RPAS-Assisted Validation of a Model Depicting Crop Residue Cover and its Effects on Canola Growth and Yield in Saskatchewan

SaskCanola（SC）、SaskWheat（SW）、Prairie Agriculture Machine Institute（PAMI）和Saskatchewan Sask Polytech（Sask Polytech）渴望继续进行与作物残茬覆盖测量有关的研究 (CRC)以及CRC管理策略对油菜生长和生产力的修正作用。我们最初 一年的研究表明，在量化CRC和解释CRC之间的相互作用方面， 并独立应用管理治疗。利用遥控飞机系统对整个 字段，然后使用各种后处理算法对CRC进行分类，2020年的目标将是提高 预测准确率超过2019年确定的80%。通过模型优化和验证，我们 希望建立一种定量、可重复性好的农作物残体评价方法， 为改进保护性耕作做法奠定了基础。

SaskCanola (SC), SaskWheat (SW), Prairie Agriculture Machine Institute (PAMI) and Saskatchewan Polytechnic (Sask Polytech) are eager to continue research pertaining to the measurement of crop residue cover (CRC) and the modifying effects of CRC management strategies on canola growth and productivity. Our initial year of research showed promising results in quantifying CRC and interpreting the interaction between CRC and independently applied management treatments. Using remotely piloted aircraft systems to image entire fields, then classify CRC using a variety of post-processing algorithms, the goal for 2020 will be to elevate predictive accuracy beyond the 80% value established in 2019. Through model refinement and validation, we hope to establish a quantitative and repeatable method for crop residue assessment that might serve as a foundation for improving conservation tillage practices.