Every year pests cause vast quantities for food to be damaged and lost. And as the global climate changes so will the challenges faced by farmers trying to keep their crops pest free. As a result of this pressing need many farmers and agricultural businesses are turning to new forms of technology.
This includes computer vision systems that allow farmers to monitor their crops for signs of pests and protect their yields better. For these applications to be successful they need to be supported by accurate and effective annotated training data.
Images of crops, fruits and vegetables can be labeled to give AI models a better understanding of pest types and how to spot them. This blog will show how a specific annotation type, semantic segmentation, is being used by developers in this sector to create vital AI applications.
Firstly, we will define semantic segmentation and identify why it is so useful for agricultural AI. Secondly, we will identify the specific pest monitoring use cases made possible by semantic segmentation. And finally, we will show how the right video or image annotation tool can speed up development for your smart farm models.
This annotation method is used to precisely define the shape of objects in training images and videos. Annotators use annotation tools to trace the pixel outline of a piece of fruit or an irregularly shaped vegetable.
This process creates a simplified mask for the image or frame, that is more interpretable by AI models. Semantic segmentation is crucial for models that need to be able to identify complex and varied crop shapes.
Pest monitoring use cases
Semantic segmentation makes a number of important pest monitoring applications possible. Together these systems could substantially improve yields and food security globally:
- Growth monitoring: One way to tell if crops are being adversely affected by pests is growth rates. If plants are growing slower than expected this can indicate they are under stress from harmful pests. AI models can take on the burden of growth monitoring and warn farmers when crops are struggling.
- Pest and disease detection: AI systems can also be used to look for pests directly. Semantic segmentation annotation allows monitoring cameras to identify tiny pests, like aphids, that may be hard for humans to spot. Early warning from AI systems can lead to early interventions that save whole crops.
- Autonomous pesticide spraying: Computer vision AI, trained by precise semantic segmentation labeling, can also be used for pesticide application. Drones, powered by AI models, can locate and spray crops in hard to reach areas with minimal supervision from growers. This technology can also keep workers safe from potentially harmful chemicals.
The perfect annotation tool for smart farming
The future of farming will be led by computer vision AI developers. By monitoring crops 24/7 AI models can ensure that pests don’t get a foothold. And when harmful pests do appear AI equipped drones can be deployed to spray large areas of crops independently.
However, the continued success of this important technology depends on semantic segmentation for training data. Image and video annotation can be a time consuming distraction for busy smart farm AI companies. Therefore, the right annotation tool can make all the difference.
Keylabs is designed to speed up annotation with efficient keyboard shortcuts. Fast outlining functions also make it easier to create semantic segmentation labels for images for fruit and vegetables.
Finally, Keylabs helps developers by providing unique workforce insights and analytics. With this information AI companies can make sure that annotation tasks are given to the highest performing workers. Keylabs makes quality dataset creation achievable for smart farm companies.