Precision Farming
R. Azadnia; A. Rajabipour; B. Jamshidi; M. Omid
Abstract
IntroductionApple 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 ...
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IntroductionApple 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.Materials and MethodsThis 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.Results and DiscussionThe 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.ConclusionField 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.
F. Azadshahraki; K. Sharifi; B. Jamshidi; R. Karimzadeh; H. Naderi
Abstract
Early diagnosis of plant diseases before the occurrence of symptoms can reduce the loss of the yield and increase the quality of agricultural crops. It also reduces the consumption of pesticides, environmental risks, and the cost of production. For this reason, the objectives of the present study were ...
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Early diagnosis of plant diseases before the occurrence of symptoms can reduce the loss of the yield and increase the quality of agricultural crops. It also reduces the consumption of pesticides, environmental risks, and the cost of production. For this reason, the objectives of the present study were non-destructive diagnosis of early blight of tomato plant and discrimination of the most important agents of early blight (A. solani and A. alternate) in the primary stages of incidence of the disease before appearing visual symptoms using Vis-NIR spectroscopy (400-900 nm). The spectral data were acquired from the leaves of the plants infected with A. solani and A. alternate, 48 hours, 72 hours, 96 hours, and 120 hours after inoculation. To develop the recognition model based on the spectral data, principal components analysis (PCA) coupled with artificial neural network (ANN) was used. The results showed that the PCA-ANN model could diagnose the infected plants and pathogen species with accuracy of 93-100% for test set samples. In 96 hours after inoculation, in addition to the simpler model (8 PCs and 3 neurons in hidden layer), accuracy of 100% was obtained. At all times after inoculation, there was no error in diagnosis of the plants infected with A. solani that is more pathogenic and aggressive than other species, from healthy plants. Early blight in tomato plant and the type of pathogen before visual symptoms, without any plant sample preparation, could be diagnosed non-destructively (with accuracy of 93-100%) using Vis-NIR (400-900 nm) spectroscopy coupled with PCA-ANN. It was concluded that this technology could be used for rapid, low-cost, and early diagnosis of this disease in tomato plant instead of time-consuming, expensive, and destructive laboratory methods.
B. Jamshidi; A. Arefi; S. Minaei
Abstract
Introduction In recent years, the determination of firmness as an important quality attribute of apple fruits has been widely noticed. Common methods for firmness measurement are destructive and cannot be applied in sorting lines. Therefore, development of a non-destructive, simple, fast, and the low-cost ...
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Introduction In recent years, the determination of firmness as an important quality attribute of apple fruits has been widely noticed. Common methods for firmness measurement are destructive and cannot be applied in sorting lines. Therefore, development of a non-destructive, simple, fast, and the low-cost determination technique of firmness is imperative. Dynamic speckle patterns (DSP) or bio speckle imaging as a new optical technique has been recently noticed for non-destructive quality assessment of food and agricultural products. In this research, the feasibility of using this technique was investigated for non-destructive prediction of firmness in intact apples during five months of cold storage. Materials and Methods During the harvest season, in 2013, a total of 540 ‘Red Delicious’ apples were obtained from a local orchard in Oshnaviyeh, Iran. The apples with similar color and shape were collected from several trees in the same place. The samples were stored under cold conditions for five months. Five experiments were carried out; the first experiment was done immediately after harvesting and other tests were performed during storage time, i.e. 30, 60, 120, and 150 days after harvesting date. In each experiment, the samples were illuminated by two laser diodes at the wavelengths of 680 nm and 780 nm, separately. DSP images of each fruit were acquired using a CCD camera. Then, time history of the speckle pattern (THSP) was created for each sample. After taking images, reference measurements were carried out for each sample to determine its firmness. Quantification of DSP activity was done using the statistical features of inertia moment (IM) and the absolute value of differences (AVD) extracted from the THSP images. Moreover, features of the images were extracted