Precision Farming
R. Azadnia; A. Rajabipour; B. Jamshidi; M. Omid
Abstract
Introduction
One of the most frequently consumed fruit in all over the world is apple. An apple fruit includes large source of minerals, fiber and several biologically compounds such as vitamin C, special phenolic compounds (natural antioxidant). The amount of nutrients plays a significant role in the ...
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Introduction
One of the most frequently consumed fruit in all over the world is apple. An apple fruit includes large source of minerals, fiber and several biologically compounds such as vitamin C, special phenolic compounds (natural antioxidant). The amount of nutrients plays a significant role in the growth, reproduction and performance of agricultural products and plants. By predicting these elements, chemical inputs can be accurately managed. Thus, timely and accurate monitoring and management of crop nutrition status are crucial for recommended fertilization, yield increase, and quality improvement, whilst by reducing the amount of chemical fertilizers applied, the risk of environmental degradation can be reduced. In crop plants, leaf samples are typically analyzed to diagnose nutrient deficiencies and imbalances, as well as to evaluate the effectiveness of current nutrient management programs. Thus, the main aim of this study was to non-destructively estimate the level of Nitrogen (N), Phosphorus (P) and Potassium (K) elements of apple tree leaves using Visible/Near-infrared (Vis/NIR) spectroscopy at wavelength range of 500 to 1000 nm coupled with chemometrics analysis.
Materials and Methods
This research investigated the potential of the Vis/NIR spectroscopy system with chemometrics analysis for predicting NPK nutrients of apple trees. To do so, 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) 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 apple tree leaf samples was done in intractance mode. Furthermore, ten reflectance spectra were captured randomly on each apple tree leaf at different points. The averaged spectrum was used to determine the reflectance (R). The data was then transformed into absorbance (log 1/R) for chemometrics analysis. The NPK contents were measured using reference methods following spectroscopy measurements. Then Partial Least Square (PLS) multivariate calibration models were developed based on reference measurements and spectral information with different pre-processing techniques. In order to remove the unwanted effects, various pre-processing methods were used to obtain an accurate calibration model. To evaluate the proposed models, Root Mean Square Error of calibration and prediction sets (RMSEC and RMSEP), as well as correlation coefficient of calibration and prediction sets (rc and rp), and Residual Predictive Deviation (RPD) were calculated.
Results and Discussion
The statistical metrics were calculated for evaluation of PLSR model. The results indicated that the PLSR model could efficiently predicted the NPK contents with a satisfactory accuracy. The best developed model based on the standard normal variation pre-processing method in combination with the second derivative (SNV+D2) with the values of rc= 0.9859, RMSEC=0.028%, rp=0.978, RMSEP=0.034% and RPD of 7.47 was related to nitrogen prediction. The best model for prediction of P content resulted in rc= 0.967, RMSEC=0.0051%, rp=0.958, RMSEP=0.0057% and RPD of 5.96. Also the PLSR model based on MSC+D2 preprocessing method resulted in the 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 prediction of K content. Moreover, the weakest model was related to estimation of P content based on data without pre-processing with rc = 0.774, RMSEC = 0.013%, rp = 0.675, RMSEP = 0.018% and RPD value of 1.87. Based on the obtained results, the proposed PLSR model coupled with preprocessing methods was able to predict the nutrients content with high precision.
Conclusion
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 fields can significantly reduce time wastage and laboratory expenses. Therefore, according to the ability of the Vis/NIR spectroscopy technique and according to the obtained results, this method can be used to implement a field portable system based on Vis/NIR spectroscopy in order to estimate the Nutrient elements needed by apple trees in the orchards and increased the productivity of the orchards.
A. Moghimi; A. Sazgarnia; M. H. Aghkhani
Abstract
IntroductionPistachio production has been adversely affected by Psylla, which is a devastating insect. The primary goal of this study was to select sensitive spectral bands to distinguish pistachio leaves infected by Psylla from healthy leaves. Diagnosis of psylla disease before the onset of visual cues ...
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IntroductionPistachio production has been adversely affected by Psylla, which is a devastating insect. The primary goal of this study was to select sensitive spectral bands to distinguish pistachio leaves infected by Psylla from healthy leaves. Diagnosis of psylla disease before the onset of visual cues is crucial for making decisions about topical garden management. Since it is not possible to diagnose psylla disease even after the onset of symptoms with the help of color images by drones, hyperspectral and multispectral sensors are needed. The main purpose of this study was to extract spectral bands suitable for distinguishing healthy leaves from psylla leaves. For this purpose, in this paper, a new method for selecting sensitive spectral properties from hyperspectral data with the high spectral resolution is presented. The intelligent selection of sensitive bands is a convenient way to build multispectral sensors for a specific application (in this article, the diagnosis of psylla leaves). Knowledge of disease-sensitive wavelengths can also help researchers analyze multispectral and hyperspectral aerial images captured by satellites or drones.Materials and MethodsA total number of 160 healthy and diseased leaves were scanned in 64 spectral bands between 400-1100 nm with 10 nm spectral resolution. A random forest algorithm was used to identify the importance of features in classifying the dataset into diseased and healthy leaves. After computing the importance of the features, a clustering algorithm was developed to cluster the most important features into six clusters such that the center of clusters was 50 nm apart. To transfer the hyperspectral dataset into a multispectral dataset, the reflectance was averaged in spectral bands within ±15 nm of each cluster center and achieved six broad multispectral bands. Afterwards a support vector machine algorithm was utilized to classify the diseased and healthy leaves using both hyperspectral and multispectral datasets.Results and DiscussionThe center of clusters were 468 nm, 598 nm, 710 nm, 791 nm, 858 nm, and 1023 nm, which were calculated by taking the average of all the members assigned to the individual clusters. These are the most informative spectral bands to distinguish the pistachio leaves infected by Psylla from the healthy leaves. The F1-score was 90.91 when the hyperspectral dataset (all bands) was used, while the F1-score was 88.69 for the multispectral dataset. The subtle difference between the F1-scores indicates that the proposed pipeline in this study was able to select appropriately the sensitive bands while retaining all relevant information.ConclusionThe importance of spectral bands in the visible and near-infrared region (between 400 and 1100 nm) was obtained to identify pistachio tree leaves infected with psylla disease. Based on the importance of spectral properties and using a clustering algorithm, six wavelengths were obtained as the best wavelengths for classifying healthy and diseased pistachio leaves. Then, by averaging the wavelengths at a distance of 15 nm from these six centers, the hyperspectral data (64 bands) became multispectral (6 bands). Since the correlation between the wavelengths in the near-infrared region was very high (more than 95%), out of the three selected wavelengths in the near-infrared region (710, 791, and 1023), only the 710-nm wavelength, which was closer to the visible region, was selected. The results of classification of infected and diseased leaves using hyperspectral and multispectral data showed that the degree of classification accuracy decreases by about 2% and if only 4 bands are used, the degree of accuracy decreases by about 3%.The results of this study revealed that the proposed framework could be used for selecting the most informative spectral bands and accordingly develop custom-designed multispectral sensors for disease detection in pistachio. In addition, we could reduce the dimensionality of the hyperspectral datasets and avoid the issues related to the curse of dimensionalitylity.
