b. jamshidi

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

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.

In this study, the ability of the near infrared interactance (NIR) spectrometry for nondestructive assessment and prediction of the sugar content of the roots of sugar beet were studied. Additionally, the effect of spectral pre-processing methods on the accuracy of multivariate predictor models was assessed. In this regard, the spectrometry of sugar beet samples was performed in the interactance measurement mode within the spectral range of 350-2500 nm using a contact probe. For this purpose, at first skin spectroscopy was performed in 3 desired areas and then the samples were cut vertically and finally spectroscopy was performed in these three areas. The results show that spectroscopy could be used to measure the amount of sugar in beets. Regions of the main absorbance peaks on NIR spectra of the intact and peeled sugar beets were the same. Therefore, the effect of chemical compounds of sugar beet skin can be ignored to identify internal compounds with nondestructive NIR spectroscopy. Additionally, sugar content of the product can be measured without the need to cut it. Prediction of the sugar content of intact samples with the PLS model based on SG + D2, is the the most accurate prediction method. Thus, SG+D2 preprocessing (rc =0.87, RMSEC = 0.90, rp = 0.95, RMSEP = 0.55) is suitable for predicting the amount of SC with the highes accuracy rate (SDR= 3.20). Finally, a system was designed based on the developed model with the ability to measure the grade of the product in a nondestructive way. This system can be used to process the facilities of sugar beet as well as research on breeding of sugar beet in breeding Institutes.

The process of guarantee of quality and evaluation of advancement of programs or effectiveness evaluation of progresses based on pre-set indicators is called ‘accreditation’. The accreditation program was run by Agricultural Engineering Research Institute (AERI) as one of the twenty research institutes affiliated to Agricultural Research, Education and Extension Organization (AREEO) of Ministry of Agriculture-Jahad (MAJ) in Iran. This institute is involved in four fields of ‘Irrigation and Drainage’, ‘Food Engineering and Post-harvest’, ‘Agricultural Machinery and Mechanization’ and ‘Greenhouse’. Thus, five working groups including ‘Missions and Objectives’, ‘Human Resources’, ‘Research’, ‘Education and Extension’ and ‘Budget and Credit’ were established and the tables of objectives and indicators, evaluation criteria, measurement indices, and achievement criteria were determined; then, six specialized questionnaires relevant to each working group (one questionnaire for each factor and two questionnaires for ‘Education and Extension’ factor) at the national level for 2014-15 activity years of the institute were given to the respondents and institute. The collected data were analyzed based on the Likert scale. The results indicated that the situation of the AERI status was satisfactory in the ‘Human Resources’, ‘Research’ and ‘Education and Extension’ aspects and unsatisfactory in ‘Missions and Objectives’ and ‘Budget and Credits’ aspects; in addition, the overall assessment of the institute was relatively satisfactory. Therefore, by benefiting more from the intellectual, skilled and collaborative efforts of AERI's faculty members as well as the attention of higher authorities, it is likely to strengthen the institute's strengths and eliminate its weaknesses to achieve a higher quality level. This study may serve as a good model for the other research institutes.

To determine the mechanical behavior (creep) of potato under constant load, two potato cultivars were stored in two different conditions and the effects of storage time and temperature were investigated on creep behavior of the samples. Creep parameters included instantaneous elastic coefficient, delay elastic coefficient, initial viscoelastic coefficient, delayed viscoelastic coefficient and delay time. Two samples showed different mechanical properties, However, in both samples, there was a significant decrease (P <0.05) in all creep parameters except retardation time with increasing storage time. The effect of storage temperature on these parameters was also checked that results showed the reduction of these parameters in the non-technical storage especially in the Clone variety compared to the technical storage. A mathematical model showed as well the creep behavior of stored potatoes. Regression equations showed good results (R2 = 0.73-0.85) for the effect of storage time and temperature on creep parameters. The results of these equations showed that the Instantaneous modulus of elasticity parameter had the most effect on the creep parameters. According to these results, it can be said that in technical storage due to low temperature and controlled moisture than non-technical storage, there are better conditions for tuber durability than non-technical warehouse. The length of storage period of ‘Agria’ genotype is higher due to lower loss of moisture content and starch content and no sprouting compared to ‘Clone’ genotype. However, the tissue of the ‘Clone’ genotype is softened in comparison to the fresh sample less than two months in non-technical storage and less than three months in the technical storage due to the loss of moisture and the reduction of starch content that directly affected on tissue.

Storage of potato is essential for the continuous supply of raw materials for domestic and industrial use.Dueto structural changes (tissue) and followed by changes in the mechanical properties of the tubers during storage and its impact on product quality, the information of the mechanical properties of potato tubers reduces losses and economic lossespartly along with other factors.In this research two potato genotype (Agria and Clone397009-8) were stored in two technical and non-technical storage for three monthsin Ardebil.The uniaxial compression test was used to measure the effect of variety,storage temperature and storage duration on elastic modulus, failure force and failure energy, and penetration test to measure hardness and penetration energy.Measurement was done weekly and the final results were reported after the storage period. The results showed thatcultivar, storage temperature and storage duration have a significant effect on all mechanical properties in both cultivars.With increase storage time, the elastic modulus, failure force, failure energy, hardness and penetration energy in technicalstorage were lower than non-technical storage,which this difference was statistically significant (P <0.05). The results showed that cultivar Agria has a higher elastic coefficient and rigidity than Clone genotype because of rigid texture and asa result, the storage period is longer.

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.

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.

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.

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.

Near-infrared spectroscopy (NIRS) combined with chemometric methods of spectral pre-processing and multivariate modeling is popular non-destructive method for measuring quality attributes of fruits and vegetables. The present study investigated the feasibility of reflectance NIRS in a spectral range of 930 1650 nm for non-destructive prediction of soluble solids content (SSC) and titratable acidity (TA) in Thomson oranges. The effect of spectral pre-processing methods on the accuracy of multivariate predictor models was also assessed. Partial least squares (PLS) multivariate calibration models were developed using the reference measurements and pre-processed spectra. The following methods were used: smoothing (moving average (MA), Savitzky-Golay (SG), wavelet transform (WT)); normalizing (multiplicative scatter correction (MSC), standard normal variate (SNV)); and increasing the spectral resolution (the first and second derivatives (D1, D2)) to predict the SSC and TA of oranges. The results indicate that reflectance NIRS plus chemometrics gives the potential for non-destructive prediction of SSC and TA in oranges. Preprocessing methods directly affected the results of the PLS models. The best prediction results for SSC (RMSEC = 0.430, rc = 0.923, RMSEP = 0.451, rp = 0.936, SDR = 2.798) and TA (RMSEC = 0.133, rc = 0.883, RMSEP = 0.177, rp = 0.863, SDR = 1.853) were achieved based on a combination of the MA + SNV pre-processing methods.