based on texture and wavelet transform. Finally, artificial neural network (ANN) models were developed for prediction of apple firmness based on image’s information obtained from the wavelengths of 680 nm and 780 nm, and the reference measurements. The 60, 15, and 25 percent of total samples were randomly used for calibration, cross-validation, and test validation sets, respectively. The correlation coefficient between measured and predicted values of the firmness and also the standard error of prediction (SEP) were calculated to compare the performance of the different ANN models. Results and Discussion After one month of the storage, apples lost about 15 percent of their initial firmness.The softening process continued and the firmness index dropped to 48.05 N (a total decrease of 42%). A significant difference was observed among the mean values of the firmness belong to the different storage times. In first and second months of the storage, a negative linear relationship was observed between DSP activity and the firmness. The lowest value of IM was observed for apples belonged to the harvesting date. DSP activity suddenly increased after 30 days of the storage. This ascending trend continued and reached to its maximum value on the 60th days of the storage. It was noted that DSP activity is significantly affected by the chlorophyll absorption during this period. Moreover, DSP activity at the wavelength of 680 nm was more than that at 780 nm. After two months of the storage, a significant decrease in DSP activity was observed for both wavelengths of 680 nm and 780 nm. The main reason for this phenomenon came back to changes in carbohydrates. During this ripping period, starch, which plays a main role in backscattering phenomenon is converted into simpler carbohydrates and it causes an increase in soluble solid contents and a decrease in the number of scattering centers. After developing the ANN models, the correlation coefficient of the prediction (rp) for different topologies was ranged from 0.74-0.81 and 0.81-0.83 for the wavelengths of 680 nm and 780 nm, respectively. Moreover, standard error of prediction (SEP) was between 8.4-9 N and 8.1-8.7 N for the wavelengths of 680 nm and 780 nm, respectively. The achieved results may be more attractive when they are compared with obtained results using multispectral/hyperspectral scattering imaging, as expensive and rather complicate techniques for non-destructive firmness assessment in apple fruits. Conclusion It was concluded that dynamic speckle patterns (DSP) or bio speckle imaging could be a simple, low-cost and appropriate technique for non-destructive prediction of firmness in intact apples during storage.
B. Jamshidi; S. Minaei; E. Mohajerani; H. Ghassemian
Abstract
In recent years, application of near-infrared spectroscopy (NIR) as a non-destructive technique combined with chemometric methods has been widely noticed for quality assessment of food and agricultural products. In chemometric methods, quality analyses are important issues which could be related to pattern ...
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In recent years, application of near-infrared spectroscopy (NIR) as a non-destructive technique combined with chemometric methods has been widely noticed for quality assessment of food and agricultural products. In chemometric methods, quality analyses are important issues which could be related to pattern recognition. In this research, the feasibility of pattern recognition methods combined with reflectance NIR spectroscopy for non-destructive discrimination of oranges based on their tastes was investigated. To this end, both unsupervised and supervised pattern recognition techniques, hierarchical cluster analysis (HCA) and soft independent modeling of class analogies (SIMCA) were used for assessing the feasibility of variety discrimination and classification (according to their taste), respectively, based on the spectral information of 930-1650nm range. Qualitative analyses indicated that NIR spectra of orange varieties were correctly clustered using unsupervised pattern recognition of HCA. It was also concluded that supervised pattern recognition of SIMCA for NIR spectra of oranges provided excellent results of variety classification based on BrimA index at 5% significance level (classification accuracy of 98.57%). Moreover, wavelengths of 1047.5nm, 1502nm, and 1475nm contributed more than other wavelengths in discriminating two classes. Samples having the same BrimA index were also correctly classified with the high classification accuracy (95.45%) at 5% significance level. The discrimination power of wavelengths of 1475nm, 1583nm, and 1436.75nm were more than those for other wavelengths to achieve this classification. Therefore, reflectance NIR spectroscopy combined with pattern recognition methods can be utilized for determination of other attributes related to taste.
