Application of smart sensors to monitor the interactive effects of temperature and lighting on plant growth in a simulated controlled environment facility
Notice bibliographique
Résumé
This thesis explored the potential use of smart sensors to monitor and integrate multiple environmental factors that play crucial roles in the intricate dynamics of plant growth in response to varying environmental conditions, such as lighting and temperature in controlled environment crop production systems. \nA controlled environment chamber was designed and built to conduct experiments across a range of temperatures (15 - 35C) and under varying lighting duration (7, 10 and 14 h) and intensity (100 and 150 µmol/m2.s). A set of wireless smart sensors were used to monitor the environmental conditions, including air and soil temperatures, relative humidity, light intensity, and carbon dioxide level. The study demonstrated that the wireless smart sensors were effective in collecting reliable data for monitoring the environmental conditions, and sensor data could be fused to optimize the environmental conditions for plant growth. From the sensor data, it was found that both fresh biomass and dry biomass were significantly influenced by the three tested environmental factors. The highest biomass accumulation was observed at moderate temperatures (25-27C), with diminished growth at both lower and higher temperatures. Light duration and intensity were found to have a noticeable effect on biomass production, with longer lighting periods and higher intensity fostering greater fresh and dry biomass production. Similar effects of environmental conditions on leaf development were found: the best environmental condition was the moderate temperatures and long lighting duration and high light intensity. However, the benefits of increased lighting were modulated by temperature, indicating a complex interplay between these factors.\nThis study contributed valuable insights into the optimization of environmental parameters for plant cultivation in controlled environment systems through the use of smart sensors. The findings highlighted the importance of carefully balancing temperature and lighting conditions to maximize plant growth. The study demonstrated the successful use of an array of wireless smart sensors in monitoring multiple environmental parameters in controlled environment crop production and multiple sensor data could potentially be fused to optimize the environment in smart vertical farming (plant factories).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».