In recent years, several studies have indicated that modeling techniques based on artificial intelligence can be used for efficient prediction of food industry-related variables. In this study, machine learning methods were used to predict the permeate flux of pomegranate juice in a membrane clarification system based on membrane material, pore size, pressure, flow rate, and processing time. The experimental data were modeled using curve fitting, fuzzy inference system (FIS), artificial neural networks (ANN), and adaptive neuro-fuzzy inference system (ANFIS). Results showed that the permeate flux is a function of time and a power equation can predict the permeate flux with MSE of 0.0136. FIS, ANN and ANFIS models resulted in MSEs equal to 0.0495, 0.0145, and 0.0045 for permeate flux prediction, respectively. According to these findings, ANFIS has resulted in more reliable performance which can be used as an acceptable model in the prediction of permeate flux. The optimum architecture for the ANN was obtained 5-22-1 whilst the architecture of ANFIS models for PVDF and MCE membranes were 3-7-12-12-1 and 4-9-24-24-1, respectively. The results of this study can be used to predict the amount of permeate flux in the absence of experimental data and/or for interpolation and extrapolation of the permeate flux.

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This paper utilizes convolutional neural network (CNN) to identify peach leaf disease infected by Xanthomonas campestris. Transfer learning was used to fine-tune AlexNet. Feature visualization from the trained CNN indicate the excellent ability of self-learned features. Three comparative experiments were conducted to compare the performance of CNN with the traditional classification methods including Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Back Propagation (BP) neural network in identifying peach leaves. Confusion matrix of each method displays that CNN can identify the peach leaves affected by Xanthomonas campestris with the accuracy of 100%. ROC (Receiver Operating Characteristic) curves and AUC (Area Under ROC Curve) values, an overall performance measurement, show that CNN achieves higher performance with AUC value of 0.9999. The test of significant experiment shows that CNN is significantly superior to the other three mentioned methods, which the p-values is 0.0343 (vs.SVM), 0.0181 (vs.KNN) and 0.0292 (vs.BP). In a word, CNN is superior to the state-of-the-art in identifying diseased peach leaves.

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This research represents an evaluation study of the linear and non-linear mathematical methods applied to predict the biogas flow rate in anaerobic digestion processes. The anaerobic digestion model No.1 was used to generate the process data. For the prediction of the biogas flow rate the partially least squares regression, the locally weighted regression and the artificial neural networks were used. Two metaheuristic tools, here a genetic algorithm and an ant colony optimization algorithm were applied to improve the prediction models. They carried out the variable selection procedure. The implemented mathematical models could successfully perform the prediction of the biogas flow rate. Nevertheless, more robust and accurate prediction of the biogas flow rate was done with the help of the artificial neural networks. Here the error of prediction was about 9% while the coefficient of determination reached 0.97.

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Pea (Pisum sativum L) and chickpea (Cicer arietinum L) are important grain legumes grown in the Palouse region of the Pacific Northwest United States. The USDA-ARS grain legume breeding program in this region focuses on developing pea and chickpea varieties with high yield potential, resistance to biotic and abiotic stresses, and superior agronomic characteristics. In this study, aerial high resolution multispectral imaging was evaluated to phenotype yield potential differences among genotypes in green pea, yellow pea and chickpea. Five experiments (three field pea and two chickpea) with 10–25 varieties grown at two locations (Pullman, Washington; Genesee, Idaho) were assessed. Images were acquired approximately 60, 70 and 90 days after planting (DAP) at 110 m above ground level. Normalized difference vegetation index (NDVI), green normalized difference vegetation index, soil adjusted vegetation index (SAVI) and simple ratio (SR) image based features (SUM, MIN, MAX, MEAN) were extracted. In most cases, the MEAN NDVI data was found to be consistently correlated with dry seed yield (p < 0.05), with green pea genotypes showing strongest relationship (r = 0.64–0.93 at about 70 DAP, both during “plot-by-plot” and “by genotype” comparisons). The MEAN SAVI and SR values were also strongly correlated with yield at 61–72 DAP in most of the pea experiments. The data collected during flowering and early pod development phenological growth stages was found to be useful in yield estimation. The developed methods can be used for early generation evaluation in breeding programs, where yield cannot be estimated due to limited seed availability.

