-15. Search in Google Scholar

Chaudhuri Arindam. (2024). Smart traffic management of vehicles using faster R-CNN based deep learning method. Scientific Reports(1),10357-10357. Search in Google Scholar

Changdong Wu,Xu He & Yanliang Wu. (2024). An object detection method for catenary component images based on improved Faster R-CNN. Measurement Science and Technology(8). Search in Google Scholar

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