Three-dimensional reconstruction has great potential to improve not only the post-harvest quality control but also the breeding efficiency in horticulture. The depth information of the consumer-grade RGB-depth sensor was unreliable compared to that obtained from industrial sensors. To cope with this disadvantage, the generated point cloud was corrected within a region of interest of the target fruit, which was extracted from the color image of the sensor. Evaluating more than a hundred apple fruits, the root-mean-square error of the volume and the largest diameter were less than 6 cm³ and 1 mm, respectively. Reconstruction of various kinds of fruits and vegetables were demonstrated. The proposed method can be applied to accelerate the quantification of three-dimensional features of agricultural products.

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The present manual sorting technique is not effective to detect fungal infection in dates; especially at the early stage. The potential of near infrared (NIR) area scan imaging (900–1700 nm together as one image) to detect fungal contamination in three popular varieties of dates (Fard, Khalas and Naghal) was investigated. Date samples were treated as three groups: untreated control (UC), sterile control (SC) (surface sterilized, rinsed and dried) and infested samples (IS) (surface sterilized, rinsed, dried and fungal inoculated). The IS was then incubated for 10 days and imaged every 48hr to obtain 5 infection stages namely IS Day2, IS Day4, IS Day6, IS Day8 and IS Day10. In total, 3150 NIR images (UC + SC + five fungal infection stages × 150 images × three date varieties) were acquired and analyzed. The overall highest classification accuracy was 97, 96 and 100% for two-class, six-class and pair-wise models, respectively while comparing IS with UC. Similarly, it was 94, 89 and 94% for two-class, six-class and pair-wise models, respectively while comparing IS with SC. However, when the developed algorithm was tested on pooled dates images (all three varieties combined), the two class model yielded a higher classification accuracy of 83 and 86% for UC and IS, respectively; and 71 and 85% for SC and IS, respectively. Thus, NIR area-scan imaging has the potential to be used as a fast and cheaper technique to detect fungal infection in food industries.

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The goal of this research was to evaluate the environmental effects of the production of soybean (Glycine max), sunflower (Helianthus annuus L.), and canola (Brassica napus) oilseed crops in Ardabil in Iran. Lifecycle assessment was conducted to compare environmental burdens, water demand, and land use of these three crops. Four categories of environmental impacts were used to define in this method. The categories are eutrophication, acidification, global warming potential, and photochemical oxidation. Additional categories were water dependence and land use. These environmental effects followed the same pattern for the three oilseed crops. The results showed that sunflower demonstrated a higher environmental load in five of the six categories because of its lower seed yield and higher water and energy demands. The results indicate that production of 1 T of soybean and canola requires 370 and 471 m² of land, respectively. Production of same quantity of sunflower required 426 m² of land. Sunflower required 1.59 and 1.25 times more water than the canola and soybean respectively, to produce the same quantity of oilseed. It was found that the effects from the three crops comes generally from manufactured fertilizer, manure, diesel combustion, agricultural practices, and electricity for irrigation. It can be said that the indirect effects associated with these inputs are related to producing and processing, which had higher impacts than those of the direct effects.

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This area of study gives the consequence of respiration rate of bhimkol (Musa balbisiana) at different storage temperatures (15–35 °C). The relationship between O₂ step down rate and CO₂ step up rate was ascertained using gas measuring data set collected by enclosing bhimkol in respirometer and measuring head space O₂ and CO₂ concentration over time. A mathematical method was used to predict the influence of storage temperature and time on respiration rate of bhimkol and the best fitted model was subsequently implemented to design modified atmospheric packaging for bhimkol. The experimental data set were used to acquire two different models namely regression function model and enzyme kinetics model and among both the models the enzyme kinetics model was found to be the better fitted model to predict the respiration rate of bhimkol banana. Modified atmospheric packaging reckons the adaptation of gas surroundings within the package by means of normal interchange involving the respiration rate of bhimkol and gas diffusion through the package material. Based on the respiration rate and permeability of the packaging materials, the best suitable packaging films obtained for the modified atmospheric packaging of bhimkol was found to be polypropylene, and polyvinyl chloride film. The explicated model furnishes an efficient technique for selection and design of suitable packaging material for bhimkol banana.

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Each year, large volumes of sweet pepper seeds (Capsicum annuum L.) are exported from Thailand to many countries. As a result, seed quality is very important. Sweet pepper seeds are very small, both in dimension and mass, therefore they are very difficult to inspect manually. Thus, we designed and constructed an automated sweet pepper seed sorting machine. It consists of three main stations: 1) seed feeder, 2) seed inspector using a camera and 3) seed sorter using a vacuum suction system. To transport seeds through each station, we designed a circular rotating plate with radial rows of blind holes. This allows all three stations to process a row of seeds in parallel. To identify discolored seeds, images were captured and processed to detect dark areas on the surface of the seeds. Our machine accurately sorted 90.9% of input seeds. The maximum potential capacity (in terms of seed processing rate) was 16,500 seeds per hour and we achieved actual rates of approximately 12,700 seeds per hour or roughly twice as fast as an experienced operator. Thus, this machine is faster and more accurate than human operators and can replace them.

