talk · Tuesday 22 September · 09:21–⁠09:33 · SAL A

AI-vision for integrated management of pest insects and their natural enemies

Ritter Guimapi · AI for Biodiversity Data

Recording time 41:50–55:43Open on Vimeo ↗

Summaries and key moments are machine-produced. Check the recording for the speaker’s exact words.

The short versionMoving precision spraying from static weeds to mobile pests and their natural enemies is blocked mainly by the difficulty of collecting representative image data.

Overview

What this was about

Ritter Guimapi of NIBIO presented an idea and work in progress: extending precision sprayer robots, which in Norway have cut herbicide use by 40-90% by dropping herbicide only on detected weeds, to insect pests within conservation biological control. The vision is a robot that distinguishes pests from beneficial natural enemies (e.g. parasitoids) and applies insecticide only to pests. He described why collecting representative training images has proven much harder than for weeds: pest-enemy interactions are transient and hard to time, insects are mobile, damaging life stages sit on leaf undersides, affordable equipment is needed, and regulations restrict releasing natural enemies in controlled settings. He invited suggestions on equipment and approaches.

Why it matters. Integrating AI vision into conservation biological control could reduce pesticide use while protecting beneficial insects, but data acquisition is the bottleneck.

Key ideas

In the room

  • Integrated Pest Management and conservation biological control aim to keep pests below thresholds while supporting beneficial organisms.
  • Precision sprayer robots in Norway reduce herbicide use by 40-90%, helped by regulations banning drone spraying.
  • Transfer learning was the first idea, but field data collection failed.
  • Challenges: timing of interactions, insect mobility, eggs and larvae on leaf undersides, cost of equipment, and national regulations.
  • A controlled environment is being explored but is not simple either.
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Notable moments

In their words

Transcript

Automatically generated captions can contain mistakes, especially in names and technical terms. Times are relative to the room recording.

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Programme abstract

AI-vision for integrated management of pest insects and their natural enemies

Ritter Guimapi

Precision farming is being adopted by many farmers, but the number of reliable tools for controlling weed, pest invertebrates and disease in arable cropping system is still quite limited. Most decision support models for plant protection against invertebrate pests consider the pest invertebrate, the plant and abiotic factors affecting them. Natural enemies of invertebrate pests are seldom considered as a factor in the decision support models. Recent advancements in precision technologies used in weed management rely on machine vision models and algorithms where weeds are identified from the cultivated plant and the chemical herbicides are applied only where weeds are identified, without broadcast uniform spraying an entire field, resulting in a 40-97% reduction in the use of chemical herbicides. Our focus in this project is therefore to reveal whether similar techniques could be adapted for precise control of pest insects while protecting and conserving their natural enemies. Our aim is to develop an Artificial Intelligence (AI) workflow that utilizes identification of pest insects and their natural enemies identification for automatic crop management systems. This work begins with the creation of a labelled and annotated image dataset for selected crops with the key pests and natural enemies. Thereafter, the dataset will be used to identify the best modelling toolbox for AI-based machine vision model for automatic species identification of pests and their natural enemies. The result will serve as basis toward designing a framework for pest monitoring and conservation biological control in the selected crops at field level.

Submitted to the conference programme before the talk, so the talk itself may differ.