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The study evaluates the Centimeter Level Augmentation Service (CLAS) of the Quasi-Zenith Satellite System (QZSS) for controlling a robot tractor. The QZSS transmits augmentation information through an L6 signal to enhance the positioning accuracy of the Global Navigation Satellite System (GNSS). Besides accessing the augmentation information through the L6 signal using a commercial QZSS receiver, this paper also introduces a method for using CLAS with a dual frequency receiver that cannot receive the L6 signal. Stationary and dynamic positioning experiments prove that the QZSS is able to improve the accuracy and availability of the current GNSS. Compensating for the biases of the CLAS positioning results relative to the current GNSS, a robot tractor works along with GNSS-based navigation within 5 cm accuracy.

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In order to study the speed ratio regulation and dynamic change of hydraulic mechanical continuously variable speed tractor, it is necessary to comprehend the dynamic characteristics of the hydraulic circuit. The identification method was adopted to study the pump-motor system of transmission. Firstly, the typical identification method of combination modeling was selected to establish the model, and then the corresponding experiments were designed to determine the transfer function parameters and models of the combined modeling. Based on these, through further simplification and indirect methods, with the help of MATLAB toolbox, a fast system identification method was established by calculating the transmission ratio of the pump motor system through the output speed of the gearbox, the engine speed and the drive ratio of the front gear of the pump, as well as the transmission ratio of the gearbox. Filter was used to remove the influence of noise during the experiment. Compared with the test data, the models established by the two identification methods have higher accuracy. The positive and negative fitting rates of the fast identification method are 91.85 and 91.13, respectively, which can meet the needs of subsequent research. This study can be used as a reference for the subsequent control design of transmission and the study on the quality of the transmission.

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Corn and soy have wide-ranging uses in food and biofuel industries due to its nutritional and energetic properties. In the present work, artificial drying experiments with hot air convection in different temperatures (30, 40 and 50 °C) were carried out with the addition of ozone (5, 10 and 15 min) applying a central composite design (CCD). The effective diffusion coefficient D_eff as thermodynamic properties was evaluated with and without the incorporation of ozone into drying air on corn and soy. The CCD showed different D_eff values and a numeric model was fitted to moisture diffusion during the drying-ozonation process (DOP) on corn-soy. Activation energy decreased from 43.90 to 35.20 kJ mol⁻¹ for corn and 38.23 to 34.29 kJ mol⁻¹ for soy when ozone was added into the drying air; similar observation occurred to enthalpy and entropy. Thus, the drying-ozonation process can be useful for technological purposes for energy improvement during postharvest stages, as well as maintaining the quality of cereal products and design of new dryers.

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In this approach, an amino-functionalized silica coated multiwall carbon nanotube (AminMagMWCNTs@SiO₂), for the first time, was rationally designed, prepared, and then investigated as an adsorbent for the adsorption and removal of Pb (II) and Cd (II) from environmental samples. The properties of synthesized magnetic nanoadsorbents were analyzed by Fourier transform infrared spectroscopy (FT-IR), X-ray powder diffraction (XRD), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The diameter of magnetic nanoadsorbents was in the range of 60–80 nm. The effects of various parameters on the adsorption efficiency were simultaneously studied using a chemometric design. The variables of interest were the amount of nanoadsorbent, pH and ultrasonication time. The experimental parameters were optimized using a Box–Behnken design and the response surface equations were derived. The removal of magnetic nanoadsorbents from the aqueous solution was simply achieved by applying an external magnetic field following the adsorption process. The adsorption efficiencies of AminMagMWCNTs@SiO₂ nanoadsorbent for Pb (II) and Cd (II) ions were in the range of 98–104% under the optimum condition. The results demonstrated that the amino-functionalized MagMWCNTs@SiO₂ nanoadsorbent could be used as a simple, efficient, regenerable and cost-consuming material for the removal of desired heavy metal ions from environmental water and soil samples.

