Abstract
Smart agriculture has become one of the most popular technologies for farmers due to its simplicity, ease of deployment, high efficiency, and low overheads. But due to an exponential increase in smart-farming data generation, it is necessary to design secure storage interfaces, that can be scaled for multiple farms. Existing storage models either showcase high security, or high storage efficiency, but a very few models enhance both these parameter sets. Such models are highly complex, and reduce the scalability when applied to large-scale scenarios. To overcome these limitations, this text proposes design of a highly efficient and secure agriculture-record-storage model via reconfigurable blockchains. The proposed model initially uses a multiple crop pattern prediction system via Binary Cascaded Convolutional Neural Network (BC CNN), and deploys a single chained Proof-of-Trust (PoT) based blockchain, that is tuned w.r.t. context of the farms. The prediction is done via weather conditions and soil types. This assists in identification of different crop types, and selection of high trust miner nodes, that can preserve privacy during communication and storage operations. As the blockchain is scaled, a Grey Wolf Optimization (GWO) based model is deployed, which assists in splitting the underlying chain into multiple sidechains. This split is done based on QoS and Security optimizations, which is estimated via temporal miner performance under different farm types. The GWO Model also assists in estimating long-term and high-capacity storage chains, which can be used for archival operations. Due to which, the proposed model is able to improve mining speed by 9.4%, while reducing the energy consumption by 3.5% for different mining operations. The model also defines an indexing strategy for different shards, which assists in increasing data access speed by 12.8% for long-term data sets. Due to these enhancements, the proposed model is capable of deployment for large-scale scenarios.
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References
Aggarwal S, Kumar N, Tanwar S (2020) Blockchain-envisioned UAV communication using 6G networks: open issues, use cases, and future directions. IEEE Internet Things J 8(7):5416–5441. https://doi.org/10.1109/jiot.2020.3020819
Busquier M, Lopez-Sanchez JM, Ticconi F, Floury N (2022) Combination of time series of L-, C-, and X-band SAR images for land cover and crop classification. IEEE J Sel Top Appl Earth Observ Rem Sens 15:8266–8286. https://doi.org/10.1109/jstars.2022.3207574
Chellasamy M, Zielinski RT, Greve MH (2014) A multievidence approach for crop discrimination using multitemporal worldview-2 imagery. IEEE J Sel Top Appl Earth Observ Rem Sens 7(8):3491–3501. https://doi.org/10.1109/jstars.2014.2349945
Christensen CL, Vartakavi A (2021) An experience-based direct generation approach to automatic image cropping. IEEE Access 9:107600–107610. https://doi.org/10.1109/access.2021.3100816
Della Vecchia A, Ferrazzoli P, Guerriero Blaes X, Defourny P, Dente L, Wegmuller U (2006) Influence of geometrical factors on crop backscattering at C-band. IEEE Trans Geosci Remote Sens 44(4):778–790. https://doi.org/10.1109/tgrs.2005.860489
Du X, Zare A (2018) Multiple instance choquet integral classifier fusion and regression for remote sensing applications. IEEE Trans Geosci Remote Sens 57(5):2741–2753. https://doi.org/10.1109/tgrs.2018.2876687
Galloza MS, Crawford MM, Heathman GC (2013) Crop residue modeling and mapping using Landsat, ALI, Hyperion and airborne remote sensing data. IEEE J Sel Top Appl Earth Observ Rem Sens 6(2):446–456. https://doi.org/10.1109/jstars.2012.2222355
Gao H, Wang C, Wang G, Li Q, Zhu J (2019) A new crop classification method based on the time-varying feature curves of time series dual-polarization Sentinel-1 data sets. IEEE Geosci Remote Sens Lett 17(7):1183–1187. https://doi.org/10.1109/lgrs.2019.2943372
Guan K, Li Z, Rao LN, Gao F, Xie D, Hien NT, Zeng Z (2018) Mapping paddy rice area and yields over Thai Binh province in Viet Nam from modis, landsat, and alos-2/palsar-2. IEEE J Sel Top Appl Earth Observ Rem Sens 11(7):2238–2252. https://doi.org/10.1109/jstars.2018.2834383
Hu Q, Wu W, Song Q, Yu Q, Lu M, Yang P, Long Y (2016) Extending the pairwise separability index for multicrop identification using time-series modis images. IEEE Trans Geosci Remote Sens 54(11):6349–6361. https://doi.org/10.1109/tgrs.2016.2581210
Hu WJ, Fan J, Du YX, Li BS, Xiong N, Bekkering E (2020) MDFC–ResNet: an agricultural IoT system to accurately recognize crop diseases. IEEE Access 8:115287–115298. https://doi.org/10.1109/access.2020.3001237
Hu C, Xie S, Song D, Thomasson JA, Hardin RG IV, Bagavathiannan M (2022) Algorithm and system development for robotic micro-volume herbicide spray towards precision weed management. IEEE Robot Autom Lett 7(4):11633–11640. https://doi.org/10.1109/lra.2022.3191240
Jiang J, Xing F, Zeng X, Zou Q (2019) Investigating maize yield-related genes in multiple omics interaction network data. IEEE Trans Nanobiosci 19(1):142–151. https://doi.org/10.1109/tnb.2019.2920419
