Biodiversity loss is one of the thorniest problems farmers face. It affects soil fertility, crop yields, and ecosystem services. Chirrup has the solution to this problem, and it lies in bird sounds. 

Namely, birds play a critical role in maintaining the health of farmland ecosystems. However, identifying and monitoring bird species can be highly demanding and time-consuming for farming professionals. To address this problem, the client developed a cloud-hosted bird sound recognition system. Via this easy-to-use platform, users can upload and manage recordings, as well as generate reports. 

Time-consuming improvements of poor-quality audio files

The client’s primary goal was to create a highly intuitive application that enables fast and accurate bird recognition from audio clips. However, the process was more complex than they expected. Namely, they faced numerous difficulties, such as poor-quality audio files that required lots of preprocessing. They were wasting lots of time clearing and improving the audio data so that it could be further analysed. 

Collecting and validating data

To the precision of bird sound predictions, we focused on collecting as much data as possible. We did that by scraping relevant information from multiple sources. During the process, data validation was our top priority. We made sure all data was properly licensed and met ethical standards.

Ensuring platform accuracy 

One of the client’s requirements was to improve the accuracy of predictions. We did that by developing an occurrence mask for the output of the model. The purpose of the mask was to check whether some bird species could appear in certain locations during specific times of the year. That helped the client reduce false positives in platform predictions. While creating the occurrence mask was highly time-consuming, it delivered immense value to the client and improved its accuracy. 

The platform development required a team highly skilled in data science and software development. We deployed a team that consisted of: 

• Two data scientists, responsible for processing and analyzing audio data. Their task was also to implement the machine-learning algorithms used for bird sound recognition.
• A back-end developer, whose task was to build and maintain the server-side platform infrastructure. It included data storage, data aggregation, and app deployment.
• A front-end developer, who designed and implemented the user-facing web application. This included the  UI/UX design.
• A product owner/project manager (PO/PM), whose task was to oversee the platform deployment and delivery. They defined and prioritized product features, managed the product backlog, and ensured the team met the client’s expectations.

While working on the project, we closely collaborated with ecology consultants. That way, we ensured the platform provided invaluable insights into the biodiversity and health of the land.

The model we developed exceeded the capabilities of its existing counterparts. It is 5 times faster than similar bird sound recognition solutions. Speed improvements let the Chirrup model perform tasks faster and more efficiently. That is exactly what gives it a major competitive advantage in this growing market.

Chirrup proved to be super-useful when it comes to detecting rare species and generating a large number of true positive predictions. Currently, 21 farms use the platform. Audio samples were collected from two sites on each farm. It is expected that the figures will double by the end of May once production starts.

How we achieved those results:

A deep learning network

We used a deep learning network to analyse the chirps of birds and identify the species in different areas. 

Building a scalable and user-friendly system

We focused on boosting its accuracy while maintaining its user-friendliness and ease of use. The platform’s machine-learning algorithms were tested and optimised to ensure the highest level of accuracy.

The back-end infrastructure was designed with scalability in mind. The platform is built to handle large volumes of recordings and data. On the other hand, the front-end interface was designed to be highly intuitive and user-friendly. It made it easy for farmers and other users to upload recordings, manage their farms, and easily generate reports.