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Photonics in Wageningen



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Grassland management has a large influence on the operating cost and environmental impact of dairy farms and requires accurate, detailed, and timely information about the yield and quality of grass. Our objective was to evaluate imaging spectroscopy as a means to obtain accurate, detailed, and rapid measurements of grass yield and quality. The work consisted of three steps. In the first step, a new mobile measurement system comprising several hyperspectral sensors was constructed and calibrated on measurements collected in six field experiments in the Netherlands in 2 yr. A partial least squares regression model was used to fit parameters derived from hyperspectral images to values of DM (dry matter) yield and quality obtained through destructive sampling. Leave-k-out cross validation showed relative errors of prediction of 8 to 22% (167-477 kg DM ha(-1) absolute) for DM yield, 21% (0.07 absolute) for the fraction of clover in DM, 6 to 12% for nutrient concentration, 15 to 16% for sugar concentration, and 3 to 5% for feeding values. In the second step, the measurement system was used to predict grassland yield and quality on fields from two farms. In the third step, the absence of calibration data for a specific date was simulated with a leave-harvest-out procedure. Predictions of absolute values were strongly biased due to system instability. Prediction of relative values was good, with a mean absolute error of 183 kg ha(-1) for DM yield. The instability of the measurement system requires that duosampling must be used for prediction of absolute values.
Conference Paper
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In the Interreg IV, EU project 'The healthy greenhouse' a new integral crop protection system is developed. Part of the project is the development of autonomous robots for monitoring individual plants. One of the sensors for monitoring is an application- specific multispectral camera for detection of fungal diseases. In this paper the development of this camera is described, starting from a laboratory based hyperspectral system. Using feature selection the number of bands is reduced to eight. Results from the analysis of the reduced images show that 90% of the pixels are properly classified. These bands will be validated in a fast filter wheel multispectral system in the greenhouse. Final goal of the project is real-time multispectral camera using micro patterned coatings on individual pixels.
In this paper we present a novel method for automated detection of Mycosphaerella melonis infected cucumber fruits. The two-step method consists of machine learning approach using: shape based features extracted from cucumber color images and light transmission spectra based features. The automated detection rate was compared to the manual detection rate of the human workers. Our automated method reached the 95% detection accuracy, which is comparable to the manual detection accuracy of 96%.
Tomatoes (Lycopersicum esculentum, Mill. cv. Capita F1) were harvested at different ripening stages. Spectral images from 400 to 700 nm with a resolution of 1 nm were recorded. After recording, samples were taken from the fruit wall and the lycopene, lutein, -carotene, chlorophyll-a and chlorophyll-b concentrations were measured using HPLC. The relation between the compound concentrations measured with HPLC and the spectral images was analyzed using partial least squares (PLS) regression. The Q2 error of the predicted lycopene concentration, determined from the PLS procedure, was 0.95 on a pixel basis, and 0.96 on a tomato basis. The Q2 error of the other compounds varied from 0.73 to 0.84. Pixel-based regression made it possible to construct concentration images of the tomatoes, which showed non-uniform ripening. The method can be applied in a conveyor belt system using sorting criteria such as concentration of the compounds and the uniformity of the distribution of the concentrations.