Darknet young hyrda вход

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After this is created, we get an. After Deploying this command, this activates the camera module deployed on the Raspberry Pi is activated and the inference on the module begins:. This is the timelapse video of a duration of 4 days reduced to 2 seconds. During actual inference of video input, this data is recorded in real time and accordingly real time notifications are updated. These notifications do not change quite frequently because the change in Video data is not a lot.

After I have successfully configured and generated the output video, detection of the video data wont be enough. In that case, I decided to send this video output data to a web-frontend dashboard for other Data-Visualization. The output generator is as follows:. Deploying unoptimised Tensorflow Lite model on Raspberry Pi:. Tensorflow Lite is an open-source framework created to run Tensorflow models on mobile devices, IoT devices, and embedded devices.

It optimizes the model so that it uses a very low amount of resources from your phone or edge devices like Raspberry Pi. Furthermore, on embedded systems with limited memory and compute, the Python frontend adds substantial overhead to the system and makes inference slow.

TensorFlow Lite provides faster execution and lower memory usage compared to vanilla TensorFlow. By default, Tensorflow Lite interprets a model once it is in a Flatbuffer file format. Before this can be done, we need to convert the darknet model to the Tensorflow supported Protobuf file format. I have already converted the file in the above conversion and the link to the pb file is: YOLOv3 file. To perform this conversion, you need to identify the name of the input, dimensions of the input, and the name of the output of the model.

This generates a file called yolov3-tiny. Then, create the "tflite1-env" virtual environment by issuing:. This will create a folder called tflite1-env inside the tflite1 directory. The tflite1-env folder will hold all the package libraries for this environment. Next, activate the environment by issuing:. You can tell when the environment is active by checking if tflite1-env appears before the path in your command prompt, as shown in the screenshot below.

Step 1c. OpenCV is not needed to run TensorFlow Lite, but the object detection scripts in this repository use it to grab images and draw detection results on them. Initiate a shell script that will automatically download and install all the packages and dependencies.

Run it by issuing:. Step 1d. Set up TensorFlow Lite detection model. Before running the command, make sure the tflite1-env environment is active by checking that tflite1-env appears in front of the command prompt. Getting Inferencing results and comparing them:. These are the inferencing results of deploying tensorflow and tflite to Raspberry Pi respectively. Even though the inferencing time in tflite model is less than tensorflow, it is comparitively high to be deployed.

While deploying the unoptimised model on Raspberry Pi, the CPU Temperature rises drastically and results in poor execution of the model:. Tensorflow Lite uses 15Mb of memory and this usage peaks to 45mb when the temperature of the CPU rises after performing continuous execution:. Power Consumption while performing inference: In order to reduce the impact of the operating system on the performance, the booting process of the RPi does not start needless processes and services that could cause the processor to waste power and clock cycles in other tasks.

Under these conditions, when idle, the system consumes around 1. This shows significant jump from 0. This increases the model performance by a significant amount which is nearly 12 times. This increment in FPS and model inferencing is useful when deploying the model on drones using hyperspectral Imaging.

Temperature Difference in 2 scenarios in deploying the model:. This image shows that the temperature of the core microprocessor rises to a tremendous extent. This is the prediction of the scenario while the model completed 21 seconds after being deployed on the Raspberry Pi.

After seconds of running the inference, the model crashed and the model had to be restarted again after 4mins of being idle. This image was taken after disconnecting power peripherals and NCS2 from the Raspberry Pi 6 seconds after inferencing. The model ran for about seconds without any interruption after which the peripherals were disconnected and the thermal image was taken. This shows that the OpenVino model performs way better than the unoptimised tensorflow lite model and runs smoother.

Its also observed that the accuracy of the model increases if the model runs smoothly. With this module, you can tell when your plants need watering by how moist the soil is in your pot, garden, or yard. The two probes on the sensor act as variable resistors. Use it in a home automated watering system, hook it up to IoT, or just use it to find out when your plant needs a little love.

