Existing commercial cow mastitis detectors require bulky historical data which may be unavailable or are considered expensive in conventional or small parlours. Thus, the objective of this study is to develop a simple, but without significant sacrifice of accuracy, online cow subclinical mastitis detector for conventional and small parlours. The detective indices are derived merely from the electrical conductivity (EC) of milk using linear discriminant and step regression analyses. The detector was validated on 192 milkings of 48 dairy cows from conventional, small parlours. It had a specificity of 83.7% for healthy quarters, a sensitivity of 46.2% for infected quarters, a prediction accuracy of 90.8% for healthy quarters, and a prediction accuracy of 30.7% for infected quarters. The performance is poorer than commercial detectors, but it is good enough for the dairy industry. This study gives the possibility to give alerts in the milking parlour and no need for animal identification.

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To establish a non-destructive method for prediction of mango quality attributes, near infrared reflectance spectroscopy (NIRS) combined with chemometrics was studied. NIR spectra were recorded on intact mangos (cv. Kent, n = 58) in the wavelength range of 1000–2500 nm using a Fourier transform near infrared (FT-NIR) spectrometer, followed by its quality attributes measurement. Partial least squares (PLS) and principal component regression (PCR) based on various spectral pre-treatment (MSC, SNV and first derivative) were used to develop prediction models for quality attributes such as soluble solids content (SSC), titratable acidity (TA) and ascorbic acid (AA). The models yielded satisfactory results with coefficient of determination of calibration ranging from 0.66 to 0.91 (SSC), 0.94 to 0.98 (TA) and 0.62 to 0.92 (AA) while in cross validation the coefficient ranging from 0.51 to 0.66 (SSC), 0.90 to 0.95 (TA) and 0.43 to 0.61 (AA). Standard error resulted in calibration and cross validation were low. It is concluded that NIRS and chemometrics is feasible for rapid and non-destructive prediction of mango quality.

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Juice production is the most important process used for citrus fruits. However, drying process applied in the production of powders, flakes, and slices is also of great importance. Drying behavior of lemon slices was investigated using convective (50, 55 and 60 °C inlet air temperatures), microwave (specific power of 0.97 W g⁻¹ ), and combined microwave-convective (specific powers of 0.97 and 2.04 W g⁻¹ assisted with 50, 55 and 60 °C inlet air temperatures) dehydration methods. The quality was assessed in terms of L∗ a∗ b∗, total color difference (ΔE), chroma, Hue angle, and rehydration capacity measurements. In addition to higher drying rate, the quality parameters for microwave-convective treatments were significantly higher than those for other methods. Drying kinetic curves indicated a constant rate period followed by a falling rate period in both convective and microwave drying methods. However, for microwave-convective treatments a falling rate period was observed with the exception of a very short accelerating period at the start. Mathematical modeling of the drying kinetics was conducted by applying the commonly used drying models to the experimental data. According to the statistical parameters, Midilli et al. model presented the best prediction for the drying kinetics.

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Support Vector Machine (SVM) was used to classify type of produce commonly sold in supermarkets. We applied a sequence of image processing algorithms such as conversion of color space, thresholding and morphological operation to obtain the region of interest from the images. Global and local features were extracted from the images and used as input for the classifiers. The color and texture features extracted in this system were L*a*b* values and texton approach respectively. Since attribute learning has emerged as a promising paradigm for assisting in object recognition, we proposed to integrate it into our system. This could tackle problem occurred when less training data are available, i.e. less than 20 samples per class. The performances of the proposed classifier and conventional SVM were also compared. The experiments showed that the classification accuracy of the proposed classifier is higher than conventional SVM by 7% when only 4 samples per class were trained.