Kussul N, Lemoine G, Gallego FJ, Skakun SV, Lavreniuk M, Shelestov AY (2016) Parcel-based crop classification in Ukraine using Landsat-8 data and Sentinel-1A data. IEEE J Sel Top Appl Earth Observ Remote Sens 9(6):2500–2508. https://doi.org/10.1109/jstars.2016.2560141
Lin CC, Deng DJ, Kang JR, Liu WY (2021) A dynamical simplified swarm optimization algorithm for the multiobjective annual crop planning problem conserving groundwater for sustainability. IEEE Trans Ind Inform 17(6):4401–4410. https://doi.org/10.1109/TII.2020.3029258
Liu J, Huffman T, Qian B, Shang J, Li Q, Dong T, Jing Q (2020) Crop yield estimation in the Canadian Prairies using Terra/MODIS-derived crop metrics. IEEE J Sel Top Appl Earth Observ Remote Sens 13:2685–2697. https://doi.org/10.1109/jstars.2020.2984158
Liu Y, Yu Q, Zhou Q, Wang C, Bellingrath-Kimura SD, Wu W (2022a) Mapping the complex crop rotation systems in southern china considering cropping intensity, crop diversity, and their seasonal dynamics. IEEE J Sel Top Appl Earth Observ Remote Sens 15:9584–9598. https://doi.org/10.1109/jstars.2022.3218881
Liu Z, Bashir RN, Iqbal S, Shahid M, TausifUmer MAQ (2022b) Internet of Things (IoT) and machine learning model of plant disease prediction–blister blight for tea plant. IEEE Access 10:44934–44944. https://doi.org/10.1109/access.2022.3169147
Liu Y, Pu X, Shen Z (2023) Crop type mapping based on polarization information of time series sentinel-1 images using patch-based neural network. Remote Sens 15(13):3384
Luciani R, Laneve G, JahJah M (2019a) Agricultural monitoring, an automatic procedure for crop mapping and yield estimation: the great rift valley of Kenya case. IEEE J Sel Top Appl Earth Observ Remote Sens 12(7):2196–2208
Luciani R, Laneve G, JahJah M (2019b) Agricultural monitoring, an automatic procedure for crop mapping and yield estimation: the great rift valley of Kenya case. IEEE J Sel Top Appl Earth Observ Remote Sens 12(7):2196–2208. https://doi.org/10.1109/access.2021.3100816
Pal P, Sharma RP, Tripathi S, Kumar C, Ramesh D (2022) Machine learning regression for RF path loss estimation over grass vegetation in IoWSN monitoring infrastructure. IEEE Trans Industr Inf 18(10):6981–6990. https://doi.org/10.1109/tii.2022.3142318
Qu X, Shi D, Gu X, Sun Q, Hu X, Yang X, Pan Y (2022) Monitoring lodging extents of maize crop using multitemporal GF-1 images. IEEE J Sel Top Appl Earth Observ Remote Sens 15:3800–3814. https://doi.org/10.1109/jstars.2022.3170345
Saleem MH, Potgieter J, Arif KM (2022) A performance-optimized deep learning-based plant disease detection approach for horticultural crops of new zealand. IEEE Access 10:89798–89822. https://doi.org/10.1109/access.2022.3201104
Tang Z, Wang H, Li X, Cai W, Han C (2020) An object-based approach for mapping crop coverage using multiscale weighted and machine learning methods. IEEE J Sel Top Appl Earth Observ Remote Sens 13:1700–1713. https://doi.org/10.1109/jstars.2020.2983439
Thenkabail PS, Mariotto I, Gumma MK, Middleton EM, Landis DR, Huemmrich KF (2013) Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data. IEEE J Sel Top Appl Earth Observ Remote Sens 6(2):427–439. https://doi.org/10.1109/jstars.2013.2252601
Vlachopoulos O, Leblon B, Wang J, Haddadi A, LaRocque A, Patterson G (2021) Evaluation of crop health status with UAS multispectral imagery. IEEE J Sel Top Appl Earth Observ Remote Sens 15:297–308. https://doi.org/10.1109/jstars.2021.3132228
Wang L, Xu L, Zheng Z, Liu S, Li X, Cao L, Sun C (2021a) Smart contract-based agricultural food supply chain traceability. IEEE Access 9:9296–9307. https://doi.org/10.1109/access.2021.3050112
Wang W, Wu Y, Zhang Q, Zheng H, Yao X, Zhu Y, Cheng T (2021b) AAVI: A novel approach to estimating leaf nitrogen concentration in rice from unmanned aerial vehicle multispectral imagery at early and middle growth stages. IEEE J Sel Top Appl Earth Observ Remote Sens 14:6716–6728. https://doi.org/10.1109/jstars.2021.3086580
Zhao C, Li H, Li P, Yang G, Gu X, Lan Y (2016) Effect of vertical distribution of crop structure and biochemical parameters of winter wheat on canopy reflectance characteristics and spectral indices. IEEE Trans Geosci Remote Sens 55(1):236–247. https://doi.org/10.1109/tgrs.2016.2604492
Zhong B, Yang A, Nie A, Yao Y, Zhang H, Wu S, Liu Q (2015) Finer resolution land-cover mapping using multiple classifiers and multisource remotely sensed data in the Heihe River Basin. IEEE J Sel Top Appl Earth Observ Remote Sens 8(10):4973–4992. https://doi.org/10.1109/jstars.2015.2461453
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Jawale, D., Malik, S. MPSARB: design of an efficient multiple crop pattern prediction system with secure agriculture-record-storage model via reconfigurable blockchains. J Ambient Intell Human Comput 15, 2529–2541 (2024). https://doi.org/10.1007/s12652-024-04769-z
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DOI: https://doi.org/10.1007/s12652-024-04769-z