Installing this sensor and its PCB will have you on your way to growing a green thumb! The soil moisture sensor consists of two probes which are used to measure the volumetric content of water. The two probes allow the current to pass through the soil and then it gets the resistance value to measure the moisture value. When there is more water, the soil will conduct more electricity which means that there will be less resistance.

Therefore, the moisture level will be higher. Dry soil conducts electricity poorly, so when there will be less water, then the soil will conduct less electricity which means that there will be more resistance. Therefore, the moisture level will be lower.

The sensor board itself has both analogue and digital outputs. The Analogue output gives a variable voltage reading that allows you to estimate the moisture content of the soil. The digital output gives you a simple "on" or "off" when the soil moisture content is above a certain threshold. The value can be set or calibrated using an adjustable on board potentiometer. In this case, we just want to know either "Yes, the plant has enough water" or "No, the plant needs watering!

With everything now wired up, we can turn on the Raspberry Pi. Without writing any code we can test to see our moisture sensor working. When power is applied you should see the power light illuminate with the 4 pins facing down, the power led is the one on the right. When the sensor detects moisture, a second led will illuminate with the 4 pins facing down, the moisture detected led is on the left.

Now we can see the sensor working, In this model, I want to monitor the moisture levels of the plant pot. So I set the detection point at a level so that if it drops below we get notified that our plant pot is too dry and needs watering. After the moisture sensor is set up to take readings and inference outputs, I will add a peristaltic pump using a relay to perform autonomous Plant Watering.

That way, when then moisture levels reduce just a small amount the detection led will go out. The way the digital output works is, when the sensor detects moisture, the output is LOW 0V. When the sensor can no longer detect moisture the output is HIGH 3. Water Sensor - plug the positive lead from the water sensor to pin 2, and the negative lead to pin 6. Plug the signal wire yellow to pin 8. Pump - Connect your pump to a power source, run the black ground wire between slots B and C of relay module 1 when the RPi sends a LOW signal of 0v to pin 1, this will close the circuit turning on the pump.

In the above code snippet, pump in has been set to pin7 and Soil Moisture Sensor pin has been set to pin8. Over here, a state of the soil moisture sensor has been set to Wet which is a variable continuously aggregating Sensor data. If the Sensor is not found to be wet and if the moisture is below the certain threshold set on the module, it activates the peristaltic pump to start watering the Apple Plant.

The state of the moisture sensor, If wet or not wet at a particular time is projected on a Streamlit front-end dashboard for Data Visualization. This Front-end data will be displayed in the further part of the project. DHT11 is a Digital Sensor consisting of two different sensors in a single package. DHT11 uses a Single bus data format for communication. Now, we will the how the data is transmitted and the data format of the DHT11 Sensor. On detection of temperature above certain threshold or below certain threshold, variables are assigned with a constant value.

Same goes with humidity sensor. Configuring Data sorting according to DateTime:. In this script, I have imported DateTime to assign temperature and Humidity sensor data with a timestamp. This is required for Visualisation of Timely Trends in Data. From DateTime I have taken into consideration allocation of Hourly timestamps as per data.

Every hour, the temperature data changes and these variables are further utilized for data plotting in Streamlit. The below video shows the Back-end of the complete project in action:. The soil moisture sensor as well as the humidity and temperature sensor send data readings with assigned timestamps to Network Gateways. These Gateways take this data, sort the data, perform computation and send this data to web cloud application.

Here, the Network Gateways are the Raspberry Pi devices. The camera module takes in video data and sends it to the Raspberry PI for classification. This data is assigned timestamp and further, this classified data is sent to the Streamlit Web Application Front-end Cloud Server. Using Kepler Geo-spatial analysis with satellite Imaging, this data is plotted on a Kpler map for data visualisation with Timely Trends of data. This data is then made availabel after processing to Mobile Users of the farm to analyse the farm and Apple Plantation data, diseases of plant.

