Early pest detection in cannabis plants with multispectral imaging: artificial intelligence and machine learning

Ahmed Si Ahmed; Simon Pierre Tchang; frederic McCune; James Eaves; Valérie Fournier; Xavier Maldague

doi:10.1117/12.2663834

12 June 2023 Early pest detection in cannabis plants with multispectral imaging: artificial intelligence and machine learning

Ahmed Si Ahmed, Simon Pierre Tchang, frederic McCune, James Eaves, Valérie Fournier, Xavier Maldague

Author Affiliations +

Proceedings Volume 12536, Thermosense: Thermal Infrared Applications XLV; 1253610 (2023) https://doi.org/10.1117/12.2663834
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

Cannabis pests such as spider mites, thrips and aphids cause enormous damage to crops. The lack of regulations regarding pesticides that can be used on cannabis, is a source of concerns for growers and the federal government since no regulations are yet in place. In order to favor prevention, as opposed to curing the problem, we developed a system based on convolutional neural networks and transfer learning techniques trained on multispectral images that is capable of detecting the early state of parasitic stress on cannabis plants instead of giving an operator the difficult task of visually detect whether the plant is already infected or not. This gives the grower time to remove pest-infected plants before they spread throughout the whole crop.

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Ahmed Si Ahmed, Simon Pierre Tchang, frederic McCune, James Eaves, Valérie Fournier, and Xavier Maldague "Early pest detection in cannabis plants with multispectral imaging: artificial intelligence and machine learning", Proc. SPIE 12536, Thermosense: Thermal Infrared Applications XLV, 1253610 (12 June 2023); https://doi.org/10.1117/12.2663834

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