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How can flying insects and drones tell up from down?
Date:October 20, 2022Source:CNRSSummary:For proper operation, drones usually use accelerometers to determine the direction of gravity. Scientists have now shown that drones can estimate the direction of gravity by combining visual detection of movement with a model of how they move. These results may explain how flying insects determine the direction of gravity and are a major step toward the creation of tiny autonomous drones.Share:
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While drones typically use accelerometers to estimate the direction of gravity, the way flying insects achieve this has been shrouded in mystery until now, as they have no specific sense of acceleration. In this study, a European team of scientists1 led by the Delft University of Technology in the Netherlands and involving a CNRS researcher has shown that drones can assess gravity using visual motion detection and motion modelling together.
To develop this new principle, scientists have investigated optical flow, that is, how an individual perceives movement relative to their environment. It is the visual movement that sweeps across our retina when we move. For example, when we are on a train, trees next to the tracks pass by faster than distant mountains. The optical flow alone is not enough for an insect to be able to know the direction of gravity.
However, the research team discovered that it was possible for them to find this direction by combining this optical flow with a modelling of their movement, i.e. a prediction of how they will move. The conclusions of the article show that with this principle it was possible to find the direction of gravity in almost all situations, except in a few rare and specific cases such as when the subject was completely immobile.
During such perfect stationary flights, the impossibility of finding the direction of gravity will destabilize the drone for a moment and therefore put it in motion. This means the drone will regain the direction of gravity at the next instant. So these movements generate slight oscillations, reminiscent of insect flight.
Using this new principle in robotics could meet a major challenge that nature has also faced: How to obtain a fully autonomous system while limiting payload. Future drone prototypes would be lightened by not needing accelerometers, which is very promising for the smallest models of the size of an insect.
Though this theory may explain how flying insects determine gravity, we still need confirmation that they actually use this mechanism. Specific new biological experiments are needed to prove the existence of these neural processes that are difficult to observe in flight. This publication shows how the synergy between robotics and biology can lead to technological advances and new biological research avenues.
Notes
1 This research results from a European collaboration between two laboratories: the Micro Air Vehicle Laboratory at the The Faculty of Aerospace Engineering at the Delft University of Technology in the Netherlands and the Institut des Sciences du Mouvement (CNRS/Aix Marseille Université) in France.
Story Source:
Materials provided by CNRS. Note: Content may be edited for style and length.
Journal Reference:
Guido C. H. E. de Croon, Julien J. G. Dupeyroux, Christophe De Wagter, Abhishek Chatterjee, Diana A. Olejnik, Franck Ruffier. Accommodating unobservability to control flight attitude with optic flow. Nature, 2022; 610 (7932): 485 DOI: 10.1038/s41586-022-05182-2
CNRS. “How can flying insects and drones tell up from down?.” ScienceDaily. ScienceDaily, 20 October 2022. <www.sciencedaily.com/releases/2022/10/221020130254.htm>.
Machine learning has now been used to identify important pests that can ravage vegetable crops, according to work published in the International Journal of Wireless and Mobile Computing.
Changzhen Zhang of Kaili University in Guizhou, Yaowen Ye, Deqin Xiao, Long Qi, and Jianjun Yin of the South China Agricultural University in Guangzhou, China point out that effective pest control requires knowledge of the species affecting the plants and the level of infestation. The team has used a so-called “bag-of-features” model to develop an automatic pest monitoring system has been. They explain that their approach combines remote information processing technology and machine vision technology.
The proposed system can be implemented in a vegetable crop field to monitor four major pests: Phyllotreta striolata (the Striped Flea Beetle, a pest of brassicas), Frankliniella occidentalis (the invasive Western Flower Thrips, feeds on some 500 or more different species of vegetable, fruit, and flower), Bemisia tabaci (the Tobacco White Fly, which affects tomato and other related plants), and Plutella xylostella (the diamond-back moth, a pest of cruciform crops).
The team demonstrated an error rate of less than 10% when compared with detection and counting by people trained to spot the pests. Given that B. tabaci can reduce tomato crop yields by 60% so the detection of such species is critical to efficient and effective farming. The other species mentioned can all affect a wide variety of crops with devastating consequences when infestation is allowed to run rampant.
The team has demonstrated success in a controlled environment. The next step will be to test the system and improve its abilities in a more complex and realistic vegetable-growing environment.
More information: Changzhen Zhang et al, Rapid detection and identification of major vegetable pests based on machine learning, International Journal of Wireless and Mobile Computing (2022). DOI: 10.1504/IJWMC.2022.124813
Hi-tech trapping is helping growers to home in on invasive pests and reduce reliance on chemicals.