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In this paper, the development of a novel quality assessment system for Bombyx mori L. cocoons is presented, which offers significant advantages over the conventional manual method (subjective, tests only few sample cocoons, involves health hazards) in terms of labor friendliness, accuracy, speed and running cost. This system consisted of a conditioned illumination unit, image acquisition and processing unit realized with a smart camera. The camera acquired the images of cocoons and by image processing algorithms (morphological operation, image enhancement, and ellipse fitting), quantitative measurements of size, shape and stain color were accomplished and automatically classified each cocoon into four defective categories and good cocoons. The system not only highlighted each category on camera screen but also displayed statistical information such as counts of cocoons in each category and overall defect percentage. In addition to that, the system was programmed to alert the user when the defect percentage exceeded a particular threshold value. The results showed that the system was capable of assessing 96 cocoons per second acquired within a single frame. It showed 100% accuracy on a sample size of 137 cocoons. To expose whole cocoon surface, they were rolled over a slope at a speed of eight rotations per second, while the system captured and processed the video of the whole surface. This process enabled in meeting the same level of quality assessment standard and counting accuracy as that of manually exposing the defective areas to the field of view when acquired in a single image.

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Sugarcane-farming corporations manage large-scale farmlands, labor, and machinery. To efficiently manage daily operations, a systematic and detailed scheduling system is needed. Compared with other common scheduling problems, the sugarcane-farming scheduling problem presents some specific characteristics, such as cooperative work and uncertainty. By analyzing the practical constraints, we propose a new mathematical model and a hybrid approach to solve the scheduling problem of sugarcane-farming corporations. The approach comprises a meta-heuristics simulated annealing (SA) and a mixed integer programming (MIP) solver of the GNU linear programming kit. The SA algorithm is used for resource assignment and search control, whereas the MIP solver is used to acquire the optimal solution for resource assignment. For planning the farm works on large-scale-dispersed farmlands in the sugarcane-farming corporations, the hybrid approach is competent to mathematically calculate an optimal schedule with minimum completion time; this was explored from enormous candidate farm work permutations and resource combinations. The results demonstrate that the proposed hybrid approach can determine an optimal resource assignment and farm work schedule for a small-scale problem. The proposed approach is applicable to the construction of a long-term detailed scheduling system for sugarcane-farming corporations.

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This paper describes a 3-stage simulation method to i/extract from experimental results the best validated empirical models of drying kinetics, ii/establish the correlations between the model coefficients and the drying airflow parameters of temperature and velocity, and the sample thickness, and then ii/use other experimental results to compare and confirm the identified model. This simulation study was applied to the case of pumpkin slices using the Response Surface Methodology (RSM) to describe the moisture ratio MR versus time. Seven thin-layer drying models including Newton, Page, Modified Page, Handerson and Pabis, Logarithmic, Midilli-Kucuk and Approximation of Diffusion models were fitted to experimental data, using nonlinear regression. It has been found that Approximation of Diffusion, Page, Midilli-Kucuk yielded the best fit. Then, best three models were selected and examined intensively, for slice thickness ranged between 0.2 and 1.4 cm, within the airflow temperature ranges (40–80 °C) and velocity (2–15 m/s). Midilli-Kucuk model gave the best correlation between the experimental and estimated data. The relationships between the model parameters (k, n, a, and b) and the drying conditions, slice thickness, and time were determined. Thus, this empirical Midilli-Kucuk thin-layer drying kinetic model including the drying conditions can accurately described with a good fitness predict and simulate the moisture ratio value for a drying process of pumpkin slices.

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In this study, the application of a versatile approach for modeling and prediction of the moisture content of dried savory leaves using hybrid artificial neural network-genetic algorithm has been presented. Genetic Algorithm was used in order to find the best Feed Forward Neural Network (FFNN) structure for modeling and estimation of moisture content in the drying process of savory leaves. The experiments were performed at three air temperatures of 40, 60 and 80 °C and at three levels of relative humidity 20%, 30% and 40% and air velocity of 1, 1.5 and 2.0 m/s for drying the savory leaves in the forced conductive dryer. Optimized neural network by GA had two hidden layers with 9 and 17 neurons in first and second hidden layers, respectively. Mean Square Error (MSE) value (0.000094606) and correlation coefficient (0.9992) of FFNN-GA experiments showed that moisture content can be accurately predicted from the input variables: air temperature, airflow velocity, relative humidity and drying time. Moreover, results showed that the optimized neural network topology could denote the superior ability of this intelligent model for on-line prediction of the moisture content of Savory leaves in different drying conditions.