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Grapevine yield prediction during phenostage and particularly, before harvest is highly significant as advanced forecasting could be a great value for superior grapevine management. The main contribution of the current study is to develop predictive model for each phenology that predicts yield during growing stages of grapevine and to identify highly relevant predictive variables. Current study uses climatic conditions, grapevine yield, phenological dates, fertilizer information, soil analysis and maturation index data to construct the relational dataset. After words, we use several approaches to pre-process the data to put it into tabular format. For instance, generalization of climatic variables using phenological dates. Random Forest, LASSO and Elasticnet in generalized linear models, and Spikeslab are feature selection embedded methods which are used to overcome dataset dimensionality issue. We used 10-fold cross validation to evaluate predictive model by partitioning the dataset into training set to train the model and test set to evaluate it by calculating Root Mean Squared Error (RMSE) and Relative Root Mean Squared Error (RRMSE). Results of the study show that rf_PF, rf_PC and rf_MH are optimal models for flowering (PF), colouring (PC) and harvest (MH) phenology respectively which estimate 1484.5, 1504.2 and 1459.4 (Kg/ha) low RMSE and 24.6%, 24.9% and 24.2% RRMSE, respectively as compared to other models. These models also identify some derived climatic variables as major variables for grapevine yield prediction. The reliability and early-indication ability of these forecast models justify their use by institutions and economists in decision making, adoption of technical improvements, and fraud detection.

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A pneumatic harvesting, separating, and cleaning machine was designed and constructed to collect Jojoba seeds from the soil surface using cyclonic separation process. Jojoba seeds do not mature at once, whenever part of seeds reach maturation they fall naturally to the ground. So, more than one harvesting in the season may be necessary, depending upon weather conditions and grower preferences. To design the machine successfully, some of the physical and aerodynamic characteristics of Jojoba seeds and other undesired materials mix with the seeds were measured. The pneumatic harvesting machine was constructed at the workshop of Agricultural and Biosystems Engineering Department, Alexandria University, Egypt. The performance of the harvesting machine was investigated under different operation conditions such as suction air velocity, machine forward speed, length of the suction hose, clearance of the suction hose inlet above the soil surface, and the different ratios of materials other than Jojoba seeds (MOS). It was found that the best-operating conditions are when the suction air velocity is 30 m. s⁻¹, hose length is 2.5 m, suction hose clearance from the soil surface is 5 cm, machine forward speed is 1.2 km .h⁻¹, and the ratio of seeds to material other than seeds is 0.1. The research is atrial to produce a harvesting machine and then evaluated its performance under simulated field conditions. Although the performance of the harvester was considered satisfactory, it requires additional modifications and parts to make it commercial applicable.

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The present study concerns the thermal pyrolysis kinetics of sieved rice husk that was classified into two sizes (38–200 μm) and (200–1000 μm) by using Thermo-Gravimetric analysis (TGA) at different heating rate (HR) values under N₂. The thermal pyrolysis analysis was presented and kinetic parameters as activation energy (E), frequency factor (A), and order of reaction (n) were determined by using three different kinetic models. The effect of heating rate (HR) and particle sizes on the chemical kinetic parameters were presented and discussed. Direct method gave lower values of E and A compared to the integral method. Results showed that as particle size increases, values of the activation energy (E) and frequency factor (A) nearly increase. The combustion characteristic parameters such as ignition, burnout and peak temperatures and their corresponding times were determined. It found that larger sizes (200–1000 μm) have a relatively lower ignition temperature, higher activation energy and noticeably lower ignition times as compared to the smaller sizes (38–200 μm).