Streamlit is an awesome new tool that allows engineers to quickly build highly interactive web applications around their data, machine learning models, and pretty much anything. Over here, to plot data of soil-moisture of 6 arrays, with nearly 6 plants in each array, we need nearly 36 sensors deployed to produce the inference. Since, these many sensors were not available for the prototype, I have created demo data of Soil Moisture to visualize the data over the plot of land.

Alternatively, the streamlit dashboard supports manual pump activation to activate the peristaltic pump and water the plants. Usually, the plant is autonomously watered based on water moisture in the soil, but in case if there is manual assistance needed, this trigger allows to activate the pump. The logic used over here is that, each time a button is pressed to activate or deactivate the pump, the GPIO pin is either set to high or low as follows:.

The second figure is meant to display the Temperature data over time. In the above code snippets, I had assigned each hourly sensor data a timestamp. This sensor data with timestamp is taken and added to the plotly chart for visualisation of data with time from 6am in the morning to 6 am the next day. For visualization of this data, the respective data timestamp is assigned with the hour of the day to sync data.

This complete process is autonomous. Finally, an average variable for temperature is declared for all the variables over time and this average variable is used to trigger notifications on the notification page as follows:. The third figure is mean to display timely-trend of humidity over time. The process of aggregating and displaying humidity data is the same as tempeerature data.

Finally, an average variable for humidity is declared for all the variables over time and this average variable is used to trigger notifications on the notification page as follows:. The fourth figure is meant to display the plot for cumulative diseases detected in a particular array.

In the above Object detection toolkit, I have altered the darknet video and image analysis python file to give output each time a particular class name is detected. In the streamlit front-end code, each time the variable is detected to be 0, the pie chart is updated increasing the percentage share of the disease in the pie chart.

The Notifications page is used for triggering notifications and updates on the health of the plant based on the OpenVino model data input deployed on the Raspberry Pi. The notifications page displays diseases updates over time as follows -- based on the code snippet:. All these variables were declared in the Darknet script edited earlier in the Object Detection part, so whenever, a class is detected, it assigns the constant value of 0 to the respective class name. This shows the alerts generated when a disease is detected and a greenpopup box when a ripe apple of a flowering plant is detected.

The home page also displays notifications regarding Temperature, humidity and Soil Moisture Data over time as follows:. The last page is dedicated for Geo-spatial Analysis of data using satellite imaging and data plotting over satellite maps corresponding to the latitude and longitude location and plant plot.

For this geo-spatial analysis plot, I have used Kepler. The streamlit dashboard links the web page to the Kepler. Link to the streamlit web app: streamlit-hydra-frontend. At Uber, kepler. In order to help data scientists work more effectively, we integrated kepler. Jupyter Notebook is a popular open source web application used to create and share documents that contain live code, equations, visualizations, and text, commonly used among data scientists to conduct data analysis and share results.

At Uber, data scientists have utilized this integration to analyze multitudes of geospatial data collected through the app, in order to better understand how people use Uber, and how to improve their trip experience. Now, everyone can leverage kepler. Kepler Geo-spatial tool works based on data input from csv, so to configure temperature, humidity and moisture data over time, I will use the pd.

The latitude and longitude data of a plant in an array will be the same and the temperature and humidity data will change over time. This was an example of the data plotted to csv with the help of pre-defined variables. The purple bar shows the humidity percentage while the blue bar and white bar show the rate of temperature of an array.

I have applied various filters for visualizing the trend in data even further like date-time wise data, trends in temperature data, trends in humidity data which can be viewed on the left bar. To find the diseases in the Apple plant, Image processing and Classification is used. Sun light and angle of Image capture is the main factor which affects the classification parameter.

For this, a case study of a farm is required. During a case study, I can capture Plant diseases from different angles and different saturation and contrast levels, along with different exposure and different background. Training the model with a complete dataset including all these parameters, will make the model accurate enough and easily deployable to classify unknown data.