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Plenty of straw is left in field after harvesting, so soil-tillage tool interaction has changed into straw-soil- tillage tool interaction. To obtain the detailed movement of soil and straw and identify interaction be- tween straw, soil and tillage tool, soil bin experiments were conducted by tillage with a rotary blade. Lengths of wheat straw were 130–230 mm with increments of 20 mm. One IT225 rotary blade was operated at rotary speed of 77 r/min and forward speed of 0.222 m/s, and three rotavator kinematic parameters (6.8, 8.8 and 10.8) with working depth of 100 mm. Tin alloy cubes (2 cm³) and colored straw were used for measuring soil and straw movement. Moreover straw forward movement was observed greater than soil movement. The unburied percent of 150 mm straw decreased from 98% to 91% when rotavator kinematic parameters increased from 6.8 to 10.8. Further results indicated that straw mixtures might not fit for studying soil displacement, especially for lateral soil displacement; higher rotavator kinematic parameter resulted in soil and straw being spread in a larger area and more straw being buried. The average displacement of lateral and longitudinal straw tracers is recommended for forward straw displacement, and longitudinal straw tracer is used for side one.

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In France, more than half of the production of sewage sludge is spread on agricultural fields as a fertilizer. However, these biosolids contain nutrients that can pollute the soil if their distribution on field is heterogeneous: it is then necessary to optimize the spreading in order to limit their environmental impacts. In that purpose, this study focuses on the impact of pasty materials rheological behaviour on spatial distribution parameters obtained with a centrifugal spreader, i.e. a spinning disc system. Kaolin suspensions were employed. To model their rheological properties, a HerscheleBulkley equation was used, which is consistent with the literature. Experiments were performed with a specific spreading device (a vane with no disc) and with the CEMOB, a bench dedicated to the analysis of organic fertilisers spreading. Results showed that radial distribution of the 3D spread patterns is clearly dependent on the size and the density of the particles, both of them being related to the material consistency. Angular distribution also tends to follow similar tendencies. Eventually, advanced imaging shows a relation between fragmentation process and material consistency. This study presents a new approach to analyse organic spreading and opens the way to more developments.

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Moringa oleifera seeds, the crop of interest in this work is an oil bearing seed which is a viable source of biodiesel. Moringa seed is an oil bearing seed with great potentials and this has necessitated the development of a machine that can be used to shell the seed. In this work, moringa seed shelling machine was designed and constructed. The major components of the machine include hopper, Tangential Impact shelling Device (TISD), screen, fan compartment and outlets for both the kernels and shells. The performance evaluation of the machine was carried out. The machine was tested by varying the moisture content of the seed from 8.43% (w.b.) to 34.59% (w.b.) at five moisture levels (viz. 8.43, 11.38, 23.69, 29.56 and 34.59%) while dependent parameters such as machine capacity, shelling efficiency, percentage whole kernel recovered, percentage broken kernel recovered, percentage unshelled seeds and overall machine efficiency were determined. The machine capacity, shelling efficiency and overall efficiency were found to have maximum values of 5.39 kg/hr, 86.00% and 57.98% respectively at different moisture content levels while the percentage whole kernel recovered, broken kernel recovered and unshelled seed recovered were found to be 52.38%, 23.00% and 7.67% respectively at various moisture content levels. The values of parameters obtained showed the varying effects of moisture content on the performance of the machine when using TISD for shelling moringa seed.

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In the present study, the effectiveness of polymer (maleic-itaconic acid) on ammonia (NH₃) emissions reduction and in retaining nitrogen (N) in fresh liquid swine manure (SM) was evaluated. The relationship between pH and NH₃ emission was also determined. Different doses of polymer (namely Treatment 1 = T1 = 0.8 L polymer/ton of manure, Treatment 2 = T2 = 1.6 L polymer/ton of manure, Treatment 3 = T3 = 2.4 L polymer/ton of manure, and Treatment 4 = T4 = 3.2 L polymer/ton of manure) were added to the SM and its effects were observed for 30 d. The tests results showed significant reduction in pH for T1, T2, T3 and T4 compared to control (C). For the short term (up to 3d) T2, T3, and T4 showed significantly lower NH₃ gas concentrations than C, however, for the long term (up to 10–20 d) only T4 continued to indicate significantly lower NH₃ gas concentrations. Although numeric observations were reported for other treatments (T1, T2 and T3), no significant differences in NH₃ gas concentrations were found. The NH₃ emissions reductions were calculated in the range of 81–92%, 31–88%, −39–61%, 6–41%, −106% to −6% for the treatment period of 1, 3, 10, 20 and 30 d, respectively. The addition of polymer resulted in no significant difference in total ammonia nitrogen (TAN) and NO₃⁻–N concentration. However, the addition of polymer had a significant influence on total Kjehldahl nitrogen (TKN) and soluble chemical oxygen demand (SCOD) concentration. The NH₃ gas emissions strongly correlated with the manure pH (R² = 0.911–0.999).

