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Setiyono, T
Schueller, J.K
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Authors
Choi, D
Lee, W
Schueller, J.K
Ehsani, R
Roka, F.M
Ritenour, M.A
Barbosa, M
Duron, D
Rontani, F
Bortolon, G
Moreira, B
Oliveira, L
Setiyono, T
Shiratsuchi, L
Silva, R.P
Holland, K.H
Topics
Sensor Application in Managing In-season Crop Variability
Artificial Intelligence (AI) in Agriculture
Type
Oral
Year
2016
2024
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1. A Precise Fruit Inspection System for Huanglongbing and Other Common Citrus Defects Using GPU and Deep Learning Technologies

World climate change and extreme weather conditions can generate uncertainties in crop production by increasing plant diseases and having significant impacts on crop yield loss. To enable precision agriculture technology in Florida’s citrus industry, a machine vision system was developed to identify common citrus production problems such as Huanglongbing (HLB), rust mite and wind scar. Objectives of this article were 1) to develop a simultaneous image acquisition system using multiple cameras... D. Choi, W. Lee, J.K. Schueller, R. Ehsani, F.M. Roka, M.A. Ritenour

2. Multi-sensor Remote Sensing: an AI-driven Framework for Predicting Sugarcane Feedstock

Predicting saccharine and bioenergy feedstocks in sugarcane enables stakeholders to determine the precise time and location for harvesting a better product in the field. Consequently, it can streamline workflows while enhancing the cost-effectiveness of full-scale production. On one hand, Brix, Purity, and total reducing sugars (TRS) can provide meaningful and reliable indicators of high-quality raw materials for industrial food and fuel processing. On the other hand, Cellulose, Hemicellulose,... M. Barbosa, D. Duron, F. Rontani, G. Bortolon, B. Moreira, L. Oliveira, T. Setiyono, L. Shiratsuchi, R.P. Silva, K.H. Holland