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Increase in global prices of grains further adds to difficulties in feeding livestock. Total Mixed Ration (TMR) formulated with food and agricultural by-products is considered as alternative animal feeds. However, it has associated problems particularly but not limited to decomposition due to high moisture content in most of them. To solve this problem, fermentation technology was brought up on TMRs. However, the fermentation condition may vary depending on the composition of the TMRs. This study set out to identify and determine a fermentation condition which can be applied regardless of the TMR composition. The Taguchi method L9 (3⁴) orthogonal array was adopted in this research. The study considered 3 levels of 4 controllable factors (temperature, moisture content, bulk density, and fermentation period) and 2 uncontrollable factors (compositions and ratio of TMR samples). Quality score was calculated using the silage quality assessment method by analyzing pH and organic acid content (lactic acid, acetic acid, and butyric acid). Fermentation 40 L volume chamber (ϕ 300 × 400 H) was built and three TMR samples were fermented for the validation test. Results indicated that animal feed formulated with by-products had the highest quality score at a fermentation temperature of 20 °C, moisture content of 50%, a bulk density of 0.6 kg/m³, at 96-h fermentation period. This fermentation condition delivers the silage quality score of over 82 regardless of the composition of the materials used in formulating the feed.

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In the grim situation of wood shortage, efficient utilize forest resources and rational use of wood have an important significance. Different kinds of trees have different use-value, so it is very important to identify the species of trees. Different species of trees have their own leaf characteristics. In this study, we proposed a novel feature extraction method based on geometric features and Haar wavelet, which can achieve the tree leaves feature rapid extraction. Extracting the geometrical features of leaves, at the same time, make Haar wavelet triple decomposition to the leaf image, calculating the leaves statistical characteristics like energy, entropy and mean values etc. Finally realize the recognition of tree species. The experimental results show that geometric features and statistical characteristics have significantly different, these differences can effectively identify the types of tree by using the classic adaboost threshold classifier, and the method is effective and practicable.

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Producing bioenergy from the anaerobic digestion (AD) of poultry droppings (PD), press mud (PM), sugarcane bagasse (SB) and sugar beet roots and tops (SRT) could be an effective source of fuel and energy for processing sugar from sugar beet and sugarcane and for reviving and making the sugar industries profitable in Bangladesh. The total solids (TS) content is crucial for an optimum performance of the AD process. In this study, batch assays were conducted to determine the optimal TS contents on the mesophilic AD of PD, PM, SB and SRT with TS contents of 5, 8, 11 and 15%, respectively. The highest biochemical methane potential (BMP) were found 254, 121, 205 and 23 NL kg⁻¹VS for PD, PM, SB and SRT after digestion for 90 days at TS content of 11%, 15%, 11% and 8%, respectively. The results indicate that the initial TS influenced the AD performance significantly and modeling showed that the optimal initial TS content for AD of PD, PM and SB ranged between 12 and 13%.The only exception was SRT, where an initial TS content of 8% is recommended.

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Diversification of processed products based on sago flour has been made, including glucose syrup production, which may be used as a substitute for sucrose sugar in various processed foods. This sago starch-based glucose syrup may be improved its added value by fortifying antioxidant and iron ingredients derived from the extract of Moringa and Katuk leaves. We study the fortification effect of Moringa and Katuk leaf extract on the properties of glucose content, iron, and vitamin C (L-ascorbic acid) of the sago liquid sugar. This study used a Completely Randomized Design with six treatments, which were the combination of the extract of Moringa, Katuk, and liquid sugar. Based on this study, we obtain the fortification of Moringa and Katuk leaf extract had a significant effect on the iron and vitamin C content but had no significant effect on glucose content. The control of G0 had glucose content of 83.70%, and increasing the content of leaves extracts had decreased the glucose content. Iron (Fe) and vitamin C contents had improved, as the leaves extracts were increased. Treatment of G4 had given the highest content of Fe (3.35 mg/100 g), and treatment of G5 had resulted in the highest Vitamin C content (5.26 mg/100 g). This study indicated that sago flour may have a good prospect in producing a variety of nutritional food ingredients, and at the same time its added value may be made.

