Image Annotation for AI Innovation in Agriculture

Image annotation is the foundation of many machine learning models. By attaching labels and semantic segmentation techniques to images, annotators add context and information that facilitate the training of computer vision systems. Image annotation also allows machine learning engineers to refine their models, establish goals, and work out how to deal with edge cases and uncertainties.

This vital process is leading to exciting AI powered applications in virtually every sector. Agriculture is one industry that is starting to see the benefits of automation and efficiency that machine learning algorithms can provide.

This blog will focus on three use cases for AI in agriculture that are made possible by conscientious image annotation services. Professional image annotation providers, like Keymakr, can leverage their hard won expertise to create high quality datasets that support innovation in this field.

Monitoring growth rates

Assessing the rate of growth of crops is an essential part of achieving optimal yields. Understanding how well a particular plant is growing allows farmers to intervene before problems damage entire crops. AI can provide growers with a real-time view of growth rates across wide areas.

This can save a significant amount of time whilst giving early warning of issues such as: water or nutrient deficiency, disease and pest problems, and toxicity. AI models are trained to interpret and report plant growth rates with the help of annotated datasets. Semantic segmentation techniques divide plant pixels from background pixels in these images, and labels can indicate the percentage growth of individual plants.

Assessing ripeness

Knowing when fruits and vegetables are optimally ripe is crucial for achieving the best possible, and most profitable harvests. AI can help to assess crop maturity across large fields, helping to save farmers time and schedule harvests more accurately. Fruit maturity, in particular, is usually determined by the colour and size of the fruit.

Annotators can outline fruit with polygon line tools to show the progression of fruit growth in training images. Labels can also be used to indicate the level of ripeness of target fruits, allowing machine learning models to operate in real world fields. Because different fruits develop and mature in different ways, tailoring datasets for each fruit variety is essential.

Detecting disease

AI technology is also helping farmers to identify plant diseases early, and before they can destroy entire crops. Computer vision based systems can monitor crops in real-time with a level of detail and scrutiny that is not possible for farm workers who have thousands of other tasks to deal with. Models are trained to identify diseases on plant leaves with annotated images.

Annotators use bounding boxes to highlight and label areas of disease in selected training images. With this added information AI disease detection algorithms can reliably distinguish between healthy and ailing plants. Monitoring of this kind can also identify the presence of harmful weeds and parasites, allowing farmers to precisely deploy countermeasures. This kind of active surveillance is key to precision agriculture.

Securing effective image annotation

Agriculture is a sector that can and is benefitting hugely from development in AI. Image annotation services can support the continued development of this technology by offering key advantages to developers.

• Collaboration: Experienced image annotation providers, like Keymakr, are able to augment agricultural AI projects by communicating well with AI company clients. Crowd sourced, remote annotations are often hard to control and improve. Troubleshooting is much easier when dealing with in-house, managed teams of annotators.
• Management: Keymakr’s proprietary annotation platform has innovative management features that allow annotation tasks to be distributed based on previous performance and experience. This functionality can help improve the accuracy and speed of image annotation for agriculture based projects.
• Flexibility: Outsourcing to dedicated image annotation services allows AI companies to moderate their data needs when development dictates. This flexibility helps control costs without hampering research.

Keymakr is helping to bring essential agricultural AI technology to market by providing innovators with precise image annotation.