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Image Annotation for applying Machine Learning to Agriculture

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Ever since Artificial Intelligence and Machine Learning have entered different spheres of human life, things have become more efficient and productive. Both the technologies are swiftly integrating into some of the most vital fields of human life while achieving the unachieved.

One such field is Agriculture. AI has emerged together with big data technologies and high-performance computing to create new opportunities in agricultural operational environments.

  • The computer vision-based crop monitoring and production system are definitely making agriculture and farming easier.
  • When it comes to harvesting, ripping, crop health monitoring and improvising the crop productivity, AI Robots, automated machines and drones are playing a major role.

Now the question is how AI-enabled machines help in precise agriculture and farming?

Well, the answer to this question is image annotation. Confused?

Don’t worry, this article will give you all the required information and of course the answer(s). But before we get into the details, let us give you a brief on image annotation.Image annotation helps machines in detecting or recognizing various objects/things in the agricultural fields which, in turn, helps the machine in taking the right action. AI machines work on computer vision technology where they are trained through annotated images using the right machine learning algorithms.

Image Annotation For Machine Learning In Agriculture

With technological advancement, image annotation has started to play a crucial role in applying ML to agricultural data created through the data labeling process. Let’s see the applications of machine learning in agriculture that is possible through image annotation services.

Robots for Precision Agriculture

From seeds planting, weeds handling, monitoring productivity growth, sorting, fruits/ vegetable picking, packaging to grading, robots, with the help of machine algorithms, can perform various actions in the agriculture sector. Further, weed detection, checking the fructify level of fruits/vegetables, and monitoring the health condition of plants are also handled by these robots in the most accurate manner.

When combined with deep learning algorithms, they easily identify any defect(s) from all angles with large color and geometric variation. The algorithms are set in a manner where they first detect the objects and later classify accordingly.

To make way for training robots the annotated images of plants, multiple crops and floras are feed into the algorithms with one of the most popular image annotation techniques – bounding box annotation. It helps in making the crops, weeds, fruits and vegetables recognizable to robots.

Sorting Fruits and Vegetables

Once the fruits and vegetables are collected, a sorting task is performed by the robots to separate the healthy and rotten fruits/vegetables before delivering them. These robots can also detect any problems, to predict which items will last longer for shipment and which items can be retained for the local market.

These tasks can be performed based on deep learning using the huge quantity of training data of annotated images. Further, the entire sorting and grading process can only be accurate when precisely annotated images are used to train the robots. Here image annotation outsourcing can be highly beneficial since image annotation companies have the required expertise and efficiency.

Monitoring the Health of Soil, Animals & Crops

With the help of Geosensing, drones and other autonomous flying objects can monitor the health condition or soils and crops. This helps farmers in knowing the right time for sowing and the various actions that must be to save the crops. After all, right soil conditions and timely insecticides are critical for better production and high crop yield.

When it comes to detecting the health of animals, Body Condition Score for bovines comes into the picture. It is an AI-enabled technique that helps in measuring the health of buffalo, cow and other similar animals. Given by the veterinarian, this score is a depiction of the body condition of animals and know their reproductive health, milk production or feeding efficiency.

Crop Yield Prediction

AI, with deep learning datasets, helps in predicting the crop yield through portable devices like smartphones and tablets. This, in turn, provides a better, exceptional farming experience that benefits all.

However, collecting and developing deep learning platforms isn’t a cakewalk; it requires expert guidance and knowledge to provide reliable yield forecasts.

Other Projects In Precise Agriculture

Besides the above-discussed use cases, image annotation offers several other object detection activities in agricultural sub-fields including irrigation, soil management, weed detection, maturity evaluation, fruit density, canopy measurement, land mapping, among others. All this together makes way for more efficient, productive and advanced farming.

The Image Annotation Solutions you need for Agricultural Activities

Acquiring high-quality machine learning training data for computer vision-based AI models is a challenging task. Luckily, dedicated data annotation companies like SunTec.AI provide world-class image annotation services to diverse industries while ensuring your image annotation projects across all domains are implemented efficiently.

Our in-house team of specialist annotators and highly experienced experts customize the annotation solutions as per your requirements. Further, we are committed to delivering satisfying results by using wide options for annotations like bounding boxes, polygons etc. depending upon the usability object types and project requirement.

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