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Plectranthus amboinicus (lour) Spreng is the herbal species belongs to the family of Lamiaceae. The leaves of this species contain Thymol which is the main bioactive compound that has anti-inflammatory, antibacterial, anti- fungal and antioxidant properties. In this study, oven dryer has been chosen due to its easy handling and simple operation. The Plectranthus amboinicus leaves were dried under different temperatures at 50 °C, 60 °C, 70 °C and 80 °C using oven dryer. Drying took place at constant rate and falling rate period. The moisture ratio data were fitted to six thin layer models chosen from literature. Among these models, Midilli and Kucuk model was found to be the best model to represent the drying behaviour of Plectranthus amboinicus leaves due to the highest value of coefficient of determination (R²) and lowest values of root mean square error (RMSE) and reduced Chi-Square (χ²). Besides, the effective diffusivities varied from 1.167 × 10⁻¹⁰ to 4.451 × 10⁻¹⁰ m²/s which was within the general range for food material. Ultrasound-assisted extraction (UAE) was performed to analyse the quality of dried P. amboinicus leaves with ethanol as the solvent using probe sonicator (130 Watts, 20 kHz) with solid to solvent ratio 1:20 for 30 min. From gas chromatography analysis, leaves dried at 70 °C gave the highest concentration of thymol compared to drying at other temperatures.

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Instrumentation and Artificial Intelligence (AI) recognition techniques were developed for automatic pest detection. This system is based on pest detection and monitoring, and it improves the efficiency of vegetable and fruit farming and food production. This paper presents an automatic pest detection system that applies artificial neural networks. The system automatically detects two defoliating pests on potato and bean crops: Mexican Bean Beetle (MBB) and Colorado Potato Beetle (CPB) in the adult stage. The neural classifiers utilized for the beetle detection are RSC (Random Subspace Classifier) and LIRA (Limited Receptive Area). The MBB images that were employed as inputs to the classifiers were obtained on Mexican plantations. The CPB images were collected from various Internet sources. We compared the results obtained with both classifiers on image databases. The RSC classifier demonstrates the better result for recognition, which is 89%, while LIRA presents a recognition rate of 88%. These results are good for pest detection and can be used for the diagnosis of pest locations in crops. The purpose was to contribute to the development of automatic detection applications based on images of potato and bean plantations. In Mexico and other countries, it is of great importance to solve pest problems in agriculture. We chose insect recognition due to the importance of potato and bean crop production and consumption. Pest detection in the adult phase is of high priority because of the high rate of crop defoliation and destruction. Our automatic pest-detection system can be employed in pest recognition in monitoring activities.

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Mango kernel seed oil is rich in unsaturated fatty acid, bioactive compounds and a potential source of butter production. This study explores the unit operation of size reduction which is a vital step in the production of mango seed kernel flour and hence, mango seed kernel oil. The mechanical property of mango seed kernel, its grinding characteristics using hammer mill and then particle size distribution (PSD) of its flour were evaluated. Comparison was made of the PSD using laboratory sieve shaker and laser diffraction technique. The Kick's model had the best energy efficiency followed by Bond's and Rittinger's models respectively. PSD by Laser diffraction method indicated higher mass relative span (RSm), Inclusive graphic skewness (IGS), kurtosis of PSD (Kg) and graphic means. Rosin-Rammler-Bennet (RRB) and Gaudin-Schuhmann (GS) models were effectively used to describe the statistical and model parameters of the particle size distribution data.

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Bamboo forests in the Fukushima area were contaminated with radiocesium following the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident. There is a need to develop technologies for remediation of contaminated bamboo forests. However, remediation technologies such as incineration of contaminated plants, which not only harms the environment but also destroy the local resources. This research introduces a novel technique, which reduces radiocesium by boiling the thinned bamboo, with the aim of commercializing the product as bamboo chip compost. We compared the ¹³⁴Cs and ¹³⁷Cs contents of boiled and unboiled bamboo chips, and also evaluated the effect of treating the bamboo boiled water extracts with bentonite. In addition, we compared the radiocesium changes and nutrient statuses of bamboo chip composts derived under aerobic and anaerobic conditions. Furthermore, we evaluated changes in soil properties and the growth of komatsuna (Brassica rapa var. perviridis) following application of final composts at 0, 2.5, 5, and 10 kg m⁻². We found that boiling bamboo with 3% NaHCO₃ for 30 min decreased the Cs content by approximately 52%, and that the 1% bentonite treatment reduced Cs contamination in water extract by 75%. The compost amendments containing boiled bamboo mixed with sub-materials under anaerobic conditions resulted in significantly lower soil Cs concentrations, than other compost amendments. The addition of aerobic composts consisting of boiled bamboo mixed with sub-materials resulted in significantly higher soil N, P levels and Komatsuna growth, with the plants containing radiocesium concentrations of <0.1 Bq kg⁻¹.

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