In-field proximal sensing of most major crops nutrients still remains an economical and technical challenge. For this purpose, the use of effective multi-excitation fluorescence and reflectance wavelengths is explored...In-field proximal sensing of most major crops nutrients still remains an economical and technical challenge. For this purpose, the use of effective multi-excitation fluorescence and reflectance wavelengths is explored in this work on Okra plant. Visible-near infrared (400 - 1000 nm) reflectance and multi-fluorescence data were collected at leaf scale in a chemically fertilized field by using an USB spectrometer mounted with an Arduino-based LED driver clip. N, P, K and Ca content of samples leaves were measured using reference methods. Average pods yield and leaves macronutrients content were calibrated using IRIV-PLS regression after spectra pretreatments. Single informative wavelengths bands in reflectance, red and far-red fluorescences were selected for building yield and macronutrient content models. We showed that flowering stage was more suitable for yield prediction. Moderately useful macronutrient models were found in Ca content (RPDval = 1.93, rP = 0.818) and potassium content with RPDval = 1.8, rP = 0.88. P and N yielding prediction performance of RPDval = 1.61 (rP = 0.718 ) and RPDval = 1.46 (rP = 0.56) respectively were less accurate. This study demonstrates potentiality of fluorescence and reflectance spectroscopy for accurate estimation of leaf macronutrient content and crop yield. High selectivity obtained from resulted spectral bands could lead to the development of reliable, rapid and cost-effective devices for nutrient diagnosis.展开更多
文摘In-field proximal sensing of most major crops nutrients still remains an economical and technical challenge. For this purpose, the use of effective multi-excitation fluorescence and reflectance wavelengths is explored in this work on Okra plant. Visible-near infrared (400 - 1000 nm) reflectance and multi-fluorescence data were collected at leaf scale in a chemically fertilized field by using an USB spectrometer mounted with an Arduino-based LED driver clip. N, P, K and Ca content of samples leaves were measured using reference methods. Average pods yield and leaves macronutrients content were calibrated using IRIV-PLS regression after spectra pretreatments. Single informative wavelengths bands in reflectance, red and far-red fluorescences were selected for building yield and macronutrient content models. We showed that flowering stage was more suitable for yield prediction. Moderately useful macronutrient models were found in Ca content (RPDval = 1.93, rP = 0.818) and potassium content with RPDval = 1.8, rP = 0.88. P and N yielding prediction performance of RPDval = 1.61 (rP = 0.718 ) and RPDval = 1.46 (rP = 0.56) respectively were less accurate. This study demonstrates potentiality of fluorescence and reflectance spectroscopy for accurate estimation of leaf macronutrient content and crop yield. High selectivity obtained from resulted spectral bands could lead to the development of reliable, rapid and cost-effective devices for nutrient diagnosis.
