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Publisher: Basel : MDPI × Subject: NDVI ×

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Now showing 1 - 2 of 2
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    Comprehensive Assessment of the Dynamics of Banana Chilling Injury by Advanced Optical Techniques
    (Basel : MDPI, 2021) Herppich, Werner B.; Zsom, Tamás
    Green‐ripe banana fruit are sensitive to chilling injury (CI) and, thus, prone to postharvest quality losses. Early detection of CI facilitates quality maintenance and extends shelf life. CI affects all metabolic levels, with membranes and, consequently, photosynthesis being primary targets. Optical techniques such as chlorophyll a fluorescence analysis (CFA) and spectroscopy are promising tools to evaluate CI effects in photosynthetically active produce. Results obtained on bananas are, however, largely equivocal. This results from the lack of a rigorous evaluation of chilling impacts on the various aspects of photosynthesis. Continuous and modulated CFA and imaging (CFI), and VIS remission spectroscopy (VRS) were concomitantly applied to noninvasively and comprehensively monitor photosynthetically relevant effects of low temperatures (5 °C, 10 °C, 11.5 °C and 13 °C). Detailed analyses of chilling‐related variations in photosynthetic activity and photoprotection, and in contents of relevant pigments in green‐ripe bananas, helped to better understand the physiological changes occurring during CI, highlighting that distinct CFA and VRS parameters comprehensively reflect various effects of chilling on fruit photosynthesis. They revealed why not all CFA parameters can be applied meaningfully for early detection of chilling effects. This study provides relevant requisites for improving CI monitoring and prediction.
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    Strategy for the development of a smart NDVI camera system for outdoor plant detection and agricultural embedded systems
    (Basel : MDPI, 2013) Dworak, Volker; Selbeck, Joern; Dammer, Karl-Heinz; Hoffmann, Matthias; Zarezadeh, Ali Akbar; Bobda, Christophe
    The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique additionally reduces the amount of energy required in terms of fuel. Although research activities have increased in this field, high camera prices reflect low adaptation to applications in all fields of agriculture. Smart, low-cost cameras adapted for agricultural applications can overcome this drawback. The normalized difference vegetation index (NDVI) for each image pixel is an applicable algorithm to discriminate plant information from the soil background enabled by a large difference in the reflectance between the near infrared (NIR) and the red channel optical frequency band. Two aligned charge coupled device (CCD) chips for the red and NIR channel are typically used, but they are expensive because of the precise optical alignment required. Therefore, much attention has been given to the development of alternative camera designs. In this study, the advantage of a smart one-chip camera design with NDVI image performance is demonstrated in terms of low cost and simplified design. The required assembly and pixel modifications are described, and new algorithms for establishing an enhanced NDVI image quality for data processing are discussed.