Precision Farming
M. Naderi-Boldaji; M. Tohidi; M. Ghasemi-Varnamkhasti
Abstract
IntroductionThe development of portable devices for real-time quality assessment of sugarcane is an essential necessity in the agricultural and industrial technology of sugarcane production and processing. Attributes of sugarcane such as sugar concentration and water content can be utilized for this ...
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IntroductionThe development of portable devices for real-time quality assessment of sugarcane is an essential necessity in the agricultural and industrial technology of sugarcane production and processing. Attributes of sugarcane such as sugar concentration and water content can be utilized for this purpose. Near infrared (NIR) spectroscopy has been one of the most widely applied techniques for quality evaluation of sugarcane. However, NIR spectrophotometers in the full NIR wavelength range (up to 2500 nm) are expensive devices that are not readily available for portable applications. Short-wave NIR devices in the range of 1100 nm are available at lower costs but need to be evaluated for specific applications. On the other hand, dielectric spectroscopy has attracted the attention of researchers for quality evaluation of agricultural and food products. In a previous study, a parallel-plate capacitance sensor was developed and evaluated for non-destructive measurement of sugarcane Brix (total soluble solids) and Pol (sucrose concentration) as well as water content, in the frequency range of 0-10 MHz. The results showed excellent prediction models with root mean square errors smaller than 0.3 for Brix and Pol. This study aimed to develop and evaluate a dielectric sensor that can be extended for portable measurements on standing sugarcane stalk in comparison with short-wave NIR (SWNIR) spectroscopy to address how the fusion of the two methods may improve the accuracy of models for predicting sugarcane Brix.Materials and MethodsA dielectric sensor in the form of a gadget was developed with metallic electrodes to encompass the sugarcane stalk samples. The dielectric sensor was excited with a sinusoidal voltage within 0-150 MHz frequency range by a function generator, and the conductive power through the electrodes was measured using a spectrum analyzer. 105 sugarcane stalk samples were prepared from seven sugarcane varieties and scanned with the dielectric sensor. The samples were also subjected to Vis-SWNIR radiation in the wavelength range of 400-1100 nm, and the reflectance spectra were captured. Reference Brix and water content of the samples were determined using a portable refractometer and oven-drying method, respectively. Regression analyses and artificial neural networks were performed on independent and combined data from dielectric and Vis-SWNIR spectroscopy to develop prediction models for Brix and water content.Results and DiscussionPartial least squares regression on independent data sets of each instrument resulted in RMSEP = 1.14 and RMSEP = 1.88 for Brix using Vis-SWNIR and dielectric spectroscopy, respectively. Moreover, data fusion of dielectric and Vis-SWNIR spectroscopy at a low level for the prediction of Brix significantly improved the prediction accuracy to R2P = 0.94 and RMSEP= 0.74. The medium-level data fusion resulted in R2P = 0.89 and RMSEP = 0.93 for prediction of water content.ConclusionIn this study, the accuracy of using Vis-SWNIR and dielectric spectroscopy data for predicting Brix and water content in sugarcane stalk samples was evaluated. To develop the prediction models, partial least squares (PLS) regression and artificial neural network (ANN) were compared. First, the prediction models were developed based on Vis-SWNIR and dielectric spectroscopy independently. Then, the two techniques were fused and the improvement in the prediction accuracy was investigated. Fusing the two methods at an intermediate level lowered the RMSE of Brix to 0.74, showing noticeable improvement compared to previous studies. Based on the achieved results, developing a fusion probe for SWNIR and dielectric spectroscopy and designing the measuring system could be the aim of future studies for in-situ evaluation of sugarcane quality parameters. Due to the importance of sugarcane quality evaluation, during growth and maturity, the results of this study can have a significant role in the development of a portable device that combines NIR and dielectric spectroscopy methods for fast and non-destructive evaluation of sugarcane quality parameters.AcknowledgementThis article was extracted from a research project financially supported by the Research deputy of Shahrekord University. The grant number was 0GRD34M1614. The authors would like to appreciate the support of the Amir-Kabir Sugarcane Agro-Industry Co., Khuzestan, Iran for providing the sugarcane stalk samples.
The relationship between machine and soil
M. Naderi-Boldaji; H. Azimi-Nejadian; M. Bahrami
Abstract
Machinery traffic is associated with the application of stress onto the soil surface and is the main reason for agricultural soil compaction. Currently, probes are used for studying the stress propagation in soil and measuring soil stress. However, because of the physical presence of a probe, the measured ...
