12/19/2023 0 Comments Multispec 4c 1.1Bogota, Colombia: Universidad Nacional de Colombia. Weed population dynamics in rice fields in the center, plateau and north of Tolima Region (in Spanish, Dinámica Poblacional de Malezas del Cultivo de Arroz en las Zonas Centro, Meseta y Norte del Departamento del Tolima). International Journal of Remote Sensing, 19(5), 823–854. Multisensor image fusion in remote sensing: Concepts. Computers and Electronics in Agriculture, 139, 224–230. Evaluation of hierarchical self-organising maps for weed mapping using UAS multispectral imaginery. Pantazi, X., Tamouridou, A., Alexandridis, T., Lagopodi, A., Kashefi, J., & Moshou, D. Computer processing of remotely-sensed images: An introduction. Weed detection by UAV: Simulation of the impact of spectral mixing in multispectral images. Louargant, M., Villette, S., Jones, G., Vigneau, N., Paoli, J., & Gée, C. IEEE international conference on robotics and automation, ICRA 2017 (pp. UAV-based crop and weed classification for smart farming. Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R., & Stachniss, C. Weed detection for site-specific weed management: Mapping and real-time approaches. Pixel-level image fusion: A survey of the state of the art. Journal of Tropical Agricultural Science, 32(2), 305–316. Critical period of weed competition in direct seeded rice under saturated and flooded conditions. Juraimi, A., Mohamad, M., Begum, M., Anuar, A., & Azmy, M. Timing of weed management and yield losses due to weeds in irrigated rice in the Sahel. Johnson, D., Wopereis, M., Mbodi, D., Diallo, S., Power, S., & Haefele, S. Photogrammetric Engineering & Remote Sensing, 75, 1213–1223. A wavelet and IHS integration method to fuse high resolution SAR with moderate resolution multispectral images. IEEE Transactions on Systems, Man, and Cybernetics, SMC (3), 610–621. Textural features for image classification. Haralick, R., Shanmugam, K., & Dinstein, I. Utility of multispectral imagery for soybean and weed species differentiation. Remote Sensing of Environment, 80(1), 76–87. Novel algorithms for remote estimation of vegetation fraction. Zheng (Ed.), Image fusion and its applications. Image fusion for remote sensing applications. International Journal of Image and Data Fusion, 1(1), 25–45.įonseca, L., Namikawa, L., Castejon, E., Carvalho, L., Pinho, C., & Pagamisse, A. Multi-sensor image fusion for pansharpening in remote sensing. Oxford, UK: Willey-Blackwell.Įhlers, M., Klonus, S., Astrand, P., & Rosso, P. Row spacing and weed control timing affect yield of aerobic rice. Applying neural networks to hyperspectral and multispectral field data for discrimination of cruciferous weeds in winter crops. T., Gomez-Casero, M., & Lopez-Granados, F. Oxford, UK: Oxford University Press.Ĭastro, A. The best weed detection performance was obtained by the NN with the fused image, with M/M GT index between 80 and 108% and MP between 70 and 85%.īishop, C. These indices were evaluated in four validation zones using three Neural Networks (NN) detection systems based on three types of images namely, RGB, RGB + NGRDI, and fused RGB-NGRDI. Additionally, to compare the performance of the method, two indices were used, specifically, the M/ M GT index which is the percentage of detected weed area, and the MP index which indicates the precision of weed detection. To test the method, a one hectare experimental plot with rice plants at 50 DAE with Gramineae weeds was selected. Finally, the fused image is obtained by transforming the new wavelet coefficients to RGB space. From this transformation, the low–low (LL) coefficients of the NGRDI image are replaced by the LL coefficients of the I layer. The fusion method consists of decomposing the RGB image using the intensity, hue and saturation (IHS) transformation, then, a second order Haar wavelet transformation is applied to the intensity layer (I) and the NGRDI image. ![]() After analyzing the normalized difference vegetation index (NDVI) and normalized green red difference index (NGRDI) for weed detection, it was found that NGRDI presents better features. The proposed method combines the texture information given by a high resolution red–green–blue (RGB) image and the reflectance information given by a low resolution multispectral (MS) image, to obtain a fused RGB-MS image with better weed discrimination features. In this paper, a new method to fuse low resolution multispectral and high resolution RGB images is introduced, in order to detect Gramineae weed in rice fields with plants at 50 days after emergence (DAE).The images are taken from a fixed-wing unmanned aerial vehicle (UAV) at 60 and 70 m altitude.
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