Development of a smart variable rate sprayer using deep convolutional neural networks for site-specific application of agrochemicals
Notice bibliographique
Résumé
Potato production in Canada typically involves approximately 20 uniform applications (UA) of agrochemicals during a growing season, while usually ignoring spatial and temporal variations in the occurrence of weeds and diseased plants within potato fields. However, UA poses a serious threat to the environment and substantially increases the cost of crop production. Spatial distribution of weeds and diseased plant patches within potato fields emphasizes the need to develop a smart variable rate sprayer (SVRS). Innovations in development of precision agriculture technologies have enabled Engineers\nto develop SVRS using machine vision (MV) and deep learning (DL) to accurately identify and encounter the targets (weeds and diseased plants) in real-time for within-fields variable rate application (VA) of herbicides and fungicides. Five potato fields were selected to collect images of spatially and temporally variable healthy potato plants, diseased potato plants, weeds and their combinations among them and with bare soil patches. The images were collected using a Canon PowerShot SX540 HS camera and Logitech C270 HD Webcam under varying natural light conditions and shadow effects. An image database was constructed by resizing, labeling, processing, and categorizing the above-mentioned images for real-time identification of weed, diseased and healthy plants using DL algorithms. Results of DL models showed > 80% accuracy in detecting targets. The tiny-YOLOv3 models were deployed and integrated into hardware to develop an innovative SVRS (cameras, nozzles, flowmeters, computer, valves and control system). Operational components of the sprayer were calibrated prior to testing in lab and potato fields. The results of lab and field testing revealed that the SVRS was accurate in detecting weeds and diseased plants in real-time and applied agrochemicals on an as-needed basis. Experiments were designed under two-factor factorial arrangements with two treatments (UA and VA) and three levels of weather conditions (cloudy, partly cloudy, and sunny) in a 2x3 factorial design. The spraying techniques and weather conditions were the two independent variables and/or factors of interest with the spray volume consumption as a response variable. A two-way ANOVA test indicated a non-significant effect of the levels of factors of interest on volume consumption of spraying liquid under different weather conditions (cloudy, partly cloudy and sunny); e.g., the spraying application techniques (VA and UA) for both lab and field evaluations had p-values respectively 0.329 and 0.156 for lab testing and 0.968 and 0.751 for field testing during weeds and diseased plant detection experiments. However, there was a significant effect of spraying application techniques on volume consumption (p-value < 0.01). The SVRS was able to save 47 and 51% of agrochemicals for weeds and diseased plant detection experiments, respectively, under all weather conditions. Results indicated that the SVRS was capable of significantly reducing the use of agrochemicals, when compared with UA, both in lab and field environments. The results of this study suggested that the developed SVRS has a great potential to reduce the use of agrichemicals, lower environmental risks, and ultimately improve farm profitability of potato producers.
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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 ».