An Artificial Intelligence model will be developed to adapt crop biophysical models so they can determine current and predict future soil-water, nitrogen and fruit load of cotton and horticulture plants based on day of the season, weather data and visual plant response captured using fixed field based cameras. A data base (weather, soil electrical conductivity, soil-water values, daily crop coefficient, NDVI, plant density, crop height) has been collected on a series of fertiliser and irrigation experiments using the remote sensing technique such as hyper-spectral imaging, visible and infrared sensors and satellite image. A spatial analysis was conducted to compare measured spatial variability and variability estimated using spatial interpolation for different locations in the field. Based on this data base, the crop and soil situations are monitored on the time series corresponding to different trials of fertilizer rate and irrigation scenarios. From this project an artificial intelligence model that is capable of autonomous or semi-autonomous decision making will be developed to facilitate the development of automated irrigation and fertiliser applications to field crops.

Professor Keith Pembleton

• Professor Keith Pembleton

• Computational Engineering and Science Research Centre
• Institute for Agriculture and the Environment
• National Centre for Engineering in Agriculture

• Artificial Intelligence and Image Processing
• Crop and Pasture Production

• Doctor of Philosophy (DPHD)