During night time, capturing classification of images based on a RGB Model cannot classify Images properly. Along with this, I am training the model further with images from different angles to predict and classify a disease from different planes. The images in this are in the form of grayscale and hence, the model needs to be trained using this dataset to classify and process night time data.

Hyperspectral deals with imaging narrow spectral bands over a continuous spectral range, producing the spectral fingerprint of all pixels in the scene. A sensor with only 20 bands can also be hyperspectral when it covers the range from to nm with 20 bands each 10 nm wide. These sensors can detect and identify minerals, vegetation and other materials that are not identifiable by other sensors. They are used in plant nutrient status, plant disease identification, water quality assessment, foliar chemistry, mineral and surface chemical composition and spectral index research.

Hyperspectral sensors have two methods for scanning: Push Broom and Whisk Broom. With this method, the scanner is able to look at a particular area for a longer period of time enabling more light to be gathered. The basic architecture of a drone, without considering the payload sensors, consists of: i frame, ii brush-less motors, iii Electronic Speed Control ESC modules, iv a control board, v an Inertial Navigation System INS , and vi transmitter and receiver.

In precision agriculture, the drones are semi-autonomous. In that case, the drone has to fly according to the definition of a flight path in terms of waypoints and flight altitude. Thus, the drone has to embed on board a positioning measurement system e. The payload of a drone includes all the sensors and actuators that are not used for the control of its flight e.

In case of precision agriculture, the sensors embedded on drones are: multispectral camera, thermal camera, RGB camera. During collecting of data from a high altitiude, using hyperspectral and multispectral imaging, there a lot of atomsphere factors which affect the data.

For this, the model needs atmospheric compensation and reduction of noise from the perceived data to predcit and analyse diseases in apple plants, This will be a future improvement after the first succesful deployement of the model. This is the data view from an altitude of 50ft which leads to additional aggregation of atmospheric noise. To reduce this, the drone will be deployed at an altitude of 10ft to get a better accuracy while capturing the diseases.

Usually, for agriculture the terrain is scanned by using satellites with multispectral and thermal cameras. For precision agriculture, due to the needed high spatial resolution, drones are more suitable platforms than satellites for scanning. They offer much greater flexibility in mission planning than satellites. The drone multispectral and thermal sensors simultaneously sample spectral wavebands over a large area in a ground-based scene. After post-processing, each pixel in the resulting image contains a sampled spectral measurement of the reflectance, which can be interpreted to identify the material present in the scene.

In precision agriculture, from the reflectance measurements, it is possible to quantify the chlorophyll absorption, pesticides absorption, water deficiency, nutrient stress or diseases. As a prototype, I have aimed to develop this model to predict diseases inclusively adding chlorophyll absorption prediction model in the second version of the prototype.

Adding GPS calibration on Drone for drone navigation:. They send strings that contain GPS data and other status messages. These strings are called NMEA sentences. Adding location plotting on geospatial map for tracking the drone:. In order to know the location where the data was detected, for a plant in an array and accurately know the exact location of the diseased plant, GPS Calibration is important. For this purpose, a simpe python logic system needs to be deployed to the existing Jupyter Notebook to store the latitude and longitude of the classified data.

Each time a disease is detected, it stores the location of the data through a script as follows:. This code gives the output as follows and prints the latitude and longitude of the region the GPS Module moves through. This gives the output as:. The above script prints the latitude and longitude of the region which can be further used to store in a csv file and the map can be deployed on kepler. Additionaly, a functionality to store these outputs in the form of CSV can be added to make a fully funtional drone based mapping.

Why is drone based disease detection system more useful than individually deploying image sensors in each array? Deploying Individual sensors in each array increases the cost by a significant amount. To reduce thi cost and make this product commercially viable, it is necessary to make an affordable working logic system. For this pupose, Drone based multispectral imaging with GPS Calibration proves to be a viable solution.

