Yazar "Mirik, Mustafa" seçeneğine göre listele

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    Determination of water stress with spectral reflectance on sweet corn (Zea mays L.) using classification tree (CT) analysis
    (Lithuanian Research Centre Agriculture & Forestry, 2013) Genç, Levent; Nalpulat, Melis; Kızıl, Ünal; Mirik, Mustafa; Smith, Scot E.; Mendes, Mehmet
    Water stress is one of the most important growth limiting factors in crop production. Several methods have been used to detect and evaluate the effect of water stress on plants. The use of remote sensing is deemed particularly and practically suitable for assessing water stress and implementing appropriate management strategies because it presents unique advantages of repeatability, accuracy, and cost-effectiveness over the ground-based surveys for water stress detection. The objectives of this study were to 1) determine the effect of water stress on sweet corn (Zea mays L.) using spectral indices and chlorophyll readings and 2) evaluate the reflectance spectra using the classification tree (CT) method for distinguishing water stress levels/severity. Spectral measurements and chlorophyll readings were taken on sweet corn exposed to four levels of water stress with 0,33,66 and 100 % of pot capacity (PC) before and after each watering time. The results demonstrated that reflectance in the red portion (600-700 mu) of the electromagnetic spectrum decreased and increased in the near infrared (NIR) region (700-900 nm) with the increasing field capacity of water level. Reflectance measured before the irrigation was generally higher than after irrigation in the NW region and lower in the red region. However, when the four levels of PC and before or after irrigation only were compared, reflectance spectra indicated that water stressed corn plants absorbed less light in the visible and more light in the NIR regions of the spectrum than the less water stressed and unstressed plants. There was a similar trend to reflectance behaviour of water stress levels using chlorophyll readings that decreased over time. The CT analysis revealed that water stress can be assessed and differentiated using chlorophyll readings and reflectance data when transformed into spectral vegetation indices.
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    Öğe
    Lettuce (Lactuca sativa L.) yield prediction under water stress using artificial neural network (ANN) model and vegetation indices
    (Lithuanian Research Centre Agriculture & Forestry, 2012) Kızıl, Ünal; Genç, Levent; İnalpulat, Melis; Sapolyo, Duygu; Mirik, Mustafa
    Water stress is one of the most important growth limiting factors in crop production around the world. Water in plants is required to permit vital processes such as nutrient uptake, photosynthesis, and respiration. There are several methods to evaluate the effect of water stress on plants. A promising and commonly practiced method over the years for stress detection is to use information provided by remote sensing. The adaptation of remote sensing and other non-destructive techniques could allow for early and spatial stress detection in vegetables. Early stress detection is essential to apply management practices and to maximize optimal yield for precision farming. Therefore, this study was conducted to 1) determine the effect of water stress on lettuce (Lactuca sativa L.) grown under different watering regime and 2) explore the performance of the artificial neural network (ANN) technique to estimate the lettuce yield using spectral vegetation indices. Normalized difference vegetation index (NDVI), green NDVI, red NDVI, simple ratio (SR), chlorophyll green (CLg), and chlorophyll red edge (CLr) indices were used. The study was carried out in vitro conditions at three irrigation levels with four replicates and repeated tree times. The different irrigation levels applied to the pots were 33, 66 and 100 % (control) of pot water capacity. Spectral measurements were made by a hand-held spectroradiometer after the irrigation. Decrease in irrigation water resulted in reduction in plant height, plant diameter, number of leaves per plant, and yield. Using all indices in a feed-forward, back-propagated ANNs model provided the best prediction with R2 values of 0.86, 0.75, and 0.92 for 100, 66, and 33 % water treatments, respectively. The overall results indicated that spectral data and ANNs have high potential to predict the lettuce yield exposed to water deficiency.