Crop health and protection is vitally important to all growers, data can play an instrumental part in optimising this within an IPM Programme. In this article, we talk about how data can help inform decision making and help manage your IPM programmes more effectively. 

"30MHz tech enables us to really understand what each individual crop is doing, and to respond accordingly." - Jonathan Zwinkels, Madestien

Using data to optimise your IPM programme
For example, vine weevil (Otiorhynchus sulcatus) is a key pest in ornamental plant production as well as a range of other crops. Adult vine weevil cut distinctive notches into leaf edges when feeding. Larvae feed on the roots of plants which then has profound effects on the development of the crop and causes serious plant damage.  A growers first line of defence in terms of products to apply are nematodes. 

There are two key treatments that are effective on vine weevil, Nemasys-L and Exhibitline Hb. Nemasys-L works down to soil temperatures of 5°C whereas Exhibitline Hb works at soil temperatures above 12°C. Knowing your soil temperature in real-time means growers can make a better-informed decision on which control option to implement.

To take this one step further the visualisation of this data needs to instantly convey this information. By displaying real-time, 24 hour and data trends growers can confidently make an informed decision. The below example visualises the differing optimum conditions for each nematode as gauges that change colour based on efficacy at a given temperature. This gives an instantaneous view of which product will offer the best control and ultimately be the most cost-effective. 

It is critical that biocontrol populations are established prior to the growth of pest populations. Trying to firefight with biological controls is expensive and often has poor results. However, it is not optimal to apply controls too early, and therefore it is important to strike a balance for population establishment timing. To a large extent, pest population development is driven thermally and as such we can make use of this and the literature on life cycles and population development to create informative dashboards to outline risk and therefore what levels of mitigation to undertake.

The above example outlines different developmental bands on a line graph. From this, we as an advisor can better understand at the current point in time which pest the environmental conditions will favour and therefore alter our biological control programmes and potentially rotational sprays of biopesticides accordingly.  
  
Further to this take for example growing degree models for the first flight of thrips. We could use this to inform decisions made by a grower and an advisor on when to start applications when to increase rates or even when to start looking to alter the makeup of the application dependant on which life cycle stage each bio is most effective at. Or as below, again make use of gauge widgets to suggest which control will work best at a given point in time. 

Data can also be used to optimise preventative controls for disease and the timing of applications of biopesticides for said diseases. For example, we have seen growers monitor humidity, airflow, leaf wetness etc and from this alter practices to decrease the potential risk from botrytis and downy mildew.  Tracking graphs are useful to outline the time period of risk and then from this make a more informed decision on what product to apply based on risk. For example, in times of low pressure rotating between more preventive/early curative products like Taegro and Romeo or at times of high risk using more curative conventional chemistry. This can help to reduce chemical inputs which will reduce residues but also help maintain the efficacy of the remaining chemistry.

What is next for technology when it comes to monitoring plant disease?
Presymptomatic disease prediction, the use of hyperspectral imaging and or predictive modelling stand to revolutionise the crop protection landscape, improving product efficacy and reducing risk. Hyper/multispectral imaging in tandem with machine learning and ultimately artificial intelligence will in time identify diseases far before the most trained human eye and predictive decision support systems will guide and inform growers on how best to defend their crops. We are not there yet, but digitising and developing technological capabilities now will put you ahead of the game tomorrow.  

A view from the growers:
"We use wireless sensor technology from 30MHz to monitor climate at canopy level. The data is used to support growing and crop protection decision-making. We're learning from it all the time." Libby Rowland, Research & Development Manager, Vitacress 

"Bringing our data sources together in one platform has made it possible to cross-reference data ex: VPD of basil leaf, or stress points on lettuce). We're able to graph the VPD data we get through 30MHz's data platform and understand it in the context of climate control, or ventilation positions. We can dig into the relationships between them, see the effects, and understand what needs to be changed to achieve our desired outcomes."-  Jonathan Zwinkels, Madestie