In this system, the mapping of the complete farm plot can be performed using Drones along with Disease detection of the plants in the farm. The detecteion can also be done using a top down view system and also can simulate the mapping of the drone using a pre defined path. While deploying the model to inference on the drone, a high FPS based classification is important.

LAI is a measure for the total area of leaves per unit ground area and directly related to the amount of light that can be intercepted by plants. It is an important variable used to predict photosynthetic primary production, evapotranspiration and as a reference tool for crop growth. The above image shows that a large number of apples are detected in the image but estimating the total yield of these is not possible by counting the number of estimated apples.

For this purpose, calculating the total area of the detected apples is important. This shows one of the apples detected in the classifications. For each detection, the xmin, xmax, ymin and ymax coordinates in the detections are stored. After taking these coordinates, estimating the box area is easy. In this way by estimating the box area of the apples the yield of the apples can be detected. Similarly, if diseases apples are detected, the yield is counted as a negative integer based on the magnitude of the estimation.

Once the apple have been detected, it basically has 4 numbers describing the box: its top-left corner x1,y1 and its bottom-right corner x2,y2. Given those, we can also easily compute the area of the rectangle. To compute the area, we only need to compute the width as x2-x1 and the height as y2-y1 and multiply them. The crop yield is also dependent on the variable: Distance between the camera and the crop.

Using this logic, the area of the crop and apple plant can be easily counted and the volume of the individual apple can be declared so that the far owner can have an estimation of the yield in his farm and similarly can estimate his yield for the quarter year. Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms.

One of the straightforward and common methods was to consider only additive effects of G and E and treat their interactions as noise. Linear mixed models have also been used to study both additive and interactive effects of individual genes and environments. More recently, machine learning techniques have been applied for crop yield prediction, including multivariate regression, decision tree, association rule mining, and artificial neural networks.

A salient feature of machine learning models is that they treat the output crop yield as an implicit function of the input variables genes and environmental components , which could be a highly non-linear and complex function. Liu et al employed a neural network with one hidden layer to predict corn yield using input data on soil, weather, and management.

Drummond et al. Marko et al. Weather prediction is an inevitable part of crop yield prediction, because weather plays an important role in yield prediction but it is unknown a priori. The reason for using neural networks for weather prediction is that neural networks can capture the nonlinearities, which exist in the nature of weather data, and they learn these nonlinearities from data without requiring the nonlinear model to be specified before estimation Abhishek et al.

Similar neural network approaches have also been used for other weather prediction studies. We can train two deep neural networks, one for yield and the other for check yield, and then we can use the difference of their outputs as the prediction for yield difference. This model structure was found to be more effective than using one single neural network for yield difference, because the genotype and environment effects are more directly related to the yield and check yield than their difference.

Deep neural network structure for yield or check yield prediction. Here, n is the number of observations, p is the number of genetic markers, k1 is the number of weather components, and k2 is the number of soil conditions. Odd numbered layers have a residual shortcut connection which skips one layer. To predict the output for weather, rainfall and crop yield, designing the neural network that has appropraite number of layers and generates the expected output is important.

For this viewing and exploring the neural network is necessary. Here, OpenVino plays an important role to visualise these layers and also adding custom layers which is not possible using keras. Also it overfit the training data which can give poor results for unseen data. So, to overcome this limitation ensemble model is used. In ensemble model results from different models are combined.

The result obtained from an ensemble model is usually better than the result from any one of individual models. The probability density functions of the ground truth yield and the predicted yield by DNN model. The plots indicate that DNN model can approximately preserve the distributional properties of the ground truth yield.

Importance Comparison Between Genotype and Environment. To compare the individual importance of genotype, soil and weather components in the yield prediction, we obtained the yield prediction results using following models:. Tor Browser aims to make all users look the same, making it difficult for you to be fingerprinted based on your browser and device information.

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