| Titre : | Smart Agriculture | | Type de document : | projet fin études | | Auteurs : | Oumaima EL GADI, Auteur | | Langues : | Français (fre) | | Catégories : | BIG DATA
| | Mots-clés : | Irrigation management system, Evolution detection, Neural network, Internet of
Things | | Index. décimale : | mast 267/19 | | Résumé : | This document describes the proposed system for remote control of the plant, i.e.
automating irrigation and taking the decision if the plant is suitable for the climate
or if its position needs to be changed.
Our system estimates the irrigation needs of a plantation, based on soil measurements
and climate variables collected by sensors connected with the Raspberry.
To estimate the plant’s needs, we propose to use a deep learning technique, more
precisely the Multilayer Perceptron (MLP). For prediction the algorithm is implemented,
the prediction function works on any database. By detecting if the plant
needs water, we start the pump and we will record this event.
The monitoring of the plant is synchronized periodically, we pray in consideration
of the autumn season. We used the R-CNN Mask for object detection and instance
segmentation to delimit the plant in the image and then we use the histogram
to calculate its size. The calculation is applied to the current image of the plant and
the previous one, then we compare the results if the plant evolves we display that
no need to change its environment. We have also implemented a function as a complement
to this one, which aims to detect if the fruit is ripe or not. This function is
dedicated to red fruits.
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Smart Agriculture [projet fin études] / Oumaima EL GADI, Auteur . - [s.d.]. Langues : Français ( fre) | Catégories : | BIG DATA
| | Mots-clés : | Irrigation management system, Evolution detection, Neural network, Internet of
Things | | Index. décimale : | mast 267/19 | | Résumé : | This document describes the proposed system for remote control of the plant, i.e.
automating irrigation and taking the decision if the plant is suitable for the climate
or if its position needs to be changed.
Our system estimates the irrigation needs of a plantation, based on soil measurements
and climate variables collected by sensors connected with the Raspberry.
To estimate the plant’s needs, we propose to use a deep learning technique, more
precisely the Multilayer Perceptron (MLP). For prediction the algorithm is implemented,
the prediction function works on any database. By detecting if the plant
needs water, we start the pump and we will record this event.
The monitoring of the plant is synchronized periodically, we pray in consideration
of the autumn season. We used the R-CNN Mask for object detection and instance
segmentation to delimit the plant in the image and then we use the histogram
to calculate its size. The calculation is applied to the current image of the plant and
the previous one, then we compare the results if the plant evolves we display that
no need to change its environment. We have also implemented a function as a complement
to this one, which aims to detect if the fruit is ripe or not. This function is
dedicated to red fruits.
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