Prediction of Crop Growth Monitoring by Using Spectral Data and Soft Computing on Wheat

رسولی شربیانی, ولی and عباسپور گیلانده, یوسف and فیض اله زاده اردبیلی, سینا and عمید, سما (2017) Prediction of Crop Growth Monitoring by Using Spectral Data and Soft Computing on Wheat. University of Mohaghegh Ardabili, University of Mohaghegh Ardabili.

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Abstract

Recent advances in precision agriculture technology have led to the development of ground-based active remote sensors (or crop canopy sensors) that calculate normalized difference vegetation index (NDVI) readings. Vegetation indices obtained from remote sensing data can help to summarize climate conditions. Artificial neural networks (ANNs), another Artificial Intelligence approach, are one of the most efficient computational methods rather than other analytical and statistical techniques for spectral data. This study was employed experimental radial basis function (RBF) of ANN models and adaptive neural-fuzzy inference system to design a network in order to predict the SPAD, protein content and grain yield of wheat plant based on spectral reflectance value and to compare two models. Results indicated that the obtained results of RBF method with high average correlation coefficient (0.997, 0.997 and 0.996 in 2011 for SPAD, yield and protein, respectively and 0.994, 0.995 and 0.997 in 2012) and low average RMSE (0.271, 103.315 and 0.111 in 2011 for SPAD, yield and protein, respectively and 0.407, 105.482 and 0.096 in 2012) has the high accuracy and high performance compared to ANFIS models.

Item Type: Other
Persian Title: بررسی امکان پیش بینی برخی ویژگی های گیاهی گندم با استفاده از داده های طیف سنجی و محاسبات نرم
Persian Abstract: -
Subjects: Research Projects
Divisions: Subjects > Faculty of Agricultural Sciences & Natural Resources > Department of Biosystem
Faculty of Agricultural Sciences & Natural Resources > Department of Biosystem
Date Deposited: 03 Dec 2019 05:31
Last Modified: 03 Dec 2019 05:31
URI: http://repository.uma.ac.ir/id/eprint/8303

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