DEVELOPMENT OF A FIELD-BASED MOBILE PLATFORM FOR PLANT PHENOTYPING
Notice bibliographique
Résumé
Design, implementation and performance verification of an affordable field-based high-throughput plant phenotyping platform for monitoring Canola plants, including both data acquisition/visualization software and measurement system, was the main objective of this research. The primary motivation for this research is the fact that breeders need a well-organized approach and efficient tool to monitor and analyze a number of plant traits to achieve a higher yield. At the moment, manual measurement is a conventional approach to gather the required information for plant analysis. Nevertheless, manual measurement has many limitations especially to study a large-scale field. To address this bottleneck, a high-throughput plant phenotyping platform (HTPP) was developed which consists of a data acquisition system, a data storage unit, and a data visualization and analysis software. Such an HTPP will be an essential asset for breeders to conveniently gather a comprehensive database which contains various information such as a plant height, temperature, Normalized Difference Vegetation Index (NDVI), etc. To develop and implement such an HTPP, first, the overall system block diagram and required algorithms were drawn. Then to find the optimum set of equipment according to the requirement of this application, the performance of different sensors and devices were examined using literature search and experimental examinations in the laboratory setting. Then a mechanical boom was attached to the rear of a farm vehicle (a Swather) to carry different sensors, cameras and other measurement equipment (mechanical development of the boom structure was carried out by other members of the research team). A control box containing power supplies, safety fuses, and a data logger unit was attached to the farm vehicle, and a program was developed for data logger to read sensors signals as well as GPS data for data geo-referencing and future retrieval purposes. The efficiency of different system architecture including different data transmission networks was examined by conducting several field tests to minimize existing errors such as delays in synchronizing different steps. Three programs were developed in MATLAB GUI for image acquisition via webcam and DSLR cameras as well as a central program for data processing and interactive data visualization. The indoor tests were performed at the Robotics laboratory, University of Saskatchewan and outdoor experiments were performed on a Canola nursery at Cargill Canada, Aberdeen, SK, throughout spring-summer 2016 and 2017. Finally, the performance and effectiveness of the developed field-based phenotyping platform was validated by various measures such as conducting some manual measurements and comparing the results with the values given by the platform. According to the achieved results, both hardware and software components of the proposed system meet the requirements of a field-based plant phenotyping platform as an essential asset for breeders for comprehensive study of Canola plants or any other cultivars as a result of some minor design modifications. The main contributions of this study to plant phenotyping research are autonomous image acquisition capability, enhancement of the data acquisition cycle to minimize data geo-referencing error, development of a modular program for data visualization in MATLAB, and faster data collection in a high-throughput fashion (almost 125 times faster).
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».