The technological fly trap uses the same fingerprint ID as a smartphone to detect specific pests and was designed to help manage Australian fruit fly.
The device uses a traditional lure to attract the fruit fly into the chamber, but it’s what happens when the pest insect is inside that sets it apart from a typical fly trap.
Nancy Schellhorn, chief executive of Rapid Aim, the company behind the sensing trap says as the insect entered the traps it interacts with sensors.
“And it’s the insect’s size, shape and behaviour that we then write algorithms to identify and detect it to know whether it’s what we’re interested in, or separate it out for the insects that enter the device that we don’t care about,” she said.
“Then there’s cutting-edge computing on board, and then that information is sent to the cloud.
“The information is streamed then in real time to the grower to their mobile app, so they can see exactly what’s happening with pests on their farm.”
The data collected can be used to target specific areas of crops for fruit fly.(Supplied)
Technology beating pests
David De Paoli uses the sensor trapping system on his chilli farm in Bundaberg, Queensland.
“I love technology,” Mr De Paoli said.
“My background is probably more engineering than farming, so if it’s out there, I’ve got to have it.”
David De Paoli started farming and exporting chillis 25 years ago.(ABC Landline: Courtney Wilson)
AustChilli is the biggest chilli farm in the country and one of the biggest suppliers of non-perishable chilli and avocado products to South-East Asia.
But growing crops in Bundaberg comes with some challenges. The Queensland fruit fly is an invasive pest that’s very active in the area.
Mr De Paoli says the introduction of sensing traps across his farming operation has vastly changed pest management practices.
“It gives us a much more proactive, not reactive, application to controlling fruit fly,” he said.
“We can see them in real time; every time a fly flies through a trap, we know ‘Hey, there’s 10 over in that corner, but there’s 50 in that corner’.”
This information allows the grower to target where and when they spray for fruit flies. The hope is that knowledge may lead to a reduction in chemical use, as its application can be more precise.
Mr De Paoli says he loves technology and how it helps to keep his farm on track.(ABC Landline: Courtney Wilson)
“They never attack the whole field,” Mr De Paoli said.
“They always start in a corner, and that’s where we’ve got to go and get them before they spread and have parties.”
Manual traps not accurate
Traditionally, fruit flies were managed through manual trapping and monitoring — a system that was both highly labour-intensive and not particularly accurate.
Isaiah Gala, an agronomy assistant at the AustChilli farm, says previously they used containers with a pheromone to attract the pests.
AustChilli is the biggest chilli farm in Australia.(ABC Landline: Courtney Wilson)
“It would take a couple hours, and I’d just manually count them out, one by one,” Mr Gala said.
“Now we can just click on a trap and Google Maps comes up, and it shows us exactly where it is.
“For example, in this one last week, we had 53 fruit flies on our Douglas farm, and we had 141mm of rain, and that number then tripled.”
The science behind becoming better farmers may start small, but it has the potential for a big impact.
Ms Schellhorn says a lot of chemical spray is wasted.
“In the US, about the equivalent of 230 jumbo jets full of pesticide gets sprayed across the landscape every year,” she said.
“But only about 0.003 per cent ever hits the target.”
Nancy Schellhorn is a former CSIRO scientist who specialises in insect ecology.(ABC Landline: Courtney Wilson)
Detecting other pest species
Beyond fruit flies, the technology captures and models behaviours to provide the data for the detection of other pest species.
“For most growers, there are usually one to three key pests that cost them the most money,” Ms Schellhorn said.
“So, for example, with apple and pear, it’s fruit fly and then codling moth. And so we are now adding codling moth into our layers of detection as well.”
The next step in the research is to move beyond trapping — to put the pest to work to kill others of its kind.
Growing chillies is a big business in Bundaberg, Queensland.(ABC Landline: Courtney Wilson)
“With our new Gen 2 product, we are no longer trapping pests,” Ms Schellhorn said.
“What happens is the pest comes in, it’s attracted to a lure. Once it comes into the chamber, it starts to pick up biocontrol. The biocontrol could be a spore, a fungal spore.
“It carries the spores out, so it gets detected on exit. But now it’s providing biocontrol for the farmer because it will go and mate with a female and it will be releasing those spores.”
It’s set to be rolled out in Queensland’s Lockyer Valley this October, and the first target is the invasive and extremely costly fall armyworm.
“So we’re super excited because it’s now bio digital,” Ms Schellhorn said.
