AI In Insect Research – A World Of New Possibilities

Insect research is a branch of zoology which involves studying the various species of insects and their activities. 

There are many reasons behind studying the activities of insects. For instance, continuously observing the insects at the same location provide insights into how they are affected by climatic changes. 

Technology is known to simplify tasks, irrespective of the field of application. AI, which has proven to be one of the most important tools in our generation has proved to be useful in insect research too.

As compared to medicine, traffic control or manufacturing industries, AI hasn’t yet penetrated much into the field of insect research. However, even in its nascent stage, its applications are nothing short of wonderful! 

Let us check out some of the best uses of AI in insect research.

AI in Insect Research

One of the major tasks in insect research consists of observing various types of insects, studying their features, and classifying them.

We are well aware of the vastness of mother nature’s creations. There are almost an uncountable number of species, and scientists have only studied a small portion of the insect world. 

Deep Learning for Insect Classification

Now, imagine a biologist or a researcher manually examining the various images of insects captured over a period. Carefully identifying every feature from the pictures to classify the insects would be a time-consuming job. 

This is where AI comes into the picture. With concepts of deep learning and computer vision, insect researchers can now work on models that would automatically identify the common features among insects.

The models can further be trained to classify each insect into the right species, family and so on. 

Due to quick and accurate classification, detecting new species of an insect becomes easier. 

Enhanced Image Collection

Thermal imaging is a branch of AI which helps in collecting and analyzing the images of insects. Today, sensors with advanced features are already being employed in insect research and exploration. 

Thermal cameras are of great help in studying the biodiversity in the insect world. With AI, scientists can now easily identify the patterns in which insects breed.

Another key element to understand the state of the ecosystem is analyzing the insect-flower interaction. Be it breeding or interaction with flowers, it is highly impossible to manually collect the minute details of insect behavior.

Time-lapse images from cameras with advanced processors on the other hand, will neither miss the important details, nor take a long time to capture data. 

With the wealth of data obtained from these cameras, identifying which species are thriving, and which are endangered becomes a simpler task. 

AI for Pest Control

Farmers tend to apply pest control measures to their lands in their entirety.

Deep learning models can help study their fields for the affected parts and details of when and how these parts are getting infested. 

AI is of immense help here, as farmers can save a lot of money by enforcing pest control measures to just the affected parts of their land. 

Challenges of AI in Insect Research

For a field such as insect research where AI incorporation is still taking baby steps, there exist some challenges.

Let us see what are the obstacles to the integration of AI into insect research. 

1. Difficulty in Monitoring

Insects do not always exhibit all of their features. For instance, in some insects, certain features can be seen only if it is preying on another organism, or while escaping from its prey.

In some cases, insects that belong to different groups exhibit some similar features. 

Due to this, the deep learning model may not have adequate learning parameters, ultimately affecting the accuracy of the model. 

2. Imbalance in Datasets

The accuracy of an AI-based model largely depends on the size and quality of the dataset it is trained on.

Datasets for insect classification are highly imbalanced. There are very few data points regarding rare insect species.

On the other hand, insects that are commonly spotted occupy the majority of the data. 

Methods such as class resampling may have to be applied to reduce the effect of the imbalance in data. 

Summary

In some parts of the world, farm owners have already started implementing AI for pest control.

But currently, there may be some roadblocks for researchers – insufficient data, imbalanced datasets, the varying light conditions under which the insect images are captured, and so on. 

However, the nature of technology is such that new developments try to eliminate the limitations and introduce never-seen-before-features.

This will open up new opportunities for AI in insect research.