Herman Ramon

Early Disease Detection in Wheat Fields using Spectral Reflectance

The difference in spectral reflectance between healthy and diseased wheat plants infected with Puccinia striiformis (yellow rust) was investigated. In-field spectral images were taken with a spectrograph mounted at spray boom height. A normalisation method based on reflectance and illumination adjustments was applied. To consider the entire canopy reflection, a spatially moving average was introduced. A classification model based on quadratic discrimination was built on a selected group of wavebands obtained by stepwise variable selection. Through this method, confusion rates dropped from 12 to 4% error classification, based on four different wavebands. These results are very encouraging for the development of a cost-effective optical device for recognising diseases, such as yellow rust, in the field in early spring.

A New Approach to Modeling Hysteresis in a Pneumatic Artificial Muscle Using The Maxwell-Slip Model

Two main challenges in using a pneumatic artificial muscle (PAM) actuator are the nonlinearity of pneumatic system and the nonlinearity of the PAM dynamics. The latter is complicated to characterize. In this paper, a Maxwell-slip model used as a lumped-parametric quasi-static model is proposed to capture the force/length hysteresis of a PAM. The intuitive selection of elements in this model interprets the unclear, but blended contributing causes of the hysteresis very well, which are assumed to originate from the dry friction of the double helix weaving of the PAM braided shell, the friction of the weaving and the bladder, the elasticity of the bladder and/or the deformation of the conical parts of a PAM close to the end caps. The obtained model is simple, but physically meaningful and easy to handle in terms of control.

Weed Detection Using Canopy Reflection

For site-specific application of herbicides, automatic detection and evaluation of weeds is desirable. Since reflectance of crop, weeds and soil differs in the visual and near infrared wavelengths, there is potential for using reflection measurements at different wavelengths to distinguish between them. Reflectance spectra of crop and weed canopies were used to evaluate the possibilities of weed detection with reflection measurements in laboratory circumstances. Sugarbeet and maize and 7 weed species were included in the measurements. Classification into crop and weeds was possible in laboratory tests, using a limited number of wavelength band ratios. Crop and weed spectra could be separated with more than 97% correct classification. Field measurements of crop and weed reflection were conducted for testing spectral weed detection. Canopy reflection was measured with a line spectrograph in the wavelength range from 480 to 820 nm (visual to near infrared) with ambient light. The discriminant model uses a limited number of narrow wavelength bands. Over 90% of crop and weed spectra can be identified correctly, when the discriminant model is specific to the prevailing light conditions.

Discrete element modelling for process simulation in agriculture

This paper presents an overview of discrete element modelling (DEM) as a modelling technique for granular assemblies. It focusses on DEM for agricultural products and processes and discusses important algorithmic and physical issues connected to this domain. Existing applications in the literature are reviewed and an overview of ongoing DEM applications in the Laboratory for Agro-Machinery and Processing is presented.

Optical properties of apple skin and flesh in the wavelength range from 350 to 2200 nm.

Optical measurement of fruit quality is challenging due to the presence of a skin around the fruit flesh and the multiple scattering by the structured tissues. To gain insight in the light-tissue interaction, the optical properties of apple skin and flesh tissue are estimated in the 350-2200 nm range for three cultivars. For this purpose, single integrating sphere measurements are combined with inverse adding-doubling. The observed absorption coefficient spectra are dominated by water in the near infrared and by pigments and chlorophyll in the visible region, whose concentrations are much higher in skin tissue. The scattering coefficient spectra show the monotonic decrease with increasing wavelength typical for biological tissues with skin tissue being approximately three times more scattering than flesh tissue. Comparison to the values from time-resolved spectroscopy reported in literature showed comparable profiles for the optical properties, but overestimation of the absorption coefficient values, due to light losses.

Validation of the wavelet spectral estimation technique in Biceps Brachii and Brachioradialis fatigue assessment during prolonged low-level static and dynamic contractions

An experiment was carried out to investigate the myoelectrical manifestations of fatigue of the Biceps Brachii and Brachioradialis muscles in low-level (15% MVC) prolonged isometric and dynamic contractions. The range of the joint angle was 70-110 degrees and the mean speed of flexion and extension was about 33.33 degrees /s (1.2 s for 40 degrees ). The use of Wavelet transform (IMNF) in weakly stationary dynamic SEMG signals was validated in comparison with the Fourier transform (MPF). The development of fatigue and its myoelectrical manifestations (increase in RMS and decrease in mean frequency) in dynamic contractions show no deviations from what is found in literature for both spectral estimation techniques. The benefit of Wavelets is its future use in non-stationary conditions. Lower IMNF slopes in dynamic compared to isometric contractions for Biceps Brachii might be an indication that wavelets reflect more the changes in muscle fiber propagation velocity. The results of the use of Wavelet transform in detecting frequency modulations in different movement phases of the dynamic tests show that in the eccentric phase a systematic shift towards lower frequencies occurs. It also reveals the great possibilities of phase separation using Wavelets with high resolution and low interaction.