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Machinery traffic is associated with the application of stress onto the soil surface and is the main reason for agricultural soil compaction. Currently, probes are used for studying the stress propagation in soil and measuring soil stress. However, because of the physical presence of a probe, the measured stress may differ from the actual stress, i.e. the stress induced in the soil under machinery traffic in the absence of a probe. Hence, we need to model the soil-stress probe interaction to study the difference in stress caused by the probe under varying loading geometries, loading time, depth, and soil properties to find correction factors for probe-measured stress. This study aims to simulate the soil-stress probe interaction under a moving rigid wheel using finite element method (FEM) to investigate the agreement between the simulated with-probe stress and the experimental measurements and to compare the resulting ratio of with/without probe stress with previous studies. The soil was modeled as an elastic-perfectly plastic material whose properties were calibrated with the simulation of cone penetration and wheel sinkage into the soil. The results showed an average 28% overestimation of FEM-simulated probe stress as compared to the experimental stress measured under the wheel loadings of 600 and 1,200 N. The average simulated ratio of with/without probe stress was found to be 1.22 for the two tests which is significantly smaller than that of plate sinkage loading (1.9). The simulation of wheel speed on soil stress showed a minor increase in stress. The stress over-estimation ratio (i.e. the ratio of with/without probe stress) noticeably increased with depth but increased slightly with speed for depths below 0.2 m.
O. Omidi-Arjenaki; D. Ghanbarian; M. Naderi-Boldaji; K. Mollazadeh
Abstract
Introduction The texture of fresh fruit is determined by the structural and mechanical properties of tissue. It depends on climate, maturity, variety and postharvest condition. During ripening, due to loss of turgor, degradation of starch and cell walls, the flesh of apple softens. The relationship between ...
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Introduction The texture of fresh fruit is determined by the structural and mechanical properties of tissue. It depends on climate, maturity, variety and postharvest condition. During ripening, due to loss of turgor, degradation of starch and cell walls, the flesh of apple softens. The relationship between fruit quality and its physiological changes has been widely investigated. Using techniques according to the principles of force-deformation, impact, and vibration tests, texture of fruit and its mechanical properties can be associated, conventionally. In analyzing the vitality of biomaterials; a non-invasive technique based on the optical phenomenon is the Biospeckle method which occurs when the surface of the sample is illuminated by laser light. It seems that because of the fact that the laser light can penetrate tissue, it is possible to obtain information about the texture and cell condition from tissue under the skin. This means that, there would be a chance to detect and monitor the variation of cells and try to make a model to predict mechanical properties. Therefore, the overall objective of this study was to develop prediction models based on biospeckle imaging to predict mechanical properties of ripe Golden Delicious apples. Materials and Methods The 400 fresh and intact 'Golden Delicious' apples were harvested and were prepared for mechanical tests and biospeckle imaging. Biospeckle imaging was carried out first, followed by compression and creep test and then penetration test. During imaging, to avoid environmental reflections, the process was carried out in a dark and closed chamber. Biospeckle activity was saved as a video (AVI format) in a computer for analyzing. The THSP method was used to analyze biospeckle activity in samples. The indices which have been used for analyzing biospeckle images are divided into 3 statistical features and 4 textural features. Apples were cut in half. One of the halves was used for cylindrical sample extraction for uniaxial compression and creep tests and another was used for penetration test. From compression tests the tangent modulus of elasticity, stress and strain of bio-yield and failure energy for toughness calculation were determined. The creep behavior was obtained by fitting the Burger's model to the experimental data. In penetration test, a stainless steel probe with a hemispherical tip was used for peeled and unpeeled samples. For each sample maximum penetration force and energy were obtained. Prediction of mechanical property was carried out using adaptive neuro-fuzzy inference system (ANFIS). To reduce the dimension of the input vector the PCA was used. Four significant adjustments were made in the structure of ANFIS in order to find the best models. The models were evaluated using RMSECV, RMSEP, MBEC, MBEP, RC, and RP. Results and Discussion Models for modulus of elasticity prediction have Rp=0.821, 0778, 0.791, 0.880, and 0.843 for 4 compression rate and secant modulus, respectively. Clearly, the results from this research are encouraging, indicating the potential of using speckle imaging system for predicting apple fruit mechanical properties. Comparing to the all texture analysis techniques, Wavelet and GLRLM provided good results for most properties leading to select them as the best techniques for analysis of biospeckle images because of their consistency in prediction performance. Prediction model for break strain has the highest Rp (Rp=0.920) followed by the retarded time (Rp=0.890), retarded viscosity (Rp=0.886) and maximum penetration force in unpeeled case (Rp=0.883). A lower correlation (Rp = 0.728) was observed for initial viscosity. Conclusion The described optical method based on biospeckle represents an innovative and reliable method for rapid and non-invasive detection of mechanical properties. The results of the evaluation showed that, as time passes, fresh apples due to the loss of water in both the elasticity and the biospeckle activity were dropped. Biospeckle imaging can accurately predict mechanical properties. The average accuracy of best prediction of mechanical properties models was R2=0.899. The present results can provide the basis of future development of in-line quality monitoring during apple quality control.
