Identification of novel optical plant signals for the early detection of cannabis pests

识别新型光学植物信号以早期检测大麻害虫

• 批准号: 538505-2019
• 负责人: Fournier, Valerie
• 金额: $ 5.19万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=699511
• 关键词: Identification; novel; optical; plant; signals

Today, cannabis growers use visual inspection to identify pest and fungus threats. Those visual judgments also guide a grower's estimate of the potential economic risk and the appropriate treatment. Growers themselves emphasize that this approach is subjective, likely often inaccurate, and acknowledge that by the time the threat is visible to a human, the outbreak's economic risk is already substantial. The purpose of this research project is leveraging a collaboration between experts in entomology, agricultural economics, and visual recognition, to identify new optical "plant signals" based on spectral signatures that can be rapidly and inexpensively analyzed to enable early and accurate identification of threats to cannabis plants, as well as classify those threat in terms of potential economic damage. Visual recognition methods are increasingly being developed to classify plant health and threats. However, an important shortcoming of current approaches is that the algorithms are trained using visual signals that human-scouts are able to identify in the field. This prevents an algorithm from identifying new signals that may be invisible to a human. Moreover, virtually no research in this area has been conducted for cannabis since it only recently became legal in Canada. To address these shortcomings, we will use controlled experiments to expose cannabis plants to different levels of infestation and then train a machine learning algorithm to identify new signals for cannabis that reveal the infestation and its severity before it would otherwise be apparent to the human eye.

今天，大麻种植者使用目视检查来识别害虫和真菌的威胁。这些视觉判断也指导种植者估计潜在的经济风险和适当的处理。种植者自己强调，这种方法是主观的，可能经常不准确，并承认，当威胁对人类可见时，疫情的经济风险已经很大。该研究项目的目的是利用昆虫学，农业经济学和视觉识别专家之间的合作，根据光谱特征识别新的光学“植物信号”，这些信号可以快速，廉价地进行分析，以便能够早期准确地识别大麻植物的威胁，并根据潜在的经济损失对这些威胁进行分类。视觉识别方法正在越来越多地被开发来对植物健康和威胁进行分类。然而，当前方法的一个重要缺点是，这些算法是使用人类侦察员能够在现场识别的视觉信号进行训练的。这防止算法识别可能对人类不可见的新信号。此外，由于大麻最近才在加拿大成为法律的，因此几乎没有对大麻进行过这方面的研究。为了解决这些缺点，我们将使用受控实验将大麻植物暴露在不同程度的侵扰中，然后训练机器学习算法来识别大麻的新信号，这些信号在人眼明显之前揭示了侵扰及其严重程度。