Background Spectral imaging is a key method for high throughput phenotyping that can be related to a large variety of biological parameters. The Normalised Difference Vegetation Index (NDVI), uses specific wavelengths to compare crop health and performance. Increasing the accessibility of spectral imaging systems through the development of small, low cost, and easy to use platforms will generalise its use for precision agriculture. We describe a method for using a dual camera system connected to a Raspberry Pi to produce NDVI imagery, referred to as NDVIpi. Spectral reference targets were used to calibrate images into values of reflectance, that are then used to calculated NDVI with improved accuracy compared with systems that use single references/standards. Results NDVIpi imagery showed strong performance against standard spectrometry, as an accurate measurement of leaf NDVI. The NDVIpi was also compared to a relatively more expensive commercial camera (Micasense RedEdge), with both cameras having a comparable performance in measuring NDVI. There were differences between the NDVI values of the NDVIpi and the RedEdge, which could be attributed to the measurement of different wavelengths for use in the NDVI calculation by each camera. Subsequently, the wavelengths used by the NDVIpi show greater sensitivity to changes in chlorophyll content than the RedEdge. Conclusion We present a methodology for a Raspberry Pi based NDVI imaging system that utilizes low cost, off-the-shelf components, and a robust multi-reference calibration protocols that provides accurate NDVI measurements. When compared with a commercial system, comparable NDVI values were obtained, despite the fact that our system was a fraction of the cost. Our results also highlight the importance of the choice of red wavelengths in the calculation of NDVI, which resulted in differences in sensitivity between camera systems.

For many growers, established and newcomers, the determination of the optimal light spectrum for growing crops can be challenging and highly dependent on crop species and variety. With the increased popularity of LED lighting, the capability to fine-tune a light spectrum has never been greater. Here, we break down the fundamental roles of the major spectral regions (ultraviolet, blue, green, red, and far-red) and explain the effect on plant growth, yield, and crop quality (i.e., greenness, coloration, flavor) when applied in isolation or combination. The first part of this review examines plant responses to light stimuli and the potential benefits for growers. We also discuss how LED lighting can be used to manipulate plant growth and development to improve crop productivity and/or value. We suggest some basic LED light “recipes” that could be used by growers to deliver specific growth effects and provide an easy-to-use visual reference guide. The second part of this review explores the impact of light treatments on crop productivity. Increased productivity is weighed against the ongoing costs associated with various light treatments, modeled in the context of UK electricity pricing.

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