Proceedings
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| Filter results2 paper(s) found. |
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1. Agronomist-in-the-Loop Semantic 3D Reconstruction of Cotton Boll Morphology from UAV Imagery for Precision AgricultureStandard aerial photogrammetry is failing precision agriculture in one specific area: the detailed morphological assessment of complex, cluttered canopies. While creating a field-level map is trivial, recovering the geometry of a single cotton boll from a drone altitude of 30 meters is often mathematically intractable for standard Structure-from-Motion (SfM) solvers. These traditional pipelines depend on pixel-perfect consistency, which breaks down amidst... P. Sundaravadivel, H. Manjunatha, S. Borah, A. Anand, A. Price, H. Torbert, L. Tamil, T. Stroud |
2. Soil Texture Classification by Image: Deep Feature Learning vs. Handcrafted Methods for Precision AgricultureAccurate soil texture classification is fundamental for precision agriculture, as it enables site-specific crop management that optimizes the utilization of agricultural resources and enhances overall crop productivity. This study presents a comparative analysis between features automatically extracted by a pre-trained SqueezeNet convolutional neural network (CNN) and three classical methods for manual feature extraction: Fast Fourier Transform (FFT), Gabor Filters, and Local Binary Patterns (LBP),... J.R. Favan , G.D. Faulin, F.M. kasita kashima, J. . Alegre, L.S. Gonçalves |