Effect of wavelength range on the measurement accuracy of some selected soil constituents using visual-near infrared spectroscopy

The wavelength range is one of the main components affecting the measurement accuracy with visible (vis) and near infrared (NIR) spectroscopy. The performance of two commercially-available spectrophotometers with different wavelength ranges for measurement of selected soil attributes was evaluated. The two spectrophotometers considered were a diode array with a short wavelength range (SWR) of 300–1700 nm and a combination of diode array and scanning monochromator with a full wavelength range (FWR) of 350–2500 nm. Scanning was performed on wet-fresh (in situ conditions) and dry-processed (laboratory conditions) soil samples, to estimate the effect of moisture content on the performance of the two spectrophotometers. Partial least squares (PLS) regression with the leave-one-out cross validation technique was used to develop calibration models relating soil spectra with chemical attributes. Results showed that under wet field soil conditions pH, available phosphorus (P), cation exchange capacity (CEC), potassium (K) and calcium (Ca) were more accurately predicted with the SWR, whereas total nitrogen (N), total carbon (C), sodium (Na) and magnesium (Mg) were better predicted with the FWR. The significant effect of the water absorption at a wavelength of 1950 nm which created considerable changes in the shape of spectra and subsequently loss of important information available at wavelengths greater than 2000 nm, reduced the efficiency of the FWR instrument. The dry soil conditions led to a superior performance of the FWR instrument over the SWR instrument for measurement of all soil attributes. This suggests that a greater wavelength range than 1700 nm is not useful for improving the measurement accuracy of soil attributes (except N and C) with vis-NIR spectroscopy when measurement is to be carried out in the field under wet conditions.

Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm

As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations.

Determination of the dynamical behaviour of biological materials during impact using a pendulum device

Abstract A pendulum device has been developed to measure contact force, displacement and displacement rate of an impactor during its impact on the sample. Displacement, classically measured by double integration of an accelerometer, was determined in an alternative way using a more accurate incremental optical encoder. The parameters of the Kuwabara–Kono contact force model for impact of spheres have been estimated using an optimization method, taking the experimentally measured displacement, displacement rate and contact force into account. The accuracy of the method was verified using a rubber ball. Contact force parameters for the Kuwabara–Kono model have been estimated with success for three biological materials, i.e., apples, tomatoes and potatoes. The variability in the parameter estimations for the biological materials was quite high and can be explained by geometric differences (radius of curvature) and by biological variation of mechanical tissue properties.

Application of Visible and Near-Infrared Reflectance Spectroscopy (Vis/NIRS) to Determine Carotenoid Contents in Banana (Musa spp.) Fruit Pulp

The analysis of carotenoids is complicated by the tendency of these compounds to react with radical species, leading to oxidative breakdown and isomerization during extraction. Therefore, protocols should be rapid and avoid unnecessary exposure to heat, acids, and so forth. Here, we evaluate the use of visible and near infrared reflectance spectroscopy (Vis/NIRS) to measure carotenoid contents in fruit from 28 Musa (banana and plantain) varieties. Carotenoid contents were first quantified using standardized RP-HPLC protocols, and these results were then used to develop algorithms to predict carotenoid contents from Vis/NIR spectra of the same samples. Cross-validation of the predictive algorithms across a genetically diverse group of varieties demonstrated that correlation coefficients between the HPLC measurements and the Vis/NIRS predictions varied from good for the total carotenoids and beta-carotene fractions (r(2)(cv), 0.84, 0.89) to reasonable for alpha-carotene and cis-carotenes (r(2)(cv), 0.61, 0.66), but there was only a poor correlation (r(2)(cv), 0.30) for the minor lutein component. Nonetheless, since approximately 90% of the Musa carotenoids consist of only alpha- and beta-carotene, results indicate that Vis/NIRS can be used for the high-throughput screening of fruit pulp samples for vitamin A nutritional content on the basis of their total carotenoids content.