A condenser was designed and developed for distillation of herbaceous oils. The performance of the condenser was evaluated by using two common refrigerants namely R12 and R134a to separate the water and essential oil molecules of spearmint (Mentha spicata) leaves from a vapor-air mixture flow. Three levels of air speed (1, 2 and 3 m/s) and temperature (180, 210 and 250 °C) were selected for the vapor-air mixture flow during the experiments. For this condenser, a better performance was observed for the R12 than the R134a refrigerant. The best performance of the condenser was obtained at lower air speeds and lower temperatures for the essential oil of spearmint leaves.

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The phosphine flow during fumigation of wheat in a Chinese horizontal warehouse is studied by computational fluid dynamics modeling. The Renormalization Group k-ε turbulence model is adopted to simulate the three-dimensional airflow field and the species transport model is used to calculate the species mixing. The phosphine concentrations in the warehouse with different leaky area positions during the fan-forced fumigation and the tablet fumigation are continuously predicted for 28.5 days. Also, the effects of the fumigation methods and the leaky area positions on the phosphine concentrations are discussed. The results reveal that the phosphine concentration in the tablet fumigation takes longer time to be uniform than that in the fan-forced fumigation. The maximum value of the phosphine concentration in the fan-forced fumigation is larger and appears earlier than that in the tablet fumigation. We also found that the fumigation methods have greater impact on the phosphine concentration than the leaky area positions in this study. And the impacts of the leaky area positions on the phosphine concentration could be further influenced by the fumigation methods.

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Hatching window and the gender of hatching eggs were investigated in relation to the yolk content and yolk ratio (yolk to egg ratio) of the eggs. To quantify the amount of yolk noninvasively, a regression model from absorbance spectra (600–900 nm) was developed using known yolk content. This model was then used to determine the yolk content subsequently the yolk to albumen ratio of the hatching eggs. This yolk information of hatching egg was then correlated with hatching time and the gender of the egg. The results showed a significant correlation between estimated yolk-albumen ratio and hatching time, but no significant correlations were obtained with the gender of the eggs.

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To control insect pests in the stored grains effectively, radio frequency (RF) treatment is one of the novel alternative methods over the existing methods, such as pesticides, fumigations, and heat and cold treatments. The RF selective heating of red flour beetle, Tribolium castaneum (Herbst) in two different volumes of bulk canola seeds at 5%, 7%, 9%, and 11% moisture contents (MC) using a 1.5 kW, 27.12 MHz RF heating unit was simulated using a finite element method based commercial simulation package (COMSOL Multi-physics). The electric field formation, dielectric heat generation, non-isothermal fluid flow, and heat transfer including surface to surface radiation were coupled. The simulated and the measured temperatures of the seeds were compared with the different MCs and volumes of the seeds. Similar comparison was also conducted for the insects and the seeds. The differences between the simulated and the measured temperatures of the seeds were not more than 17.4%, 13.4%, 8.08%, and 19.0% for the small volume seeds and 14.8%, 15.6%, 19.5%, and 18.8% for the large volume seeds at 5%, 7%, 9%, and 11% MCs, respectively. Non-uniform RF heating of the seeds was observed regardless of the MC and the volume of the seeds. The RF selective heating of the insects was most effective for the small volume seeds at 11% MC. The temperature of the insects was 14.6 °C (maximum) higher than the temperature of the seeds. The RF selective heating of the T. castaneum in the canola seeds could be improved with a proper design of RF applicator that minimizes the non-uniformity of heating.

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Smart farming is seen to be the future of agriculture as it produces higher quality of crops by making farms more intelligent in sensing its controlling parameters. Analyzing massive amount of data can be done by accessing and connecting various devices with the help of Internet of Things (IoT). However, it is not enough to have an Internet support and self-updating readings from the sensors but also to have a self-sustainable agricultural production with the use of data analytics for the data to become useful. In this work, we designed and implemented a smart hydroponics system that automates the growing process of the crops using Bayesian Network model. Sensors and actuators are installed to monitor and control the parameters of the farm such as light intensity, pH, electrical conductivity, water temperature, and relative humidity. The sensor values gathered are used in the building the Bayesian Network, which classifies and predicts the optimum value in each actuator to autonomously control the hydroponics farm. Results show that the fluctuations in terms of the sensor values were minimized in the automatic control using BN as compared to the manual control. The prediction model obtained 84.53% accuracy after model validation and the yielded crops on the automatic control was 66.67% higher than the manual control.

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In this study, a field scene recognition system was developed to estimate a self-position of a traveling vehicle along a farm road by using an original capture system with three cameras, a vector quantization method to express the features of field scenes, a machine learning based scene recognition algorithm, and a vehicle position estimation algorithm with an original voting method. The potential of our system was demonstrated through five experiments performed over four months. In the experiments, the system could robustly estimate the vehicle position with the accuracy less than 1 m at the processing speed of approximately 2.0 Hz when the vehicle was driven straight along a traveling line on the targeted two types of roads: a surfaced road and an unsurfaced road, at the driving speed of 0.5 m/s. The results demonstrated an applicability of our system to navigate an autonomous agricultural robot vehicle without using GNSS.

