Drone-based Crop and Livestock Monitoring

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In recent years, precision agriculture has become an increasingly popular method for improving crop yields and reducing costs. The use of drones in agriculture is one of the most promising applications of this technology. Crop and livestock monitoring is a crucial aspect of modern farming, as it helps farmers to optimize their yields and improve the health of their crops and animals.

Maintaining acres of farmland with traditional agriculture is time-consuming and expensive. Pest & bacteria growth is estimated to destroy one-third of all the agricultural yield. In addition, farmers are required to monitor livestock and control pests and animals.

The drone was equipped with a high-resolution camera and iMX 8M, a powerful processor, which was used to capture images of the crops and animals. The images were then analyzed using image processing algorithms to detect the health of the crops and monitor the animals.

Next, we used OpenCV and machine learning algorithms to train the drone to recognize different types of crops and animals. The algorithms were trained using a dataset of images of crops and animals, which were labelled with their respective classifications. The drone was then able to accurately identify the crops and animals in the images it captured.

We also implemented a system for monitoring crop health by analyzing the images for signs of disease or stress. This was done by looking for changes in colour, shape, and texture in the images of the crops. The Bosch BME688 Env Gas sensor is a power-packed sensor that gives us the Relative humidity, barometric pressure, temperature, and VOC (gas sensing) data to detect the bacteria growth in the crop. The drone was able to detect these changes and alert the farmer to potential problems with the crops.

The drone will be trained to fly over the agricultural field. The vision system is pre-programmed to recognize crop growth, livestock, and trespassers and trigger alerts if there is an anomaly.

 

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  • ABOUT THE ENTRANT

  • Name:
    Rahul Khanna
  • Type of entry:
    individual
  • Patent status:
    none