Full Length Research Paper
References
|
Andrade LN, Vieira TGC, Lacerda WS, Volpato MML, Davis Junior CA(2013). Application of Artificial Neural Network in the Classification of Coffee Areas in Machado, Minas Gerais State. Coffee Sci. 8(1):78-90. |
|
|
Antunes JFG, Mercante E, Esquerdo JCDM, Lamparelli RA, Rocha JV (2012). Soybean crop area estimation through image classification normalized by the error matrix. Pesqui. Agropecu. Bras. 47(9):1288-1294. |
|
|
Bsaibes A, Courault D, Baret F, Weiss M, Olioso A, Jacob F, Hagolle O, Marloie O, Bertrand N, Desfond V, Kzemipour F (2009). Albedo and LAI estimates from FORMOSAT-2 data for crop monitoring. Remote Sens. Environ. 113(4):716-729. |
|
|
Chagas CS, Vieira CAO, Fernandes Filho EI, Carvalho Júnior W (2009). Use of artificial neural networks in the classification of degradation levels of pastures. Rev. Bras. Eng. Agríc. Amb. 13(3):319-327. |
|
|
Congalton RG, Green K (2009). Assessing the accuracy of remotely sensed data: Principles and practices. 2nd ed. Boca Raton, FL, USA: CRC Press.; 183 p. |
|
|
Duveiller G, Baret F, Defourny P (2011). Crop specific green area index retrieval from MODIS data at regional scale by controlling pixel-target adequacy. Remote Sens. Environ. 115(10):2686-2701. |
|
|
Elizondo DA, McClendon RW, Hoogenboom G (1994). Neural network models for predicting flowering and physiological maturity of soybean. Trans. ASABE 37(3):981-988. |
|
|
Foody GM (2002). Status of land cover classification accuracy assessment. Remote Sens. Environ. 80(1):185-201. |
|
|
Freitas RM, Arai E, Adami M, Souza AF, Sato FY, Shimabukuro YE, Rosa RR, Anderson LO, Rudorff BFT (2011). Virtual laboratory of remote sensing time series: visualization of MODIS EVI2 data set over South Am. J. Comput. Interdisc. Sci. 2(1):57-68. |
|
|
Haykin S (2008). Redes Neurais: princípios e prática. Trad. Paulo Martins Engel. 2nd ed. Porto Alegre: Bookman; p. 893. |
|
|
Hippert HS, Taylor JW (2010). An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting. Neural Net. 23(23):386-395. |
|
|
Huete AR, Liu HQ, Batchily K, Leeuwen WV (1997). A comparison of vegetation indices over a global set of TM images for EOS‑MODIS. Remote Sens. Environ. 59(3):440-451. |
|
|
Johann JA, Rocha JV, Lamparelli RAC, Duft DG (2012). Estimation of summer crop areas in the state of Paraná, Brazil, using multitemporal EVI/Modis images. Pesqui. Agropecu. Bras. 47(9):1295-1306. |
|
|
Kaul M, Hill RL, Walthall C (2005). Artificial neural networks for corn and soybean yield prediction. Agric. Syst. 85(1):1-18. |
|
|
Lamparelli RAC, Waste MOC, Marcante E (2008). Mapping of soybean (Glycine max (L.) Merr.) culture by MODIS/Terra and TM/Landsat 5: a comparative. Eng. Agríc. 28(2):334-344. |
|
|
Landis JR, Koch GG (1977). The measurement of observer agreement for categorical data. Biometrics 33(1)159‑174. |
|
|
Liao K, Paulsen MR, Reid JF, Ni BC, Bonifacio-Maghirang EP (1993). Corn kernel breakage classification by machine vision using a neural network classifier. Trans. ASAE 36(6):1949-1953. |
|
|
Liu J, Goering CE, Tian L (2001). Neural network for setting target corn yields. Trans. ASAE 44(3):705-713. |
|
|
Luo XY, Jayas DS, Symons SJ (1999). Comparison of statistical and neural network methods for classification of cereal grains using machine vision. Trans. ASAE 42(2):413-419. |
|
|
Nanni MR, Demattê JAM (2006). Soil line behavior obtained by laboratorial spectroradiometry for different soil classes. Rev. Bras. Ciênc. Solo 30(6):1031-1038. |
|
|
Pan Y, Li L, Zhang J, Liang S, Zhu X, Sulla-Menashe D (2012). Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index. Remote Sens. Environ. 119(1):232-242. |
|
|
Pax-Lenney M, Woodcock CE (1997). The effect of spatial resolution on the ability to monitor the status of agricultural lands. Remote Sens. Environ. 61(2):210-220. |
|
|
Peng Y, Gitelson AA (2012). Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content. Remote Sens. Environ. 117(1):440-448. |
|
|
Rizzi R, Risso J, Epiphanio RDV, Rudorff BFT, Formaggio AR, Shimabukuro YE, Fernandes SL (2009). Estimativa da área de soja no Mato Grosso por meio de imagens MODIS. In Proceedings of the 14th Brazilian Remote Sensing Symposium, Natal, RN, Brazil, 25–30 April 2009; INPE: São José dos Campos, SP, Brazil, pp. 387-394. |
|
|
Rudorff CM, Rizzi R, Rudorff BFT, Sugawara LM, Vieira CAO (2007). Spectral-temporal response surface of MODIS sensor images for soybean area classification in Rio Grande do Sul State. Ciênc. Rural 37(1):118-125. |
|
|
Silva Junior CA, Frank T, Rodrigues TCS (2014). Discrimination of soybean areas through images EVI/MODIS and analysis based on geo-object. Rev. Bras. Eng. Agríc. Amb. 18(1):44-53. |
|
|
Wu B, Li Q (2012). Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes. Int. J. Appl. Earth Observ. Geoinf. 16(1):101-112. |
|
|
Xiao X, Boles S, Frolking S, Salas W, Moore B, Li C, He L, Zhao R (2002). Landscape-scale characterization of cropland in China using VEGETATION sensor data and Landsat TM imagery. Int. J. Remote Sens. 23(18):3579-3594. |
|
Copyright © 2024 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0
