Machine vision-based high-resolution weed mapping and patch-sprayer performance simulation

L. Tang, L.F. Tian, B.L. Steward

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

An experimental machine vision-based patch-sprayer was developed. This sprayer was primarily designed to do real-time weed density estimation and variable herbicide application rate control. However, the sprayer also had the capability to do high-resolution weed mapping if proper mapping techniques were integrated. Two weed mapping methods were developed. One was a GPS signal based off-line weed mapping; another one was radar distance measurement-based on-line weed mapping. The high-resolution weed maps provided evidence to further support the patch-spraying concept. Randomly sampled field images were processed with different nozzle control zone sizes and thresholding methods to simulate sprayer performance. Fundamental system design strategies regarding these two factors were obtained through simulation. System design techniques, including system construction, weed sensing and crop-row detection algorithms were reported.
Original languageEnglish
Pages (from-to)317-318
JournalTransactions of the ASAE
Volume108
Issue number2
Publication statusPublished - 1999

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