Site-Specific Crop Management
C. E. Goering,
J. W. Hummel, J. Liu, R. Hornbaker
University of Illinois
A study is being initiated to use neural networks to set spatially referenced crop yields on the basis of appropriate input variables. Data from some on-going experiments at the University of Illinois will be used to train the neural networks. Setting of realistic target yields is a key step in site-specific crop management.
J. W. Hummel
The goal of this project is to develop a precision herbicide application (robotic) system for low-input pest control strategies in soybean and maize production. Specific objectives include (1) evaluating the agronomic and environmental benefits of low-input herbicide applications and the status of current technology in this area, (2) developing practical real-time prototype systems for individual plant sensing and equipment control, (3) conducting on-farm trials to evaluate the prototype under the constraints of normal farm operation. With this precision system, herbicide would be only applied to the target plants in the fields.
J. W. Hummel, R. E. Wolf
Equipment and techniques are being developed to improve the application efficiency of agricultural chemicals. Droplet size spectra from various atomizers are measured to determine target coverage versus spray drift potential. Field studies of spray drift deposits are used to verify the droplet size evaluations. Sensors and automatic control systems are being developed to apply pest control substances as a function of soil organic matter, travel speed, and other input variables. Techniques for incorporation of herbicides in the soil profile of conservation tillage systems are being developed and evaluated.
J. F. Reid
In an effort to reduce herbicide application amounts, this research will integrate a machine vision-sensing system with a herbicide sprayer system to create an intelligent sensing and mapping system for pest control.
We will first develop a real-time plant-sensing and spraying system based on field experiments to characterize the plant features that can be used for crop detection. The integrated system will be field tested under varying field conditions. Outcomes of this research will provide a system for increasing farm sustainability and protecting water quality through precision application of herbicides. The vision technology will provide a precision sensor that can be further developed for precision mapping of field weed infestations and vehicle guidance.