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A mechanized sugarcane production system with small machinery is important because it is good farming management, lowers carbon, saves energy and conserves the environment. Making a database is necessary to achieve high working efficiency and low fuel consumption of farm machines such as harvesters and tractors. Mechanizing Okinawa farms with small machines is important. Two small sugarcane harvesters were recently introduced to Okinawa. The time and fuel consumption to operate, harvesting, hauling out, stopping, and traveling forward and backward were measured. A computer program to estimate these variables was developed based on past and current performance tests. There was little difference between estimated values and measured data.

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Distributions of thermo-physical properties: such as porosity and thermal conductivity of Japanese apricot and pear during storage were determined based on X-ray CT image analysis. Japanese apricot was stored at 25 °C, whereas pear was stored at 25 °C and 5 °C. Average CT value was determined based on a series of X-ray CT images captured for each whole fruit. At the end of storage period, the average CT value decreased in Japanese apricot and pear at 25 °C, whereas it was the almost same as pear stored at 5 °C. Porosity increased, whereas thermal conductivity slightly decreased at 25 °C in Japanese apricot and pear. As a result of the experiment, it seemed that the internal structure of pear stored at 5 °C was well maintained during storage. Conversely, void space progressed in Japanese apricot and pear during storage at 25 °C. The porosity and thermal conductivity distributions were visualized based on the CT image during storage.

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Agriculture remains a vital sector for most countries. It presents the main source of food for the population of the world. However, it faces a big challenge: producing more and better while increasing the sustainability with a reasonable use of natural resources, reducing environmental degradation as well as adapting to climate change. Hence, it is extremely important to switch from traditional agricultural methods to modern agriculture. Smart Agriculture is one of the solutions to deal with the growing demand for food while meeting sustainability requirements. In Smart Agriculture, the role of information is increasing. Information on weather conditions, soils, diseases, insects, seeds, fertilizers, etc. constitutes an important contribution to the economic and sustainable development of this sector. Smart management consists of collecting, transmitting, selecting and analyzing data. As the amount of agricultural data increases significantly, robust analytical techniques capable of processing and analyzing large amounts of data to obtain more reliable information and much more accurate predictions are essential. Data Mining is expected to play an important role in Smart Agriculture for managing real-time data analysis with massive data. The aim of this paper is to review ongoing studies and research on smart agriculture using the recent practice of Data Mining, to solve a variety of agricultural problems.

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The growth rate of mycelium of the tiger's milk mushroom (Lignosus rhinocerotis) at intermediate development stage was successfully increased up to 16% by the application of corona discharge through multiple needles at a steady state voltage of 5 kV generated by a Van der Graff generator, for 5 h a day, for four weeks. The same method could enhance the yield of tuber up to 56%. Myco-chemical analysis on the tuber of the corona treated group did not show any significant variation in the total flavonoid content and metabolite chromatogram pattern in comparison with that of the control groups and the reference groups. The experiment shows that the enhancement of the harvest of tuber of the tiger's milk mushroom is much more significant than the growth rate improvement of the mycelium of the same type of mushroom, as it was reported previously.

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An approach was developed to evaluate the mechanism involved, based on the interaction of rutin with an excess of metal chloride (magnesium, nickel or aluminum). It was found that the binding constants increased in the following order: 2.57 ± 0.23 × 10⁴ L mol⁻¹ for magnesium (II), 5.61 ± 0.45 × 10⁷ L mol⁻¹ for nickel (II) and 3.46 ± 0.16 × 10⁹L mol⁻¹ for aluminum (III). The stoichiometric ratios (metal:rutin) were 1:1 for rutin and magnesium (II), 2:3 for rutin and nickel (II) and 1:2 for rutin and aluminum (III), determined by titration. This strategy allows the detection of aluminum ions in the presence of calcium, potassium, nickel and magnesium ions, demonstrating that this method provides a promising cationic sensor. So, these results open up a perspective for the study of the interaction mechanism of rutin and for the development of materials capable of capturing metal ions toxic through the construction of efficient bioinorganic systems.

