Wheat biomass is an important indicator reflecting the growth status of wheat. Wheat biomass management is one of the most important links in wheat breeding and plant production management in agro-ecosystems, and it is also one of the key factors affecting wheat growth. Wheat production and income. The purpose of this work is to study wheat biomass estimation based on neural network and hyperspectral vegetation index, and to study and establish a biomass prediction model based on hyperspectral vegetation index. Corresponding characteristic parameters (spectral reflectance, spectral index, red edge width, characteristic wavelength) were extracted by analyzing the canopy spectrum, the correlation between characteristic parameters and biomass was analyzed, and a biomass prediction model based on neural network was established. The comparison results of vegetation index and traditional multiple regression model show that the total sample prediction R2 of the multiple linear regression model is 0.869, which is smaller than the total sample prediction R2 of the BP neural network. The biomass estimation model is higher than the wheat biomass estimation model of BP neural network.

Logensh Sainni. Wheat Biomass Estimation Based on Neural Network and Hyperspectral Vegetation Index. Academic Journal of Environmental Biology (2022), Vol. 3, Issue 1: 56-64. https://doi.org/10.38007/AJEB.2022.030106.

[1] Grossberg S . Classical and Instrumental Learning by Neural Networks. 2019, 4(1):1-0.

[2] Sarkar N, Deb A K . Leader-Follower Formation of Second-Order Nonlinear Multi-Agent Systems by Adaptive Neural Networks with Novel Kernel. IFAC-PapersOnLine, 2022, 55( 1):758-764. https://doi.org/10.1016/j.ifacol.2022.04.124

[3] Prey L, Schmidhalter U . Simulation of satellite reflectance data using high-frequency ground based hyperspectral canopy measurements for in-season estimation of grain yield and grain nitrogen status in winter wheat. Isprs Journal of Photogrammetry & Remote Sensing, 2019, 149(MAR.):176-187.

[4] Mert, Dedeolu, Levent, et al. Assessment of the vegetation indices on Sentinel-2A images for predicting the soil productivity potential in Bursa, Turkey.. Environmental monitoring and assessment, 2019, 192(1):16-16. https://doi.org/10.1007/s10661-019-7989-8

[5] Joerg H, Pardini M, Hajnsek I, et al. Sensitivity of SAR Tomography to the Phenological Cycle of Agricultural Crops at X-, C- and L-band. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(9):3014-3029.

[6] Hidayat A A, Cenggoro T W, Pardamean B . Convolutional Neural Networks for Scops Owl Sound Classification. Procedia Computer Science, 2021, 179(4):81-87. https://doi.org/10.1016/j.procs.2020.12.010

[7] Gorlatch S . Convolutional neural networks in APL. Computing reviews, 2020, 61(2):72-72.

[8] Betts P, Barbi N, Gates G, et al. Hyperspectral and Multispectral Reflectance Imaging of Paintings. Microscopy and Microanalysis, 2021, 27(S1):3008-3010.

[9] Shupletsov V V, Zherebtsov E A, Dremin V V, et al. Polyacrylamide-based phantoms of human skin for hyperspectral fluorescence imaging and spectroscopy. Quantum Electronics, 2021, 51(2):118-123. https://doi.org/10.1070/QEL17513

[10] Szulczewski W, Zyromski A, Jakubowski W, et al. A new method for the estimation of biomass yield of giant miscanthus (Miscanthus giganteus) in the course of vegetation. Renewable and Sustainable Energy Reviews, 2018, 82(PT.2):1787-1795. https://doi.org/10.1016/j.rser.2017.07.057

[11] Paul S, Vinayaraj P, Imamoglu N, et al. Canopy Averaged Chlorophyll Content Prediction of Pear Trees using Convolutional Auto-Encoder on Hyperspectral Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, PP(99):1-1.

[12] Rocha A D, Groen T A, Skidmore A K, et al. Role of Sampling Design When Predicting Spatially Dependent Ecological Data With Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, 2020, PP(99):1-12.

[13] Becker F, Backhaus A, Johrden F, et al. Optimal multispectral sensor configurations through machine learning for cognitive agriculture. at - Automatisierungstechnik, 2021, 69(4):336-344. https://doi.org/10.1515/auto-2020-0069

[14] Tongue Tumor Detection in Hyperspectral Images Using Deep Learning Semantic Segmentation. IEEE Transactions on Biomedical Engineering, 2021, 68(4):1330-1340. https://doi.org/10.1109/TBME.2020.3026683

[15] Tipping W J, Wilson L T, An C, et al. Stimulated Raman scattering microscopy with spectral phasor analysis: applications in assessing drug–cell interactions. Chem. Sci. 2022, 13(12):3468-3476. https://doi.org/10.1039/D1SC06976D

[16] Adaptive Deep Cascade Broad Learning System and Its Application in Image Denoising. IEEE Transactions on Cybernetics, 2021, 51(9):4450-4463.

[17] Naif S S, Mahmood D A, Al-Jiboori M H . Seasonal normalized difference vegetation index responses to air temperature and precipitation in Baghdad. Open Agriculture, 2020, 5(1):631-637. https://doi.org/10.1515/opag-2020-0065

[18] Ka A, At B, Go C, et al. Forest cover change in Onigambari reserve, Ibadan, Nigeria: Application of vegetation index and Markov chain techniques. The Egyptian Journal of Remote Sensing and Space Science, 2021, 24( 3):983-990. https://doi.org/10.1016/j.ejrs.2021.08.004