Abstract
More than half of the Australian cropping land is n o-tillage and weed control within continuous no-til lage agricultural cropping area is becoming more and more difficult. A major problem is that the heavy herb icide usage causes some of more prolific weeds becoming more resistant to the regular herbicides and therefo re more powerful and more expensive options are being pursued. To overcome such problems with aiming at the reduction of herbicide usage, this proposed res earch focuses on developing a machine vision system which can detect and mapping weeds or do spot spray. The weed detection methods described in this stud y include three aspects which are image acquisition, a new green plant detection algorithm using hybrid spectral indices and a new inter-row weed detection method taking the advantage of the location of the crop rows. The developed method could detect the weeds both during the non-growing summer period and also within the growing season until the canopy of the c rop has closed. The design of the methods focuses o n overcoming the challenges of the complex no-tillage background, the faster image acquisition speed and quicker processing time for real-time spot spray. T he experiment results show that the proposed method are more suitable for the weed detection in the no-till age background than the existing methods and could be used as a powerful tool for the weed control.
Highlights
Weeds are among the most significant and costly environmental threats in the agriculture industry worldwide
A major problem is that the heavy herbicide usage causes some of more prolific weeds becoming more resistant to the regular herbicides and more powerful and more expensive options are being pursued. To overcome such problems with aiming at the reduction of herbicide usage, this proposed research focuses on developing a machine vision system which can detect and mapping weeds or do spot spray
The weed detection methods described in this study include three aspects which are image acquisition, a new green plant detection algorithm using hybrid spectral indices and a new inter-row weed detection method taking the advantage of the location of the crop rows
Summary
Weeds are among the most significant and costly environmental threats in the agriculture industry worldwide. Through the experiment and the study of the previous work, it is found that the combination of using the visible image at 400 to 700 nm spectral band and the near infrared images at 750 to1000 nm spectral band can significantly improve the accuracy of the weed detection than just using one type of the images Based on this fact, the mechanism of the image acquisition and the algorithm are designed. Based on the green plant detection algorithm, a new inter-row weed detection algorithm was developed This algorithm uses the combination of the crop row detection technology and morphological processing method to separate the weed from the crops.
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