The use of low-cost depth imaging sensors is investigated to automate plant pathology tests.Spatial evolution is explored to discriminate plant resistance through the hypersensitive reaction involving cotyledon loss.A...The use of low-cost depth imaging sensors is investigated to automate plant pathology tests.Spatial evolution is explored to discriminate plant resistance through the hypersensitive reaction involving cotyledon loss.A high temporal frame rate and a protocol operating with batches of plants enable to compensate for the low spatial resolution of depth cameras.Despite the high density of plants,a spatial drop of the depth is observed when the cotyledon loss occurs.We introduce a small and simple spatiotemporal feature space which is shown to carry enough information to automate the discrimination between batches of resistant(loss of cotyledons)and susceptible plants(no loss of cotyledons)with 97%accuracy and with a timing 30 times faster than for human annotation.The robustness of the method-in terms of density of plants in the batch and possible internal batch desynchronization-is assessed successfully with hundreds of varieties of Pepper in various environments.A study on the generalizability of the method suggests that it can be extended to other pathosystems and also to segregating plants,i.e.,intermediate state with batches composed of resistant and susceptible plants.The imaging system developed,combined with the feature extraction method and classification model,provides a full pipeline with unequaled throughput and cost efficiency by comparison with the state-of-the-art one.This system can be deployed as a decision-support tool but is also compatible with a standalone technology where computation is done at the edge in real time.展开更多
基金funded by ANRT(Association Nationale de la Recherche et de la Technologie),under grant agreement[2020-1738],Vilmorin-Mikado company,Limagrain group,and University of Angerssupported by the French National Research Agency(ANR),the Investments for the Future program(PIA),and the project PHENOME,ANR-11-INBS-0012.
文摘The use of low-cost depth imaging sensors is investigated to automate plant pathology tests.Spatial evolution is explored to discriminate plant resistance through the hypersensitive reaction involving cotyledon loss.A high temporal frame rate and a protocol operating with batches of plants enable to compensate for the low spatial resolution of depth cameras.Despite the high density of plants,a spatial drop of the depth is observed when the cotyledon loss occurs.We introduce a small and simple spatiotemporal feature space which is shown to carry enough information to automate the discrimination between batches of resistant(loss of cotyledons)and susceptible plants(no loss of cotyledons)with 97%accuracy and with a timing 30 times faster than for human annotation.The robustness of the method-in terms of density of plants in the batch and possible internal batch desynchronization-is assessed successfully with hundreds of varieties of Pepper in various environments.A study on the generalizability of the method suggests that it can be extended to other pathosystems and also to segregating plants,i.e.,intermediate state with batches composed of resistant and susceptible plants.The imaging system developed,combined with the feature extraction method and classification model,provides a full pipeline with unequaled throughput and cost efficiency by comparison with the state-of-the-art one.This system can be deployed as a decision-support tool but is also compatible with a standalone technology where computation is done at the edge in real time.
