Proceedings
Authors
| Filter results5 paper(s) found. |
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1. A Non-Destructive Method of Estimating Red Tip Disease in PineappleRed Tip disease typically reduces pineapple yields by up to 50%. At present, the causal agent of Red Tip disease is still unconfirmed. B... F. Abu kassim, G. Vadamalai, A. Mohd hanif, S.K. Balasundram |
2. Compensating for Soil Moisture Effects in Estimation of Soil Properties by Electrical Conductivity SensingBulk apparent soil electrical conductivity (ECa) is the most widely used soil sensing modality in precision agriculture. Soil ECa relates to multiple soil properties, including clay content (i.e., texture) and salt content (i.e., salinity). However, calibrations of ECa to soil properties are not temporally stable, due in large part to soil moisture differences between measurement dates. Therefore, the objective of this research was to investigate the effects of temporal soil moisture variations... K.A. Sudduth, N.R. Kitchen, E.D. Vories, S.T. Drummond |
3. Coupling Machine Learning Algorithms and GIS for Crop Yield Predictions Based on Remote Sensing Imagery and Topographic IndicesIn-season yield prediction can support crop management decisions helping farmers achieve their yield goals. The use of remote sensing to predict yield it is an alternative for non-destructive yield assessment but coupling auxiliary data such as topography features could help increase the accuracy of yield estimation. Predictive algorithms that can effectively identify, process and predict yield at field scale base on remote sensing and topography still needed. Machine learning could be an alternative... M.F. Oliveira, G.T. Morata, B. Ortiz, R.P. Silva, A. Jimenez |
4. Map Whiteboard As Collaboration Tool for Smart Farming Advisory ServicesPrecision agriculture, a branch of smart farming, holds great promise for modernization of European agriculture both in terms of environmental sustainability and economic outlook. The vast data archives made available through Copernicus and related infrastructures, combined with a low entry threshold into the domain of AI-technologies has made it possible, if not outright easy, to make meaningful predictions that divides individual agricultural fields into zones where variable rates... K. Charvat, R. Berzins, R. Bergheim, F. Zadrazil, J. Macura, D. Langovskis, H. Snevajs, H. Kubickova, S. Horakova, K. Charvat jr. |
5. Multi-sensor Remote Sensing: an AI-driven Framework for Predicting Sugarcane FeedstockPredicting 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 |