ULTRASONIC WAVES THROUGH AGRICULTURAL SOILS TO DETERMINE THEIR COMPACTION AND POROSITY LEVEL

Compaction is one of the major causes of the physical degradation of agricultural soils. The traffic of more and more heavy machines leads to a decrease of the porosity at both the topsoil and subsoil levels. This has negative impacts in agricultural and environmental contexts such as the reduction of soil fertility and water infiltration. This project aims at characterizing in a fast and non-destructive way the state of compaction of an agricultural soil at a local scale using ultrasonic wave propagation. Acoustic signatures of soil samples will be correlated to their compaction level and their porosity distribution. This should allow a better comprehension of the compaction process and help to define critical threshold. As a result, this methodology could assist in taking restrictive measures such as load limitation of agricultural engines and implementing remedial methods. This poster presents the experimental protocol implement for this research

Multi-scale modelling of shell failure for periodic quasi-brittle materials

In a context of restoration of historical masonry structures, it is crucial to properly estimate the residual strength and the potential structural failure modes in order to assess the safety of buildings. Due to its mesostructure and the quasi-brittle nature of its constituents, masonry presents preferential damage orientations, strongly localised failure modes and damage-induced anisotropy, which are complex to incorporate in structural computations. Furthermore, masonry structures are generally subjected to complex loading processes including both in-plane and out- of-plane loads which considerably influence the potential failure mechanisms. As a consequence, both the membrane and the flexural behaviours of masonry walls have to be taken into account for a proper estimation of the structural stability. Macrosopic models used in structural computations are based on phenomenological laws including a set of parameters which characterises the average behaviour of the material. These parameters need to be identified through experimental tests, which can become costly due to the complexity of the behaviour particularly when cracks appear. The existing macroscopic models are consequently restricted to particular assumptions. Other models based on a detailed mesoscopic description are used to estimate the strength of masonry and its behaviour with failure. This is motivated by the fact that the behaviour of each constituent is a priori easier to identify than the global structural response. These mesoscopic models can however rapidly become unaffordable in terms of computational cost for the case of large-scale three-dimensional structures. In order to keep the accuracy of the mesoscopic modelling with a more affordable computa- tional effort for large-scale structures, a multi-scale framework using computational homogeni- sation is developed to extract the macroscopic constitutive material response from computa- tions performed on a sample of the mesostructure, thereby allowing to bridge the gap between macroscopic and mesoscopic representations. Coarse graining methodologies for the failure of quasi-brittle heterogeneous materials have started to emerge for in-plane problems but remain largely unexplored for shell descriptions. The purpose of this study is to propose a new periodic homogenisation-based multi-scale approach for quasi-brittle thin shell failure. For the numerical treatment of damage localisation at the structural scale, an embedded strong discontinuity approach is used to represent the collective behaviour of fine-scale cracks using average cohesive zones including mixed cracking modes and presenting evolving orientation related to fine-scale damage evolutions. A first originality of this research work is the definition and analysis of a criterion based on the homogenisation of a fine-scale modelling to detect localisation in a shell description and determine its evolving orientation. Secondly, an enhanced continuous-discontinuous scale tran- sition incorporating strong embedded discontinuities driven by the damaging mesostructure is proposed for the case of in-plane loaded structures. Finally, this continuous-discontinuous ho- mogenisation scheme is extended to a shell description in order to model the localised behaviour of out-of-plane loaded structures. These multi-scale approaches for failure are applied on typical masonry wall tests and verified against three-dimensional full fine-scale computations in which all the bricks and the joints are discretised

Robot weed killers - no pain more gain

Weed destruction plays a significant role in crop production, and its automation has both economic and environmental benefits by minimizing the usage of chemicals in the fields. Our aim is to design a small low-cost versatile robot allowing the destruction of weeds that lie between the crop rows by navigating in the field autonomously. Major challenges foreseen are: mapping the unknown geometry of the field, high-level planning of efficient and complete coverage of the field, and controlling the low-level operations of the robot. Traditionally, sensors like odometer have been used for localisation of robots but without much success in real-world scenarios. Specialized sensors like cameras will therefore be investigated and the plethora of image recognition algorithms will be explored and fine-tuned to enable Simultaneous Localisation And Mapping (SLAM) even on resource constrained robotic platforms. Vision-based localisation is not always viable because of the varying weather conditions of the environment and to overcome that, intelligent stochastic data fusion and machine learning algorithms will be utilized to combine data from heterogenous sensor. The image sensors for localisation will be re-used to differentiate crop rows from the weeds, which are cut when they grow. Finally, logics and reinforcement learning techniques will be explored, to exploit the generated map of the field and other sensorial information, to efficiently plan and execute weed elimination

Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing.

peer reviewedEstimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping. In this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multispectral images taken from tillering to maturity was examined. To overcome the scarcity of labeled data, a training pipeline was devised. This pipeline involves transfer learning, pseudo-labeling of unlabeled data and temporal relationship correction. The results demonstrated that CNN models significantly benefit from the pseudolabeling method, while the machine learning approach employing a PLSr did not show comparable performance. Among the models evaluated, EfficientNetB4 achieved the highest accuracy for predicting above-ground biomass, with an R² value of 0.92. In contrast, Resnet50 demonstrated superior performance in predicting LAI, nitrogen uptake, and nitrogen concentration, with R² values of 0.82, 0.73, and 0.80, respectively. Moreover, the study explored multi-output models to predict the distribution of dry matter and nitrogen uptake between stem, inferior leaves, flag leaf, and ear. The findings indicate that CNNs hold promise as accessible and promising tools for phenotyping quantitative biophysical variables of crops. However, further research is required to harness their full potential

Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification

The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different organs. Recent deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of conditions. However, they remain complicated to implement and necessitate a huge training database. This paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage. The tested method was based on superpixel classification exploiting features from RGB and multispectral cameras. Three classifiers were trained with wheat images acquired from heading to maturity on two cultivars at different levels of fertilizer. The best classifier, the support vector machine (SVM), yielded satisfactory segmentation and reached 94% accuracy. However, the segmentation at the pixel level could not be assessed only by the superpixel classification accuracy. For this reason, a second assessment method was proposed to consider the entire process. A simple graphical tool was developed to annotate pixels. The strategy was to annotate a few pixels per image to be able to quickly annotate the entire image set, and thus account for very diverse conditions. Results showed a lesser segmentation score (F1-score) for the heading and flowering stages and for the zero nitrogen input object. The methodology appeared appropriate for further work on the growth dynamics of the different wheat organs and in the frame of other segmentation challenges

A First Insight on the Interaction between Desiccation Cracking and Water Transfer in a Luvisol of Belgium

peer reviewedThe present paper presents the interactions between water retention/evaporation and cracking during the desiccation of intact and disturbed Belgian Luvisol. The disturbed (DS) and undisturbed (NDS) samples (reduced-tillage-residue-in (RTRI) and conventional-tillage-residue-out (CTRO)) were collected from an agricultural field in Gembloux, Wallonia, Belgium. The drying experiment took place in controlled laboratory conditions at 25 °C. Moisture content, soil suction and surface cracks were monitored with a precision balance, a tensiometer and a digital camera, respectively. The image processing and analysis were performed using PCAS® and ImageJ® software. The results showed that crack formation was initiated at a stronger negative suction and a lower water content (Wc) in DS > CTRO > RTRI. The suction and the crack propagation were positively correlated until 300 kPa for the DS and far beyond the wilting point for the NDS. For the NDS, the cracking accelerated after reaching the critical water content (~20% Wc) which arrived at the end of the plateau of evaporation (40 h after crack initiation). The Krischer curve revealed that the soil pore size > 50 µm, and that it is likely that cracks are important parameters for soil permeability. The soil structure and soil fibre content could influence the crack formation dynamic during drying. The agricultural tillage management also influences the crack propagation. As retention and conductivity functions are affected by cracks, it is likely that the movement of fluids in the soil will also be affected by the cracks following a desiccation period (i.e., when the cracked soil is rewetted)

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

peer reviewedMathematical models have been widely employed for the simulation of growth dynamics of annual crops, thereby performing yield prediction, but not for fruit tree species such as jujube tree (Zizyphus jujuba). The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter. The model was established using data collected from dedicated field experiments performed in 2016–2018. Simulated growth dynamics of dry weights of leaves, stems, fruits, total biomass and leaf area index (LAI) agreed well with measured values, showing root mean square error (RMSE) values of 0.143, 0.333, 0.366, 0.624 t ha−1 and 0.19, and R2 values of 0.947, 0.976, 0.985, 0.986 and 0.95, respectively. Simulated phenological development stages for emergence, anthesis and maturity were 2, 3 and 3 days earlier than the observed values, respectively. In addition, in order to predict the yields of trees with different ages, the weight of new organs (initial buds and roots) in each growing season was introduced as the initial total dry weight (TDWI), which was calculated as averaged, fitted and optimized values of trees with the same age. The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI. The modelling performance was significantly improved when it considered TDWI integrated with tree age, showing good global (R2≥0.856, RMSE≤0.68 t ha−1) and local accuracies (mean R2≥0.43, RMSE≤0.70 t ha−1). Furthermore, the optimized TDWI exhibited the highest precision, with globally validated R2 of 0.891 and RMSE of 0.591 t ha−1, and local mean R2 of 0.57 and RMSE of 0.66 t ha−1, respectively. The proposed model was not only verified with the confidence to accurately predict yields of jujube, but it can also provide a fundamental strategy for simulating the growth of other fruit trees

Improving Wheat Yield Prediction Accuracy Using LSTM‐RF Framework Based on UAV Thermal Infrared and Multispectral Imagery

peer reviewedYield prediction is of great significance in agricultural production. Remote sensing technology based on unmanned aerial vehicles (UAVs) offers the capacity of non‐intrusive crop yield prediction with low cost and high throughput. In this study, a winter wheat field experiment with three levels of irrigation (T1 = 240 mm, T2 = 190 mm, T3 = 145 mm) was conducted in Henan province. Multispectral vegetation indices (VIs) and canopy water stress indices (CWSI) were obtained using an UAV equipped with multispectral and thermal infrared cameras. A framework combining a long short‐term memory neural network and random forest (LSTM‐RF) was proposed for predicting wheat yield using VIs and CWSI from multi‐growth stages as predictors. Validation results showed that the R2 of 0.61 and the RMSE value of 878.98 kg/ha was achieved in predicting grain yield using LSTM. LSTM‐RF model obtained better prediction results compared to the LSTM with n R2 of 0.78 and RMSE of 684.1 kg/ha, which is equivalent to a 22% reduction in RMSE. The results showed that LSTM‐RF considered both the time‐series characteristics of the winter wheat growth process and the non‐linear characteristics between remote sensing data and crop yield data, providing an alternative for accurate yield prediction in modern agricultural management

Global Wheat Head Detection 2021: an improved dataset for benchmarking wheat head detection methods

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version