Proceedings
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| Filter results4 paper(s) found. |
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1. Optimizing Site-Specific Adaptive Management Using A Probabilistic Framework: Evaluating Model Performance Using Historic DataAgricultural producers are tasked with managing crop yield responses to nitrogen (N) within systems that have high levels of spatial (biophysical), climatic, and price uncertainty. To date, the outcome of most variable rate application (VRA) research has focused on the spatial dimension, proposing optimal fertilizer prescription maps that can be applied year after year. However, temporally static prescriptions can result in suboptimal outcomes, particularly if they do... L.J. Rew, B.D. Maxwell, P.G. Lawrence |
2. 3-Dimension Reconstruction Of Cactus Using Multispectral ImagesUsing 3D reconstruction result to investigate plant morphology has been a focus of virtual plant. And multispectral imaging has proved to carried biological information in quite a lot work. This paper present a idea to investigate chlorophyll spatial variability of cactus using a bunch of multispectral images. 46 multispectral images are taken at equally distributed angles surrounding the tree and have over 80% overlap. Structure from motion approach has been used... F. Liu, Y. He, Y. Zhang, L. Tan, Y. Zhang, L. Jiang |
3. Agrosense: AI-enabled Sensing for Precision Management of Tree CropsMonitoring the tree inventory and canopy density and height frequently is critical for researchers and farm managers. However, it is very expensive and challenging to manually complete these tasks weekly. Therefore, a low-cost and artificial intelligence (AI) enhanced sensing system, Agrosense, was developed for tree inventory, canopy height measurement, and tree canopy density classification in this study. The sensing system mainly consisted of four RGB-D cameras, two Jetson Xavier NX, and one... C. Zhou, Y. Ampatzidis, H. Guan, W. Liu, A. De Oliveira Costa Neto, S. Kunwar, O. Batuman |
4. Using Floral Bract Withering to Identify Green-ripe Pineapples with Deep LearningGreen-ripe pineapples are ideal for extended transportation and storage during summer but are challenging to identify during on-site harvesting. This study introduces a deep learning-based approach using the YOLO-NAS algorithm to detect green-ripe pineapples by analyzing the withering rate of floral bracts at the fruit's base. A high- mounted tracked vehicle, equipped with an Intel D405 depth camera, captures images at a distance of 300–400 mm as it navigates pineapple ridges. The system... S. Chen |