Dr. Khaled Hussein
Eng. Nour Elhuda Ashraf
In the past centuries, Wheat planting has been deteriorated due to the growing of Wild Oat ( Avena Fatua ) plant and rust disease with it which on expansion do reduce the wheat production by 93% for every square-meter. Although it’s going to be hard to differentiate between the two plants, we will detect both wild oats and rust in wheat by using image processing and deep learning at the beginning of the farming process, to decrease their appearance. Thus, if the farmer didn’t recognize wild oats within the first 30 days of growing with the Wheat, it kills the crop and spread about 100 to 150 of the Oats seeds. Hence, detection in the early stages is a must. Pervasion in wheat fields can diminish yield by as much as 80%. Our target is to differentiate between both wild oat and wheat and decreasing rust disease.
1- Simplify the identification operation so that the device can be used by anyone.
2- Identification of wild oats at an early stage so that we can avoid destroying the soil, spreading the seeds, or lowering the wheat’s maintenance estimate while still having a healthy wheat.
3- The area of Wheat Rust within the wheat plants must be monitored as soon as possible by detecting it in its early stages, thus limiting the spread of wild oat.
1- To decrease the number of Wild Oats and rust in Wheat
2- Increase the crop production
3- Producing it by the best quality and in larger crop weight output
4- Increasing the farmer income
5- Reduce the work and the time he takes in the field to recognize the Wild Oats
6- Decrease the money that the farmer pays for using the expensive high-quality chemicals to control Wild Oats.
Documents and Presentations
You will find here the documents and presentation for our Thesis