Agricultural Pesticide Spraying Robot

Are you tired of struggling to determine which crop is best suited for your soil type and minerals? Are you worried about the harmful effects of fertilizers on your health? Then, our Agricultural Pesticide Spraying Robot project is the perfect solution for you!

We have divided our project into three phases to ensure that we cover all aspects of agriculture. In phase one, we created a crop prediction data science project that predicts the best crop suited for your soil type and minerals. We collected data on crops, soil type, and soil minerals and used machine learning to create a model that accurately predicts the best crop. This project is a game-changer for farmers as it will save them time, money, and effort in selecting the right crop.

In phase two, we developed a Pesticide spraying robot that sprays the fertilizers to increase soil fertility. The robot car is controlled using Bluetooth, and a motor is used to spray the fertilizer. By using this robot, farmers can reduce their exposure to harmful fertilizers and ensure their safety. Our low-cost and easy-to-use device will revolutionize the way farmers work in their agricultural fields.

Finally, in phase three, we have created an e-commerce website where you can buy fresh vegetables directly from the farmers. Our website offers a wide range of vegetables that are sourced directly from the farmers, ensuring that you get fresh and healthy produce. This website is user-friendly and easy to navigate, making your online shopping experience hassle-free.

In conclusion, our Agricultural Pesticide Spraying Robot project is a comprehensive solution to all your agricultural needs. Our project will not only save you time, effort, and money but will also ensure your safety and health. Join us on our mission to revolutionize the agricultural industry and make farming easier and more efficient.

• Phase-1 : 

• First we want to create a crop prediction data science project to help the farmers to decide which crop is best suited according the soil fertility.
• For this, we collected the dataset about the crops and their soil type and soil minerals.
• Then, using this dataset we written the machine learning code to predict which type of soil is best using the soil type and soil minerals as an input.   
• For Project link- click here.
• This dataset was created by expanding the rainfall, climate and fertilizer datasets available for India.

• Physical parameters:
• N - ratio of Nitrogen content in soil
• P - ratio of Phosphorous content in soil
• K - ratio of Potassium content in soil
• Temperature - temperature in degree Celsius
• Humidity - relative humidity in %
• PH - PH value of the soil
• Rainfall - Rainfall in mm
• With the help of the below video we can get the required parameters mentioned above. video link

• To download the dataset - click here

• 

  Sample data set

• You can get the required parameters value for the specific crop.

• 

  showing the value of nitrogen for Rice crop.
  
  

  
  
• Next we using Machine learning algorithm, logistic regression, we made a model it will predict the required values for the crop.
• Logistic regression- In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination). 
• Get code from this GitHub repo - click here

      

• Phase- 2:
• Next step is we created a Pesticide spraying robot, to spray the fertilizer to increase the soil fertility.

• For this we used Arduino and made the spraying robot car.

• We can control the robot using Bluetooth, and we used a motor to spray the fertilizer when we press the button on our phone.

• By this, it will be helpful for the farmer not to get directly connected to the harmful fertilizers.

• The project provides a new approach to replace humans in various agricultural activities such as spraying pesticides, spraying fertilizers and other tasks, thereby ensuring the safety of farmers. The approach created involves the creation of a prototype that uses a simple, low-cost device to assist farmers in a variety of agricultural field work.

• Hardware components :
• Ardunio UNO
• DC Motor
• Hc-05 Bluetooth Module
• SG90 Micro-servo motor
• Micro-motors and Grippy Wheels
• Dual H-Bridge motor drivers
• DC charge pump Adjustable voltage regulator
• Jumper wires.

• Software Components :
• Ardunio IDE
• Ardunio Web Editor
• MIT APP Inventor

• Block Diagram:
  
  
  
• You can find the code and more about our project click here.

• Phase- 3:
• Now we created a e-commerce website to sell the vegetables directly from the farmers.
• For this we used wix.com.
• In this website, it will be available all the vegetables everything from the farmers for you can buy online.
• For website link- click here.