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To rapidly and accurately detect the quality of blueberry, hyperspectral imaging (HSI) technique was used to simultaneously detect the soluble solids content (SSC) and firmness (FI) of blueberry. In total, 204 blueberry samples, including 164 samples in Calibration set and 40 samples in prediction set, were investigated in this study. Multi-stage successive projections algorithm (MS-SPA) and SPA1/SPA2 were proposed to select a few feature wavelengths from the spectral region of 450–950 nm. Prediction models were developed based on partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BPNN) model. The results showed that prediction model based on MS-SPA performed better in prediction results. Furthermore, the prediction based on BPNN model was better than that based on PLSR and SVR models, which used full spectrum (FS), SPA1/SPA2, MS-SPA, respectively, to select feature wavelengths. This research suggested that MS-SPA-BPNN model, which obtained the best prediction results of SSC (R_P = 0.894, RMSEP = 0.220), and FI (R_P = 0.843, RMSE = 0.225), was a reliable tool to detect SSC and FI simultaneously. The visualization of distribution map of parameters was an intuitive and convenient measurement for quality detection of blueberry. The method could provide a theoretical basis for developing an online detecting and grading system of blueberry quality based on multispectral imaging technique.

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Fuel consumption and power take-off (PTO) power requirement were measured for a 33.8 kW two-wheel drive tractor when used for operating a 1.6 m rotavator with 36 “L” shaped blades in sandy clay loam soil at an average soil moisture content of 8.8 ± 1% (dry basis) at IIT Kharagpur, India. Field experiments were conducted for a tractor with rotavator at seven different engine speeds (between 35 and 75% of full throttle engine speed), gear settings (L2 and L3) and depths of operation (60, 80 and 100 mm). Depth of operation, engine speed and gear setting were found to affect the fuel consumption of tractor. For the same PTO power consumption, lesser fuel consumption of tractor was observed in gear up conditions. A variation from −3.60 to −19.67% was observed while comparing the observed fuel consumption values with those predicted by the American Society of Agricultural and Biological Engineers (ASABE D 497.7) model. These variations were due to non-inclusion of gear settings in the ASABE fuel consumption model. Hence, an attempt was made to modify the ASABE fuel consumption model by incorporating gear settings in terms of speed ratio (peripheral speed of the rotavator to forward speed of the tractor i.e. u/v ratio). The developed fuel consumption model comprising engine speed, PTO power consumption and u/v could predict the observed values with a variation of ±6%.

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Cocoons of the mulberry silkworm Bombyx mori L. are the main raw material for the silk production. Currently, at the market, their quality assessment and pricing are done on a few random samples by manual method, which is shaking cocoons with hand and assessing the generated sound, due to the absence of automated systems and time constraint. This manual method is subjective, laborious and prone to errors. A novel automated cocoon quality assessment system is proposed, which not only classifies them into good and defective ones but also subclassifies the later into dried and mute cocoons. A unique vibration impact acoustic emission (VIAE) is generated from each category due to the difference in the physical state of pupa inside the cocoon. In this system, the cocoons were vibrated using a plastic arm attached to a servo motor driven by Arduino board and the VIAE so generated was recorded by two microphones. A computer loaded with a custom-made algorithm preprocess the VIAE and compared its area under the curve of power spectral density against the pre-known threshold values, to identify the cocoon category. This automated system could successfully classify 86 cocoons with 100% accuracy in 4 s (excluding the duration of VIAE recording). This is better than the manual method in terms of accuracy, cost and skilled laborer dependency. This could make it a good replacement for the manual method to ensure the fairer cocoon trade in the market and better silk quality in the reeling centers.

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