جستجوی مقالات مرتبط با کلیدواژه « مرئی » در نشریات گروه « کشاورزی »

Apple is one of the most frequently consumed fruits in the world. It is a source of minerals, fiber, various biological compounds such as vitamin C, and phenolic compounds (natural antioxidants). The amount of nutrients plays a significant role in the growth, reproduction, and performance of agricultural products and plants. Chemical inputs can be accurately managed by predicting these elements. Thus, timely and accurate monitoring and managing the status of crop nutrition is crucial for adjusting fertilization, increasing the yield, and improving the quality. This approach minimizes the application of chemical fertilizers and reduces the risk of environmental degradation. In crop plants, leaf samples are typically analyzed to diagnose nutrient deficiencies and imbalances, as well as to evaluate the effectiveness of the current nutrient management system. Therefore, the main aim of this study is to estimate the level of Nitrogen (N), Phosphorus (P), and Potassium (K) elements in the leaves of the apple tree using the non-destructive method of Visible/Near-infrared (Vis/NIR) spectroscopy at the wavelength range of 500 to 1000 nm coupled with chemometrics analysis.

This research investigated the potential of the Vis/NIR spectroscopy coupled with chemometrics analysis for predicting NPK nutrient levels of apple trees. In this study, 80 leaf samples of apple trees were randomly picked and transferred to the laboratory for spectral measurement. The Green-Wave spectrometer (StellarNet Inc, Florida, USA) was utilized to collect the spectral data. In the next step, the spectral data were transferred to the laptop using the Spectra Wiz software (StellarNet Inc, Florida, USA). For this purpose, spectroscopy of the leaf samples was done in interactance mode. Ten random points were selected on each leaf to capture reflectance spectra and the averaged spectrum was used to determine the reflectance (R). The data was then transformed into absorbance (log 1/R) for chemometrics analysis. Following the spectroscopy measurements, the NPK contents were measured using reference methods. Afterward, Partial Least Square (PLS) multivariate calibration models were developed based on the reference measurements and spectral information using different pre-processing techniques. To remove the unwanted effects, various pre-processing methods were utilized to obtain an accurate calibration model. To evaluate the proposed models, the Root Mean Square Error of calibration and prediction sets (RMSEC and RMSEP), as well as the correlation coefficient of calibration and prediction sets (rc and rp), and Residual Predictive Deviation (RPD) were calculated.

The statistical metrics were calculated for the evaluation of PLS models and the results indicated that the PLS models could efficiently predict the NPK contents with satisfactory accuracy. The model with the best performance for nitrogen prediction was based on the standard normal variate pre-processing method in combination with the second derivative (SNV+D2) and resulted in rc= 0.988, RMSEC=0.028%, rp=0.978, RMSEP=0.034%, and RPD of 7.47. The best model for P content prediction resulted in rc= 0.967, RMSEC=0.0051%, rp=0.958, RMSEP=0.0057%, and RPD of 5.96. Additionally, the PLS model based on MSC+D2 pre-processing method resulted in rc= 0.984, RMSEC=0.017%, rp=0.976, RMSEP=0.021%, and RPD of 7.10, indicating the high potential of PLSR model in predicting K content. Moreover, the weakest performing model was related to the estimation of P content without pre-processing with rc = 0.774, RMSEC = 0.013%, rp = 0.685, RMSEP = 0.018%, and RPD value of 1.87. Based on the obtained results, the proposed PLS models coupled with suitable pre-processing methods were able to predict the nutrient content with high precision.

Field spectroscopy has recently gained popularity due to its portability, ease of use, and low cost. Consequently, the use of a portable system for estimating nutrient levels in the field can significantly save time and lower laboratory expenses. Therefore, due to the accuracy of the Vis/NIR spectroscopy technique and according to the obtained results, this method can be used to actualize a portable system based on Vis/NIR spectroscopy to estimate the nutrient elements needed by the apple trees in the orchards and to increase the productivity of the orchards.
Keywords

In situ solvent formation microextraction (ISFME) as a green¡ simple and sensitive method has been proposed for preconcentration of trace quantities of hazardous brilliant green in aqueous samples containing very high salt concentrations by UV-Vis spectrophotometry. In this technique¡ the extraction phase is simultaneously formed in situ. A water-miscible ionic liquid (IL) ([Hmim][BF4])¡ is first added to the sample¡ followed by addition of sodium hexafluorophosphate (NaPF6¡ as an ion-pairing agent) to obtain the hydrophobic IL ([Hmim][PF6]). As a result¡ a cloudy solution is formed due to the formation of hydrophobic IL ([Hmim][PF6]). All the critical parameters affecting the analytical performance of the method were investigated. Under the optimized conditions¡ the enhancement factor was 70. The detection limit and precision (RSD) were 0.1 µg L−1 and 3.3% (n=6) respectively. The applicability of the proposed method was evaluated by determination of trace amounts of brilliant green in various water samples for fish farming.

Vis-NIR spectroscopy has been introduced as a non-destructive, fast, and cheap technique, with minimal sample preparation and no loss or damage to the environment. No investigation has yet been carried out to examine the ability of this method to estimate soil properties in Iran. The objective of this research was to investigate the capability of Vis-NIR spectroscopy to predict the amount of organic matter, carbonate and gypsum in surface soils of Isfahan province. A total of 248 surface soil samples were collected from the study area. Soil organic matter content, gypsum and carbonates percentages were measured by standard laboratory methods. Soil spectral analyses were performed by a field spectrometer using 350-2500 nm wavelength range. Different pre-processing methods were evaluated after recording the spectra. Partial least squares regression was used to predict soil parameters. R2 values for organic matter, carbonates and gypsum were 0.61, 0.45 and 0.8, respectively. Based on RPD (Ratio of Prediction to Deviation) values, the precision of prediction model for gypsum was quite good, and acceptable for organic matter, whereas the prediction of the model for soil carbonates was poor. Consequently, vis-NIR spectroscopy is capable of predicting some soil properties simultaneously and the model accuracy is acceptable.