Proceedings :: International Society of Precision Agriculture

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Hammond, J

Bantchina, B

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Authors

Karampoiki, M

Todman, L

Mahmood, S

Murdoch, A

Paraforos, D

Hammond, J

Ranieri, E

Bantchina, B

Topics

Big Data, Data Mining and Deep Learning

Proximal and Remote Sensing of Soils and Crops (including Phenotyping)

Type

Oral

Year

2022

2024

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Filter results2 paper(s) found.
1. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical Data Bayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri Download paper
2. Land Cover and Crop Types Classification Using Sentinel-2A Derived Vegetation Indices and an Artificial Neural Network Developments in remote sensing data acquisition capabilities, data processing and interpretation of ground-based, airborne and satellite observations have made it possible to couple remote sensing technologies and precision crop management systems. Land cover and crop types classification is a fundamental task in remote sensing and is crucial in various environmental and agricultural applications. Accurate and timely information on land cover and crop types is essential for land management, land-use... B. Bantchina Download paper

Filter results2 paper(s) found.

1. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical Data

Bayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri

Download paper

2. Land Cover and Crop Types Classification Using Sentinel-2A Derived Vegetation Indices and an Artificial Neural Network

Developments in remote sensing data acquisition capabilities, data processing and interpretation of ground-based, airborne and satellite observations have made it possible to couple remote sensing technologies and precision crop management systems. Land cover and crop types classification is a fundamental task in remote sensing and is crucial in various environmental and agricultural applications. Accurate and timely information on land cover and crop types is essential for land management, land-use... B. Bantchina

Download paper