Proceedings
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| Filter results2 paper(s) found. |
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1. Development and Field Validation of a Mid-Infrared Proximal Sensing System for In-Season, On-Vine Monitoring of Grape CompositionAccurate in-season monitoring of grape composition is important for precision agriculture, as it supports timely decisions on crop management and harvest scheduling. In practice, however, commonly used approaches for assessing internal quality—such as refractometry and near-infrared (NIR) spectroscopy—are often applied to harvested samples, which limits their use for truly non-destructive measurements on developing fruit in the field. We have been developing a novel mid-infrared... H. Furukawa |
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 |