Toward a tool aimed to quantify soil compaction risks at a regional scale: application to Wallonia (Belgium)

The spatial analysis of the soil compaction risk has been developed at the regional level and applied to Wallonia (Belgium). The methodology is based on the estimation of the probability of exceeding the preconsolidation stress due to the application of loads on the soil. Preconsolidation stresses (Pc) are computed from the pedotransfer functions of Horn and Fleige (2003) at pF 1.8 and 2.5 and classified into 6 categories ranging from very low Pc ( 150 kPa). The computation requires the knowledge of pedological (texture, organic content), mechanical (bulk density, cohesion, internal friction angle), and hydraulic variables (water content available, non-available water content, air capacity, saturated hydraulic conductivity). These variables are obtained from databases like HYPRES or AARDEWERK or from pedotransfer functions. The computation of Pc takes into account the spatial structure of the data: in some cases, data are abundant (e.g. texture data) and spatial variability is taken into account through geostatistical methods. In other cases, the data is sparse but uncertainty information can be extracted from the knowledge of the statistical distribution. Maps of the most probable Pc class are produced. Uncertainty is computed as the classification error probability. Implementation of these methods in Wallonia showed that Pc values higher than 120 kPa are reached either on 64 % of the territory at pF 2.5 or on 55 % at pF 1.8. A higher uncertainty was found at pF 2.5 than at pF 1.8. Uncertainty was also found higher for clay and clayed loess than for other textural classes present in Wallonia. The risk of compaction is defined as the probability that Pc is exceeded by the stress created by a load applied to the soil at a depth of 40 cm, the loads being similar to those induced by agricultural or forestry tires. It appeared that subsoil compaction risks exist mainly in loamy forest soils with small coarse fragments supporting loads similar to that existing on logging machines. In the zones where the uncertainty is low, the developed tool could be used as a basis for providing policy measures in order to promote soil-friendly farming and forest practices.Etude de la compaction des sols de Walloni

Evaluation of chicory seeds maturity by chlorophyll fluorescence imaging

Chicory (Cichorium intybus L.) seed production includes sorting to remove foreign materials and non-viable seeds. A machine vision system was developed to monitor the fluorescence in order to detect the immature chicory seeds. It comprised a monochromatic light source, a highpass filter and a monochromatic CCD camera sensitive to red and infrared. With this device, blue light reflected by the seeds was blocked whilst red fluorescence was measured by the camera. A segmentation algorithm was designed to estimate separately the fluorescence intensities of the pappus, a crown of scales, and the main body of the pericarp. Experiments were carried out on five clones of cross-pollinated chicory plants used for seed production. Two hundred flower heads were labelled at flowering and harvested at different times during the maturation process expressed in “days after flowering” (DAF). Germination tests were performed according to the recommendations of the International Seed Testing Association to measure the germination percentage (GP) and the germination rate (GR), an indicator of seed vigour. Seed chlorophyll content diminished during maturation following a different logistic trend for the pappus and the pericarp. The GP increased from 18 DAF to reach its maximum value at 21 DAF, but the GR remained low until 30 DAF and increased afterwards. The potential of chlorophyll fluorescence to be used as an indicator of chicory seed vigour was the greatest between 21 and 36 DAF

The quality of 'Golden delicious' apples by colour computer vision

peer reviewedA colour machine vision system was developed to form a basis for colour grading and defect inspection of 'Golden delicious' apples. The criteria were based on European Union standards and took into account commercial practices which add subclasses to the basic categories. The system was able to grade correctly more than 90% of the apple for colour (94% by using three colorimetric parameter R, G, B or H,S,I and 91% by using the single canonical variate) and ensured good defect detection (russet, scab, fungi attack, tec.)

Quality fruit grading by colour machine vision: defect recognition.

peer reviewe

Development of an Optical Sensor to Measure Direct Injection Spraying System Performance

Evaluation of direct injection sprayer’s performance is an important step for a successful direct injection sprayer technology development. A low-cost optical sensor was developed to characterize direct injection system response by dynamic measurement of fluorescent dye concentration. The method is based on sensing the fluorescence of mixture by the light-to-voltage converter equipped with integral optical green filter TSLG257. The dye is excited by one blue light LED HLMP-CB15 (emission band of 472 nm ± 32). The light transmittance was measured by the converter in two on-line positions to LED; the emitter and transmitter placed longitudinally at 45° angle and transversally at 90° angle to flow line. The measurement of transmittance for concentrations between 0 to 10 mg/l showed that the trend is linear for concentrations under 2.5 mg/l (R2 > 99%).The results showed that the offset for longitudinal measurements is bigger than for the transversal ones (about 600%) because of the direct interception of the light by the converter. The highest sensitivity is related to the transversal 90° position transmittance. The amplification of the excitation power of the LED by varying current supply between 50% and 100% gave a proportional increase of the sensitivity without affecting the linearity. Test results of sensor showed that it can be used to calibrate direct injection system accurately and to characterize the performance of the system for upstream and downstream injection location

Assessing nitrogen fertilisation strategies according to climate variability : A modelling approach

/Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de micro-capteurs en vue d’optimiser l’apport en engrais azoté

Automation in agriculture and in food industry. An example : fruit grading according to their external characteristics

Séminaire de vulgarisation sur l'utilisation des techniques d'analyse d'image pour l'évaluation de la forme de fruit et pour la recherche de défauts

Assessing the potential of an algorithm based on mean climatic data to predict wheat yield

The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de micro-capteurs en vue d’optimiser l’apport en engrais azoté

Yield variability linked to climate uncertainty and nitrogen fertilisation

peer reviewedAt the parcel scale, crop models such as STICS are powerful tools to study the effects of variable inputs such as management practices (e.g. nitrogen (N) fertilisation). In combination with a weather generator, we built up a general methodology that allows studying the yield variability linked to climate uncertainty, in order to assess the best N practice. Our study highlighted that, applying the Belgian farmer current N practice (60-60-60 kg N/ha), the yield distribution was found to be very asymmetric with a skewness of -1.02 and a difference of 5% between the mean (10.5 t/ha) and the median (11.05 t/ha) of the distribution. This implies that, under such practice, the probability for farmers to achieve decent yields, in comparison to the mean of the distribution, was the highest.Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de micro-capteurs en vue d’optimiser l’apport en engrais azoté