Humans vital despite AI crop pest and disease control


Pest and disease monitoring is a labour-intensive process, which requires scouts to accurately evaluate plant and crop health as they move across the greenhouse, field or farm. AI-driven image analysis is aimed at helping to automate crop surveillance


Technological advancements in pest and disease scouting are transforming a labour-intensive sector into a more efficient and data-driven one. As artificial intelligence (AI) is developed to aid crop production, growers must be more critical than ever in assessing the benefits of these early-stage solutions.

Dr Mikkel Grum, A research and development director at global crop pest and disease mapping experts Scarab Solutions,  says farm and crop protection managers should continue focusing on the technologies augmenting human labour instead of holding out for the AI promise.

The Food and Agriculture Organisation of the United Nations (FAO) estimates that between 20 and 40 per cent of global crop production is lost annually to pests and diseases, costing the global economy $220 billion. Pests such as thrips, aphids, leaf miners, mites, whiteflies and caterpillars, diseases such as blights, mildews, botrytis and stem and root rots are common throughout all the climatic zones.

It is true that to become even more effective, crop management will require improved techniques as well as technologies. Many believe AI holds the answer.

AI takes its first steps in horticulture

Pest and disease monitoring is a labour-intensive process, which requires scouts to accurately evaluate plant and crop health as they move across the greenhouse, field or farm. AI-driven image analysis is aimed at helping to automate crop surveillance.

In horticulture, recent developments include a ‘robot scout’ equipped with near-infrared image cameras to detect powdery mildew and image analysis to predict bud and flower yields, and the IRIS Scout Robot. There is a remote pest-monitoring system, using machine learning (ML) to conduct an image analysis of pheromone traps, and a large number proposing drone and satellite imagery, as the basis of future crop management.

More widespread use of smartphone applications to scan photos for signs of pests and diseases, often presented as ready, or nearly ready for prime time use.

Many have heard that Google’s image analysis is now better than humans at recognising cats and dogs in images, or that in breast cancer research, the AI-image analysis now detects cancer on mammograms with more efficiency and accuracy than expert radiologists. So surely, using image analysis to identify crop pests and diseases on photos taken with a smartphone can’t be that far off. Not so fast.

Reality paints a less rosy picture

Efforts to use image recognition technology in smartphones fall short of their promise to provide both a granular insight and actionable overview of farms and greenhouses.

As highlighted in a recent Scientific American article, statistics used to present how well image analysis works are often misleading. The most common ‘pairing test’,  which  tests the ability to compare two images and state which of the two has any pest or disease, gives much higher percentages for accuracy than an analysis of multiple images, with no knowledge of whether any of  the crops  has the pest or disease.

Using inaccurate or skewed results gleaned from AI as a basis for pesticide control can cause more damage than good, as illustrated by the issue of false positives.

Let’s envisage an imaging system giving a false positive for blight just five percent of the time, a very conservative figure even by the claims of accuracy of any current app. In a field full of blight, this wouldn’t pose a problem, but now let’s take a field that does not have any occurrence of the disease. If you took 2,000 images in that field you would get 100 positive results!

Does the farmer act on this result, or inspect the 100 “positive” locations to check whether they really do have this problem? Multiply this by the other pests and diseases that the image analysis system is also checking for and perhaps has even higher false-positive rates for, and you have the workings of a practical nightmare. The higher the number of false positives, the more resources are required to conduct independent verification of results—meaning all the gains of automation are lost.

Machine versus human

This approach also needs to be put in context. Studies comparing situations where there is either AI or no crop scouting technology at all do not paint a realistic picture, because in some cases there is already a system in place, which helps to record and analyse data collected by human scouts.

In the breast cancer research case, as tumors are not visible to the human eye, the doctors and AI are looking at the same image. In a greenhouse setting, however, image analysis is much less effective than human attention to detail. A scout can move their head and turn over leaves to see a problem from multiple angles and with a magnifying glass

Augmenting human skills with mobile technology – smartphones make people smarter

Farms and greenhouses still need people to walk around, open up the crop canopy, turn over leaves and use a magnifying glass where needed. This requires technologies that enable scouts to do their jobs more accurately, faster and to a greater result.

Smartphones will continue to be key—but not primarily as an AI tool. A more realistic and proven use of mobile applications is for data collection and mapping. Instead of using smartphones to take photos for AI to analyse, crop protection managers should empower scouts to use their inspection skills and record the results as they go.

Training plays an important role. Correct identification and scouring of pests and diseases, thorough knowledge of sampling protocol and the technique to speed up the process, are all required to harmonise the performance and accuracy of scouts across the entire farm. This is key for success.

AI may help guide scouts to correctly identify unknown pests or diseases, but most crop scouting is about keeping track of the distribution of a well-known set of pests and diseases.

Digital mapping and scouting technologies enable humans to glean new insights

If we couple data recorded by scouts with geographical information, the results create data sets, providing a clear audit trail for traceability and data visualisation options such as digital maps, charts and graphs—and other additional help  to easily identify unique and recurring problems and patterns and any  false positives.

Digital mapping is where pest and disease scouting technology meets human expertise to optimise outcomes. At Scarab Solutions, we see this every day. Clients use Scarab Precision crop pest and disease scouting and mapping solutions to provide a solid basis to pinpoint infestation hotspots, determine the right pesticide use or biological control agent and reduce crop losses through enhanced farm management.

As the data sets grow, crop protection managers can in some cases benchmark against pest and disease figures in their region, using anonymised data from other farms.

Not yet AI’s time to shine, but we will always need the human touch

While AI-driven image analysis remains a talking point in the industry, the technology has a long way to go before it can produce reliable, accurate and actionable use cases. Today, GPS-tracking, mobile data collection and interpretation tools are the most effective and lucrative technology solutions for pest and disease management for crops.

As horticulture undergoes a technological transformation, artificial intelligence should not be seen as a substitute for existing processes, but as an extension of human intelligence. AI-driven image analysis will come with drones and robots in some settings, but that is a story for another day.

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