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+# Flower State Classification for Watering System
+
+This project aims to develop a machine learning model to detect plants
+and classify them as water-stressed or not.
+
+## Overview of the Approach
+
+The machine learning pipeline is divided into two stages: detection
+and classification. Images are first passed to the object detection
+model which draws bounding boxes where it thinks plants are. These
+bounding boxes (image coordinates) allow us to get a cutout of the
+image and pass said cutout to the classifier.
+
+The classifier takes the cutout and predicts one of two classes:
+*healthy* or *stressed*. The confidence (probability) of the
+classifier for either class is seen as a proxy for the degree of
+stress. A plant which has been classified as 70% stressed, for
+example, will get a rating of 7/10 regarding its stress level, where
+10 is the most severe stress. Conversely, a 60% healthy plant is put
+into the 4/10 stress category.
+
+### Plant Detection
+
+To detect plants, we chose the popular single stage detector
+YOLO. There are multiple versions available, with the most recent one
+YOLOv8 available since 2023. At the start of the project YOLOv8 did
+not exist yet, which is why the pipeline is built on YOLOv7.
+
+### Classification
+
+The classifier is a ResNet50 model which has been chosen again due to
+its popularity and proven track record. It has been trained on a
+dataset of only 452 healthy and 452 stressed plants, but works
+surprisingly well nevertheless.
+
+## Installing the Dependencies
+
+In order to run the model(s), a couple of dependencies have to be
+installed. These are listed in the `classification/requirements.txt`
+file. The code is tested under python versions 3.6 and 3.7 and might
+not work for other versions.
+
+```bash
+python3.7 -m pip install -r classification/requirements.txt
+```
+
+Afterwards, it is necessary to either add the `classification`
+directory to the `PYTHON_PATH` environment variable or to install the
+package locally.
+
+```bash
+# From the classification folder
+python3.7 -m pip install -e .
+```
+
+## Running the Model(s)
+
+The models can be run by calling the function `detect()` in
+`evaluation/detect.py`. This will return a pandas dataframe with the
+coordinates of the bounding boxes and their confidence values. This
+dataframe together with the original image can be passed to the
+`draw_boxes()` function in `utils/manipulations.py`. A new image with
+the bounding boxes drawn on the original image can then be either
+shown to the user via OpenCV or saved to disk.
+
+## Model Evaluation
+
+The `evaluation` directory contains multiple Jupyter notebooks which
+evaluate the models separately as well as together. These notebooks
+contain explanations of the code and what the results are in quasi
+[literate
+programming](https://en.wikipedia.org/wiki/Literate_programming)
+style.
+
+## Deploying Model to Jetson Nano
+
+The folder `jetson-deployment` contains a script to periodically run
+detections and expose the results over a simple API. The python
+bindings for OpenCV are somehow very difficult to install, primarily
+because NVidia does not provide regular updates to the Python and
+Ubuntu distributions. Therefore, Image capture is not implemented in
+Python but in C++. The binary has to be compiled with the provided
+`Makefile`.