本地化多光谱作物成像传感器:成本效益高的植物胁迫和病害传感器的工程和验证

B. Grieve, S. Hammersley, Anne-Katrin Mahlein, E. Oerke, H. Goldbach
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引用次数: 12

摘要

作物和土壤的近距离高光谱和多光谱成像通过对植物病原体、病毒和害虫的实时精确管理以及对有益作物性状的非破坏性高通量筛选,为优化耕地生产和种子育种的可持续集约化提供了巨大的潜力。这些机会最近已被报道,并且是工业界和学术界正在进行的研发主题。大型商业最终用户通过与田间和温室机械相结合而广泛采用这项技术受到成本和设备可靠性的限制。在将其扩展到消费者市场和小农时,它进一步受到光谱和空间分辨率,功率预算和尺寸的限制。这项研究首次验证了多光谱传感器系统架构,利用专有的窄带led和硅c - mos成像探测器,能够取代传统的和更昂贵的线扫描高光谱成像系统,当在作物冠层附近(c. 1-2米)工作时。这是通过将新led传感器系统的原型版本与参考实验室高光谱成像单元的数据进行比较而实现的,参考实验室高光谱成像单元之前是为作物表型而开发的,并在整个大麦和甜菜植物中早期检测两种真菌病原体传播的疾病。作物和病害的选择与参考高光谱单元重复了早期的研究,并用于证明新的led传感器系统对谷类和块茎类作物的普遍适用性。结果表明,新方法可以提供与参考系统相当质量的数据,用于现场任务,并为更高的灵敏度和空间分辨率提供了机会。然后讨论了在商业产品中应用新的多光谱led系统的未来潜力。
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Localized multispectral crop imaging sensors: Engineering & validation of a cost effective plant stress and disease sensor
Close proximity hyperspectral and multispectral imaging of crops and soils offers significant potential to optimize sustainable intensification of arable produce and seeds breeding, through the real-time precision management of plant pathogens, viruses and pests and the non-destructive high throughput screening for beneficial crop traits. These opportunities have been recently reported and are the subject of ongoing R&D within industry and academia. The broad uptake of the technology by large commercial end-users, through integration with in-field and glasshouse machinery, is limited by cost and equipment reliability. It is further restricted by spectral and spatial resolution, power budget and size, when extending its applicability to consumer markets and small-holder farmers. This study verifies, for the first time, that multispectral sensor systems architectures, exploiting proprietary narrowband LEDs and silicon C-MOS imaging detectors, are capable of substituting for conventional and more expensive line-scanning hyperspectral imaging systems when operated in close proximity (c. 1-2m) of a crop canopy. This was achieved by comparing the data from a prototype version of the new LED-sensor system versus a reference laboratory hyperspectral imaging unit, which was previously developed for crop phenotyping, and the early detection of two fungal pathogen borne diseases in whole barley and sugar beet plants. The choice of crops and diseases replicates earlier studies, with the reference hyperspectral unit, and serves to demonstrate the generic applicability of the new LED-sensor system to cereal and tuber classes of crops. The results indicate that the new approach can deliver data of comparable quality to that of the reference system, for in-field duties, and offers the opportunity for higher sensitivity and spatial resolution. Future potential to apply the new multispectral, LED-based system within commercial products is then discussed.
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