How crucial is Image Annotation for Agriculture to Adapt AI?

Can you imagine an industry that involves more challenges than agriculture? You reap what you sow, they say. But what they forget to add is “if you’re lucky.” When the weather strikes or crops get affected by the disease, farmers can hardly talk about yields. Or when a global pandemic hits, all of a sudden it gets harder to manage various processes because most are not digital.

At the same time, the global population is growing, and urbanization is continuing. Disposable income is rising, and consumption habits are changing. Farmers are under a lot of pressure to meet the increasing demand, and they need a way to increase productivity. Thirty years from now, there will be more people to feed. And since the amount of fertile soil is limited, there will also be a need to move beyond traditional farming.

We need to look for ways to help farmers minimize their risks, or at least make them more manageable. Implementing artificial intelligence in agriculture on a global scale is one of the most promising opportunities.

Artificial Intelligence is being used by the agriculture industry to help produce healthier crops, control pests, monitor soil, and growing conditions, organize data for farmers, reduce effort, and improve a wide range of agriculture-related operations along the food supply chain.

Image Annotation for Agriculture

In the agriculture field image, annotation helps to make crops and other things recognizable to make the right decision without the use of humans. So, let’s find out what image annotation can do for an agricultural field and how it is utilized in machine learning and AI.

Crops and Vegetable Detection

The robots used agriculture and farming to detect the crops including fruits and vegetables for performing various tasks. Image annotation annotates the crops to make them recognizable to machine learning models like robots or drones.

Annotation to Check Plants Fructification

Just like detecting the crops, image annotation also helps to check the fructification level of plants, if they are ready for harvesting, or what is the maturity level of plants. Image annotation techniques can help to detect such plants and inform the farmers to take actions as per the crops' Fructification levels.

Crops Health Monitoring

Apart from detecting the crops, image annotation also helps computer vision models to check or monitor the health of the crops through deep learning AI model training. The robots can closely monitor the crop or plant and analyze its condition whether it's matured, not matured, infected, or need pesticides to protect from insects and other harmful pests.

Live Stock Management

Animal husbandry, which usually comes in farming, you can say part of the agricultural sector can be also managed by AI-enabled devices. Image annotation helps to detect and recognize the animals helping the farmers to keep an eye and monitor the live stocks making the animal husbandry business profitable. Bounding box annotation and polygon annotation helps to recognize the animals precisely.

Monitoring soil health

AI systems can conduct chemical soil analyses and provide accurate estimates of missing nutrients. The type of soil and nutrition of soil plays an important factor in the type of crop is grown and the quality of the crop. It’s time to implement image recognition-based technology. that can identify the nutrient deficiencies in soil including plant pests and diseases by which farmers can also get an idea to use fertilizer which helps to improve harvest quality. The farmer can capture images of plants using smartphones. TagX can label these images for model training.

Drone Imagery Data Annotation

In the agricultural sector, image annotation is also used for geo-sensing to check the soil condition and other attributes of the agricultural fields to analyze the situation to decide for the right timing of crop sowing and harvesting. Basically, drones are used and semantic segmentation image annotation technique is used to observe and monitor the health condition of the various agricultural fields.

Unwanted Crops Detection

With useful crops there are many unwanted crops that utilize the minerals of the soil under the roots, that should reach the main crop. Such unwanted plants are called weeds that should be removed to improve the crop yield of the crops and boost the productivity of the entire agricultural field.

Conclusion

In the future, AI will help farmers evolve into agricultural technologists, using data to optimize yields down to individual rows of plants. Artificial Intelligence in agriculture not only helps farmers to automate their farming but also shifts to precise cultivation for higher crop yield and better quality while using fewer resources.

Companies involved in improving machine learning or Artificial Intelligence-based products or services like training data for agriculture, drone, and automated machine making will get technological advancement in the future and will provide more useful applications to this sector helping the world deal with food production issues for the growing population.

TagX provides you with high-quality training data by integrating our human-assisted approach with machine-learning assistance. Our text, image, audio, and video annotations will give you the courage to scale your AI and ML models. Regardless of your data annotation criteria, our managed service team is ready to support you in both deploying and maintaining your AI and ML projects.

Originally published at https://www.tagxdata.com.