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The suitable operating parameters of fluted roller metering mechanism need to be selected to address variability of application of inputs in a variable rate applicator. At present, the selection of operating parameters depends mainly on empirical rules and experimental trials. This paper presents the results of development and evaluation of multiple linear regression (MLR) and artificial neural network (ANN) models for predicting operating parameters of fluted roller metering mechanism of a variable rate applicator. The MLR and ANN models were developed to predict operating parameters viz. application rate, particle damage and particle distribution per unit area based on the data collected from experimental trials conducted under laboratory condition using fluted roller metering mechanism. The MLR models simulated the fluted roller exposed length with coefficient of determination (R²) values of 072, 0.65, 0.74 for urea, SSP and MOP fertilizers, respectively during training and 0.62, 0.54 and 0.59 for urea, SSP and MOP fertilizers, respectively during testing. The ANN model was optimized for 3–1–4 configuration with Levenberg–Marquardt (LM) algorithm, which indicated good performance during testing with the coefficient of determination (R²) of 0.60–0.84, 0.71–0.91, and 0.59–0.87 for granular SSP, urea and MOP fertilizer, respectively. The Nash–Sutcliffe coefficient (E) for ANN training data set ranged 0.66–0.85, 0.71–0.92 and 0.61–0.85 for granular SSP, urea and MOP fertilizer, respectively. It was concluded that the ANN model predicted the operating parameters of the variable rate applicator better than MLR model with r² value close 1.

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Among the different cases of emerging food fraud during the post-harvest processing, the adulteration in grape syrup is one. Typically, the grape syrup is adulterated with some illegitimate foreign materials such as grape paste (sauce), date syrup and even adding sugar-water solution to the pure grape syrup. The present study deals with assessing an electronic nose (e-nose) consisting of eight different metal oxide semiconductor (MOS) sensors for prompt detection of adulteration in the grape syrup. Three different adulterants i.e. grape paste, date syrup and sugar-water solution, each at three levels of 50, 60 and 75%, were tested. The collected data from MOS were normalised and visualised with the help of standard normal variate (SNV) and principal component analysis (PCA), respectively. Moreover, the scores obtained from PCA were used to perform hierarchal cluster analysis (HCA) to identify the similarities between different adulterated mixtures and pure grape syrup. Three different classification cases were considered to (i) address the presence of adulteration, (ii) detect the different adulterants and (iii) classify the amount of each adulteration. Linear discriminant analysis (LDA) and multi-class support vector machine (SVM) were used for classification analyses. Results showed that PCA identified provided separate clusters for the MOS data corresponding to different adulterants and their levels. The HCA showed a hierarchal of similarities between pure grape syrup and different levels of adulterations. LDA and SVM resulted in a successful classification modelling. However, the performance of SVM was considerably better than LDA with classification accuracies of 98.6 ± 0.10%, 98.9 ± 1.16% and 95.1 ± 1.39% for detecting adulteration, different adulterants and different concentrations of adulterants, respectively. MOS sensors coupled with chemometrics could provide a useful instrument and fast procedure for detection of adulteration in grape syrup.

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In the present investigation, aromatic compound of aniline acquired from textile industry waste was degraded by isolated bacterial species from the source of contaminant by using acclimatization and enrichment techniques. The isolated bacterial species were further identified as Bacillus firmus BA01 by 16S rRNA sequencing. The growth kinetics of bacteria was validated over the entire concentration range by Haldane's growth kinetics model. Further, the specific growth rate and the decay coefficients have been found to be 0.024 h⁻¹ and 0.006 h⁻¹ respectively. The salt tolerance of potent degrading strain was analyzed through dose response assessment showing IC50 value of 1.7%. The specific turnover rate was obtained to be 0.047 μM h⁻¹ mg of protein⁻¹. The intermediates of aniline were identified by GC/MS analysis before and after treatment.

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In this study, a geometric model of Coffea arabica var. Colombia fruits is reviewed to represent computationally the fruit topology in any development stage. A finite element analysis was carried out to estimate Young's moduli for different ripeness stages. Analytical equations were adjusted to experimental data of size, volume and Young's moduli (semi-experimental) with the aim to analyze its behavior in the ripening process. Further, a volume equation is also developed in dependence on the orthogonal variations of the fruit. These variations show that the fruit keeps a growth constant ratio. To simulate the fruit growth, a finite element analysis was performed on an unripe fruit with displacement boundary conditions that enforce the geometric changes. To verify that the volume changes were simulated correctly in each ripeness stage, a strain index was defined to compare the growth evolution with experimental data. Results show that the simulated fruit growths properly since dimensions and volume were predicted in a reasonable level of approximation. Additionally, the internal pressure was computed for all ripening stages and the results are consistent with the comparisons done from the strain energy of the fruit.

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