An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI

被引：0

作者：

Mohammad Saleem Khan

Manoj Semwal

Ashok Sharma

Rajesh Kumar Verma

机构：

[1] CSIR-Central Institute of Medicinal and Aromatic Plants,Information and Communication Technology Department

[2] CSIR-Central Institute of Medicinal and Aromatic Plants,Plant Biotechnology Department

[3] CSIR-Central Institute of Medicinal and Aromatic Plants,Agronomy and Soil Science Department

[4] Academy of Scientific and Innovative Research (AcSIR),undefined

来源：

Precision Agriculture | 2020年 / 21卷

关键词：

Aromatic crops; Mentha; Neural network; Crop modelling; Spectral indices; Yield estimation;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Yield forecasting is essential for management of the food and agriculture economic growth of a country. Artificial Neural Network (ANN) based models have been used widely to make precise and realistic forecasts, especially for the nonlinear and complicated problems like crop yield prediction, biomass change detection and crop evapo-transpiration examination. In the present study, various parameters viz. spectral bands of Landsat 8 OLI (Operational Land Imager) satellite data and derived spectral indices along with field inventory data were evaluated for Mentha crop biomass estimation using ANN technique of Multilayer Perceptron. The estimated biomass showed a good relationship (R2 = 0.762 and root mean square error (RMSE) = 2.74 t/ha) with field-measured biomass.

引用

页码：18 / 33

页数：15

共 50 条

[1] An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI
Khan, Mohammad Saleem
Semwal, Manoj
Sharma, Ashok
Verma, Rajesh Kumar
[J]. PRECISION AGRICULTURE, 2020, 21 (01) : 18 - 33
[2] Landsat 8 OLI Satellite Image Classification using Convolutional Neural Network
Rai, Amit Kumar
Mandal, Nirupama
Singh, Akansha
Singh, Krishna Kant
[J]. INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE, 2020, 167 : 987 - 993
[3] An artificial neural network model for estimating crop yields using remotely sensed information
Jiang, D
Yang, X
Clinton, N
Wang, N
[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2004, 25 (09) : 1723 - 1732
[4] An artificial neural network model for crop yield responding to soil parameters
Liu, G
Yang, XH
Li, MZ
[J]. ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 3, PROCEEDINGS, 2005, 3498 : 1017 - 1021
[5] A crop yield prediction model based on an improved artificial neural network and yield monitoring using a blockchain technique
Sumathi, M.
Rajkamal, M.
Raja, S. P.
Venkatachalapathy, M.
Vijayaraj, N.
[J]. INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, 2022, 20 (06)
[6] Retrieval of Turbidity and TDS of Deepor Beel Lake from Landsat 8 OLI Data by Regression and Artificial Neural Network
Bormudoi, Arnob
Hinge, Gilbert
Nagai, Masahiko
Kashyap, Manash Pratim
Talukdar, Rajib
[J]. WATER CONSERVATION SCIENCE AND ENGINEERING, 2022, 7 (04) : 505 - 513
[7] Retrieval of Turbidity and TDS of Deepor Beel Lake from Landsat 8 OLI Data by Regression and Artificial Neural Network
Arnob Bormudoi
Gilbert Hinge
Masahiko Nagai
Manash Pratim Kashyap
Rajib Talukdar
[J]. Water Conservation Science and Engineering, 2022, 7 : 505 - 513
[8] Mapping crop cover using multi-temporal Landsat 8 OLI imagery
Sonobe, Rei
Yamaya, Yuki
Tani, Hiroshi
Wang, Xiufeng
Kobayashi, Nobuyuki
Mochizuki, Kan-ichiro
[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017, 38 (15) : 4348 - 4361
[9] Improving the accuracy of forest aboveground biomass using Landsat 8 OLI images by quantile regression neural network for Pinus densata forests in southwestern China
Zhang, Xiaoli
Li, Lu
Liu, Yanfeng
Wu, Yong
Tang, Jing
Xu, Weiheng
Wang, Leiguang
Ou, Guanglong
[J]. FRONTIERS IN FORESTS AND GLOBAL CHANGE, 2023, 6
[10] Tracking Phytoplankton Biomass Amid Wildfire Smoke Interference Using Landsat 8 OLI
Mohammady, Sassan
Erratt, Kevin J.
Creed, Irena F.
[J]. Remote Sensing, 2024, 16 (19)

← 1 2 3 4 5 →