Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance

3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds

3D crop reconstruction with a high temporal resolution and by the use of non-destructive measuring technologies can support the automation of plant phenotyping processes. Thereby, the availability of such 3D data can give valuable information about the plant development and the interaction of the plant genotype with the environment. This article presents a new methodology for georeferenced 3D reconstruction of maize plant structure. For this purpose a total station, an IMU, and several 2D LiDARs with different orientations were mounted on an autonomous vehicle. By the multistep methodology presented, based on the application of the ICP algorithm for point cloud fusion, it was possible to perform the georeferenced point clouds overlapping. The overlapping point cloud algorithm showed that the aerial points (corresponding mainly to plant parts) were reduced to 1.5%–9% of the total registered data. The remaining were redundant or ground points. Through the inclusion of different LiDAR point of views of the scene, a more realistic representation of the surrounding is obtained by the incorporation of new useful information but also of noise. The use of georeferenced 3D maize plant reconstruction at different growth stages, combined with the total station accuracy could be highly useful when performing precision agriculture at the crop plant level

Visualization of Tomato Growth Based on Dry Matter Flow

The visualization of tomato growth can be used in 3D computer games and virtual gardens. Based on the growth theory involving the respiration theory, the photosynthesis, and dry matter partition, a visual system is developed. The tomato growth visual simulation system is light-and-temperature-dependent and shows plausible visual effects in consideration of the continuous growth, texture map, gravity influence, and collision detection. In addition, the virtual tomato plant information, such as the plant height, leaf area index, fruit weight, and dry matter, can be updated and output in real time

Improved image recognition via Synthetic Plants using 3D Modelling with Stochastic Variations

This research extends previous plant modelling using L-systems by means of a novel arrangement comprising synthetic plants and a refined global wheat dataset in combination with a synthetic inference application. The study demonstrates an application with direct recognition of real plant stereotypes, and augmentation via a plant-wide stochastic growth variation structure. The study showed that the automatic annotation and counting of wheat heads using the Global Wheat dataset images provides a time and cost saving over traditional manual approaches and neural networks. This study introduces a novel synthetic inference application using a plant-wide stochastic variation system, resulting in improved structural dataset hierarchy. The research demonstrates a significantly improved L-system that can more effectively and more accurately define and distinguish wheat crop characteristics

Smart greenhouse and plant growth control

Since the development of agriculture is an important problem for every state, huge funds are allocated to this industry. However, the problem of lack of fresh fruits/vegetables, that is, the problem of import substitution remains a pressing issue in many countries. The aim of the study was to inspect the growth of plants in a home-based mini-greenhouse, for which reason the following tasks were set: conduct a biological experiment; search for dependence of the influence of environmental conditions (microclimate) on growth. The paper highlights the problem of import substitution of vegetables in Kazakhstan, and suggests the best way to solve this issue. The proposed solution offers the development of mini-greenhouse that meets the criteria of price and quality. The developed system differs from other smart greenhouses, firstly, by its availability to a wide range of users (price criterion), and secondly, by ensuring agrotechnical, energy, and design requirements (quality criterion). These requirements are implemented through the use of promising technologies: phytomonitoring, intelligent technologies and open source software, the use of available construction materials and water saving technologies such as drip irrigation. The economic effect from the use of the proposed technology has amounted to 10,000 tenge, the payback period was 4 seasons

A Low-cost Depth Imaging Mobile Platform for Canola Phenotyping

To meet the high demand for supporting and accelerating progress in the breeding of novel traits, plant scientists and breeders have to measure a large number of plants and their characteristics accurately. A variety of imaging methodologies are being deployed to acquire data for quantitative studies of complex traits. When applied to a large number of plants such as canola plants, however, a complete three-dimensional (3D) model is time-consuming and expensive for high-throughput phenotyping with an enormous amount of data. In some contexts, a full rebuild of entire plants may not be necessary. In recent years, many 3D plan phenotyping techniques with high cost and large-scale facilities have been introduced to extract plant phenotypic traits, but these applications may be affected by limited research budgets and cross environments. This thesis proposed a low-cost depth and high-throughput phenotyping mobile platform to measure canola plant traits in cross environments. Methods included detecting and counting canola branches and seedpods, monitoring canola growth stages, and fusing color images to improve images resolution and achieve higher accuracy. Canola plant traits were examined in both controlled environment and field scenarios. These methodologies were enhanced by different imaging techniques. Results revealed that this phenotyping mobile platform can be used to investigate canola plant traits in cross environments with high accuracy. The results also show that algorithms for counting canola branches and seedpods enable crop researchers to analyze the relationship between canola genotypes and phenotypes and estimate crop yields. In addition to counting algorithms, fusing techniques can be helpful for plant breeders with more comfortable access plant characteristics by improving the definition and resolution of color images. These findings add value to the automation, low-cost depth and high-throughput phenotyping for canola plants. These findings also contribute a novel multi-focus image fusion that exhibits a competitive performance with outperforms some other state-of-the-art methods based on the visual saliency maps and gradient domain fast guided filter. This proposed platform and counting algorithms can be applied to not only canola plants but also other closely related species. The proposed fusing technique can be extended to other fields, such as remote sensing and medical image fusion

Strategies To Reconstruct 3d Coffea Arabica L. Plant Structure

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Accurate model of structural elements is necessary to model the foliage and fruit distributions in cultivated plants, both of them being key parameters for yield prediction. However, the level of details in architectural data collection could vary, simplifying the data collection when plants get older and because of the high time cost required. In the present study, we aimed at reconstructing and analyzing plant structure, berry distributions and yield in Coffea arabica (Arabica coffee), by using both detailed or partial morphological information and probabilistic functions. Different datasets of coffee plant architectures were available with different levels of detail depending on the tree age. Three scales of decomposition-plant, axes and metamers were used reconstruct the plant architectures. CoffePlant3D, a software which integrates a series of mathematical, computational and statistical methods organized in three newly developed modules, AmostraCafe3D, VirtualCafe3D and Cafe3D, was developed to accurately reconstruct coffee plants in 3D, whatever the level of details available. The number of metamers of the 2nd order axes was shown to be linearly proportional to that of the orthotropic trunk, and the number of berries per metamer was modeled as a Gaussian function within a specific zone along the plagiotropic axes. This ratio of metamer emission rhythm between the orthotropic trunk and plagiotropic axes represents the pillar of botanical events in the C. arabica development and was central in our modeling approach, especially to reconstruct missing data. The methodology proposed for reconstructing coffee plants under the CoffePlant3D was satisfactorily validated across dataset available and could be performed for any other Arabica coffee variety.5Brazilian Consorcio Pesquisa Cafe [02.09.20.008.00.03, 02.13.02.042.00.03]CNPq [381241/2014-9]Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings