Melindungi Lebah Madu dari Hornet Asia ‘Pembunuh’ dengan Bantuan AI

Invasi Hornet

“Murder” hornets, also known as Asian hornets, yellow-legged hornets, Asian predatory wasps, or by their scientific name Vespa Velutina, are an invasive species that prey on honeybees.

Mengendalikan penyebarannya telah menjadi perjuangan dan kebanyakan merupakan pertempuran yang kalah sejauh ini, dengan spesies ini menyebar di Asia Timur, Eropa, dan bahkan baru-baru ini, Amerika Utara.

Sejauh ini, upaya untuk membatasi penyebarannya mengandalkan identifikasi manual oleh relawan, peternak lebah, dan lembaga lingkungan. Namun, metode ini terbukti tidak efisien, dengan banyak alarm palsu dan terlalu banyak sarang hornet yang tidak terdeteksi. Setiap sarang yang tidak terdeteksi menyebarkan ratu baru setiap tahun, sehingga perkalian spesies invasif ini menjadi eksponensial.

Syukurlah, metode baru yang mengandalkan AI mungkin dapat membantu, memberikan aplikasi baru yang mengejutkan dari pembelajaran mesin tingkat lanjut.

Pentingnya Lebah Madu

Honeybees have been domesticated by mankind for millennia to produce honey and other valuable products like beeswax and royal jelly.

Mereka juga merupakan bagian penting dari ekosistem, berkontribusi pada penyerbukan bunga liar serta tanaman pertanian penting. Ini mencakup sebagian besar pohon buah, pohon kacang, serta tanaman bernilai tinggi seperti cokelat, anggur, tomat, stroberi, raspberry, agave, dll.

Secara total, lebah madu diperkirakan memberikan layanan penyerbukan senilai $15 B “gratis” hanya di AS, dan jauh lebih banyak secara global.

Populasi lebah telah berada di bawah tekanan berat dalam beberapa dekade terakhir, dengan penurunan populasi akibat pestisida, parasit, dan patogen, hilangnya bunga dan padang rumput asli, tanaman invasif, serta perubahan iklim.

Invasi Hornet

An important threat to honeybees is the Asian hornets, which focus on honeybees when they find them in a new environment.

Asalnya dari Asia Tenggara, hornet Asia adalah predator kuat bagi populasi lebah asli di Amerika Utara, Eropa, dan Asia Timur. Berbeda dengan spesies asli di Asia Tenggara, lebah di wilayah ini kesulitan bertahan dari predasi hornet Asia.

Kemunculan pertamanya di Eropa terjadi di Prancis pada tahun 2004, diikuti penyebarannya di Spanyol pada 2010, Italia pada 2012, dan Jerman pada 2014. Deteksi pertama di Amerika Utara terjadi di Kanada dan AS pada 2019.

Memungkinkan bahwa lebah madu asli dapat beradaptasi dan belajar bertahan dari hornet Asia, namun mereka membutuhkan waktu. Jadi, membatasi populasi mereka selama beberapa dekade ke depan mungkin sangat diperlukan untuk membantu spesies lebah asli bertahan.

Sumber: Unsplash oleh USGS 

AI untuk Menjaga Hornet

The key part in avoiding the spread of Asian hornets is to detect their arrival in a new ecosystem as early as possible. If the initial nest or first dozen nests are left to spread more queens, their number multiplies very quickly, leading to exponentially growing costs and environmental damage.

Identifikasi manual terlalu tidak dapat diandalkan untuk menjadi cukup. Sistem deteksi otomatis sebelumnya telah dicoba, tetapi tingkat identifikasinya biasanya berada dalam kisaran ~74.5–83.3%, dengan banyak hasil positif palsu.

By leveraging advances in AI and machine vision, researchers at the University of Exeter have managed to achieve a detection rate above 99%, with their results published in Nature in the scientific paper “VespAI: a deep learning-based system for the detection of invasive hornets”.

Detektor Hornet VespAI

The prototype of the AI hornet detector, named VespAI, was deployed on the island of Jersey, close to the French coasts.

It includes a camera, a power source (battery + 40W solar panel), and as little computing as Raspberry Pi 4 hardware.

Sumber: Nature

Training data was collected from several points in Europe and manually annotated so Asian hornets could be properly detected.

Sumber: Nature

The system can distinguish between Asian hornets and native hornet species. It uses an AI method called layer-wise relevance propagation (LRP), which allows the AI system to correctly identify the importance of each pixel in detecting accurately Asian hornets.

For example, the pixels aligned with the orange band on the fourth abdominal segment, and those around the outer edge of the wing, were among the important data points.

Sumber: Nature

The machine vision software used was YOLOv5 (You Only Look Once), created by Ultralytics, a software also at the core of self-driving car technology.

Menuju Netralisasi Hornet Otomatis

Thanks to the nonlethal nature of the detector, the Asian hornet could be followed back to its nest, and the nest subsequently destroyed. It also does not kill other species like hornet traps generally do.

By detecting its target continuously without human intervention, VespAI’s ability to detect Asian hornets early on is several orders of magnitude superior to manual identification methods.

This, possibly, makes it the only credible option for detecting invasion early enough to stop it entirely.

Finally, the use of open-source software and low-cost hardware makes the mass deployment of this system realistic and cost-competitive compared to the existing hornet mitigation strategies.

It also does not rely on cloud computing and only uses WiFi connectivity to send alerts once an Asian hornet has been detected. Triggering the AI analysis only once an insect has been detected reduces power consumption.

Perusahaan AI di Pertanian

Ecosystem management and agriculture are often viewed as “un-sexy” industries, as far as one can imagine from the tech industry and AI.

This is not true anymore, with plenty of innovative technologies making their way into the agricultural sector, even if none are directly dealing with Asian hornet yet.

1. Arugga

This company, which has raised $5.8 million to date, builds AI-powered robots that use computer vision, developed via the NVIDIA (NVDA ) Metropolis platform, to identify flowers ready for pollination and then initiate the process by blasting air pulses at them.

While the bots’ performance is on par with the bumblebees and, in some cases, better by up to 5%, it also comes with the ability to collect and analyze data along the way.

Arugga’s robots have demonstrated yield improvements of up to 20% without sacrificing quality. Its roving ground robot, Polly, works with strawberries, blueberries, tomatoes, and other crops.

Most recently, the company converted Finnish Agrifutura’s 4.6-hectare greenhouse into the world’s first facility to fully utilize robotic pollination technology.

It is also looking to add pest and disease detection to its robots, through a collaboration with Israeli startup ViewNetic. In the future, it also aims to add non-contact pruning, yield prediction, and plant lowering.

2. Advanced.farm

Progresses in machine vision can also be deployed beyond the question of pollination. For example, it can be used to spot and correctly assess the maturity of fruits, and then pick them.

These tasks are usually very labor intensive. Instead, Advanced.farm is using 6 robotic arms, machine vision, and a suction cup to harvest apples without the need for human presence gently.

Sumber: Advanced.farm

It can also do it at night, allowing for a 24/7 harvest schedule. Advanced.farm has also designed a strawberry harvester, which is 5x more efficient than a human harvester.

So despite a little spooky look when operating at night (see below), it is really safe and efficient.