Weed and Crop Detection using Computer Vision on Drone Imagery

November 9, 2023

Introduction

Omdena is an organization that uses AI to solve social and environmental problems. One of Omdena’s projects is the Weed and Crop Detection project, which is developing a computer vision model to identify weeds and crops in drone imagery.

Problem

Weeds are a major problem for farmers. They compete with crops for water, nutrients, and sunlight, and can reduce crop yields by up to 50%. Herbicides are often used to control weeds, but they can be expensive and harmful to the environment.

Proposed Solution

Omdena’s Weed and Crop Detection project is developing a computer vision model that can accurately identify weeds and crops in drone imagery. This model can be used to help farmers target their herbicide applications more precisely, reducing the amount of herbicides they need to use and the environmental impact of herbicide use.

Implementation

The Weed and Crop Detection model is being trained on a dataset of labeled drone images. The model is being developed using a deep learning approach, and is being trained on a convolutional neural network (CNN).

Results

Preliminary results show that the Weed and Crop Detection model can accurately identify weeds and crops in drone imagery with an accuracy of over 90%.

Conclusion

Omdena’s Weed and Crop Detection project has the potential to revolutionize the way that farmers control weeds. By accurately identifying weeds and crops in drone imagery, the model can help farmers reduce their use of herbicides, save money, and improve their crop yields.

How this case study is relevant to other organizations

The Weed and Crop Detection project is relevant to any organization that is interested in using AI to solve social and environmental problems. The project is also relevant to any organization that is interested in using AI to improve the efficiency and sustainability of agriculture.

Here are some specific examples of how other organizations could benefit from Omdena’s Weed and Crop Detection project:

Government agencies: Government agencies could use the Weed and Crop Detection project to help farmers reduce their use of herbicides and improve the environmental quality of their farms.
Seed companies: Seed companies could use the Weed and Crop Detection project to help farmers identify and control weeds that are resistant to their herbicides.
Farm machinery companies: Farm machinery companies could develop new equipment that can be used to apply herbicides more precisely using the Weed and Crop Detection project.
Food companies: Food companies could use the Weed and Crop Detection project to help their suppliers reduce their use of herbicides and improve the sustainability of their supply chains.

Overall, Omdena’s Weed and Crop Detection project is a promising new technology that has the potential to benefit a wide range of organizations.

Successful Project between Omdena and Skymaps

In collaboration with Skymaps is an agritech startups, Omdena successfully completed a two-month project on weed and crops detection using computer vision on drone imagery. The project aimed to minimize the use of herbicides by developing a state-of-the-art model that can detect and identify various weed species and different crops.

The resulting model seamlessly integrates with the Skymaps application, offering users the option to select the crop and the most probable weeds, ultimately leading to less spraying of herbicides.