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Microwave dielectric spectroscopy (MDS) is an online, compact, non-destructive/invasive, low power analytical methodology based upon the rotation of molecules and their functional groups in the presence of an electromagnetic field in the frequency range of 0.3–300 GHz which may then be used to differentiate materials of different composition. Recent technological developments have increased the availability of the equipment needed to investigate the application of MDS within the food industry. This article gives an overview of the fundamentals of the technology and a review of potential applications in the food industry. Challenges and the future potential of this technology are also considered.

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The use of phase change materials (PCMs) in encapsulated pebbles or pellets as freeze protection method for low-stature plants/crops is proposed and discussed at the conceptual level. Utilizing a simplified geometrical model, it is shown that, for practical applications, small encapsulated-PCM pebbles/pellets of 1-cm sized could provide the necessary heat to prevent the freezing during typical freezing time. Taking into account the balance between the loss of energy on a typical frost night and the latent energy stored in the encapsulated PCMs-pebbles/pellets, the amount of such pebbles/pellets per unit of soil-area was calculated and resulting in the use of less than 2% of the soil-area. The preliminary results at conceptual level are encouraging, however, additional work and R&D is require before its real practical efficacy can be put to test. The choice of the suitable crops, the specific environmental conditions during the freezing time, the manufacture recyclability and environmental impact of the pcm-pebbles/pellets are some of the aspects which must be carefully addressed.

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The measurement of moisture content (MC) for rough rice is important in agro-industry. As the price goes higher of rice products there is a need to apply non-destructive method to measure MC. And by measuring single kernel of rice sample, we can quickly obtain the MC distribution of rice samples. This study aims to apply two spectrometers (meter A and meter B) in visible and near infrared (NIR) regions to record spectrum on three rice types, i.e. single kernel (SK), multi kernels (MK), and cracked multi kernels (CMK). Taikeng No.9 medium rough rice were randomly collected from field after harvested and they were conditioned by oven to six MC levels ranging from 11.5 to 28.7%. Two calibration methods, multiple linear regressions (MLR) and partial least square regression (PLSR), were tested to calibrate mathematical model on reflectance and absorption spectra with the corresponding MC values. Among 72 tested models, the best model was found at PLSR model with first differential of 21 gap points, which had r_c = 0.97, SEC = 1.30 for calibration and r_p = 0.92, SEP = 2.51 for validation. Results also suggested that ten loadings used in PLSR method could have the highest r_p and lowest SEP. When three MC levels were calibrated for SK sample type, a higher r_p value 0.95 was reached. The top five wavelengths selected by MLR were 905, 936, 925, 1015, and 499 nm in 400–1050 nm (meter A) and shifted to 900, 860, 970, 1610, and 830 nm when 400–2498 nm (meter B) was applied. Removing outlier also could improve the model both in six MC groups and three MC group models.

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Broiler body temperature is an important indicator of both a broiler's response to its environment and its overall health. This study used an infrared thermal camera to capture the thermal images of a broiler flock in a broiler house and then analyzed the temperatures of the broiler flock by image processing. Given the lack of feathers on a broiler's head and feet, the thermal images showed a raised temperature for these parts, making the head an appropriate part for measuring body temperature. The average or Gaussian filter was first used to smooth the images, after which the Otsu automatic threshold algorithm was used to determine the gray-scale thresholds and to segment the broiler's head and feet, followed by segmenting of the overlapping broiler images via the watershed method. Area filtering was then used to identify the broiler head blocks among the segmented blocks. Finally, the labeling methodology was used to calculate the number of broilers in each image. Analysis results show that the proposed image processing procedures can successfully identify the broiler head and analyze the temperatures of the broiler flock by capturing thermal images from a height of 160 cm and a depression angle of 30°, while achieving an identification accuracy of up to 91.3%.

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