“We’re on a mission to reduce the chemical intensity of agriculture, and we know that we have the technology and solutions and a new paradigm shift that allows us to do that.”
Date:February 22, 2022 Source:University of Copenhagen – Faculty of Science Summary: Insect populations are plummeting worldwide, with major consequences for our ecosystems and without us quite knowing why. A new AI method is set to help monitor and catalog insect biodiversity, which until now has been quite challenging.Share:
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Insect populations are plummeting worldwide, with major consequences for our ecosystems and without us quite knowing why. A new AI method from the University of Copenhagen is set to help monitor and catalogue insect biodiversity, which until now has been quite challenging.
Insects are vital as plant pollinators, as a food source for a wide variety of animals and as decomposers of dead material in nature. But in recent decades, they have been struggling. It is estimated that 40 percent of insect species are in decline and a third of them are endangered.
Therefore, it is more important than ever to monitor insect biodiversity, so as to understand their decline and hopefully help them out. So far, this task has been difficult and resource-intensive. In part, this is due to the fact that insects are small and very dynamic. Furthermore, scientific researchers and public agencies need to set up traps, capture insects and study them under the microscope.
To overcome these hurdles, University of Copenhagen researchers have developed a method that uses the data obtained from an infrared sensor to recognize and detect the wingbeats of individual insects. The AI method is based on unsupervised machine learning — where the algorithms can group insects belonging to the same species without any human input. The results from this method could provide information about the diversity of insect species in a natural space without anyone needing to catch and count the critters by hand.
“Our method makes it much easier to keep track of how insect populations are evolving. There has been a huge loss of insect biomass in recent years. But until we know exactly why insects are in decline, it is difficult to develop the right solutions. This is where our method can contribute new and important knowledge,” states PhD student Klas Rydhmer of the Department of Geosciences and Natural Resource Management at UCPH’s Faculty of Science, who helped develop the method.
Advanced artificial intelligence
The researchers have already developed an algorithm that identifies pests in agricultural fields. But instead of identifying insects as pests, the researchers have been able to develop this new algorithm to identify and count various insect populations in nature based on the measurements obtained from the sensor.
“The sensor is a bit like the wildlife surveillance cameras used to monitor the movements of larger animals in nature. But instead of snapping a photo, the sensor measures insects that have has flown into the light source. The algorithm then uses the insect’s wingbeat to identify them into different groups,” explains Assistant Professor Raghavendra Selvan of the Department of Computer Science, who led the development of the artificial intelligence used in the sensor.
The algorithm distinguishes insects by their silhouettes when their wings are folded out, as it is only then that their physical differences become most apparent. It then compares the silhouettes of different insect recordings, and puts similar silhouettes into the same group which can then be used to determine the insect that most likely flew through the light beam.
Prototype to be released in spring
When insects emerge in full force come spring, scientists will be using the initial prototype to venture out into nature and collect real-world data.
Until now, researchers have tested the algorithm and artificial intelligence using a large image database of insects recordings obtained in controlled conditions and some real-world data, where results have been promising.
“We will test the sensor in different landscapes, including heathland, forests and agricultural areas, to see how it works out in the real world. But also, to feed the algorithm more data, so that it can become even more accurate,” says Raghavendra Selvan.
According to the researchers, their invention makes it possible to monitor many geographical areas more thoroughly than has been possible in the past. At the same time, the invention makes it less resource-intensive to keep a close eye on insects, which make up 80 percent of all terrestrial animal species.
“Today, it is impossible to afford the kind of monitoring needed to gain a more precise overview of how our insects are doing. This sensor only needs humans to place it out in the wild. Once there, it begins collecting data on local insect populations,” concludes Klas Rydhmer.
Background:
Insects are the largest, most diverse group of described animal species on Earth. They make up about 80% of all terrestrial animal species on the planet.
It is the first time that this artificial intelligence method, known as Variational Auto Encoder (VAE), is being used to take inventory of insect biodiversity.
Using an optical signal from an infrared sensor, the algorithm is able to decode insects flying through a light beam.
University of Copenhagen – Faculty of Science. “Insect wingbeats will help quantify biodiversity.” ScienceDaily. ScienceDaily, 22 February 2022. <www.sciencedaily.com/releases/2022/02/220222135250.htm>.
What’s the Place of Technology in the Fall Armyworm Crisis? Photo credit: G. Goergen, IITA
This post was written by Ellen Galdava of FHI 360 and Kwasi Donkor of USAID.
Everyone from amateur gardeners to agricultural experts know that pest management is one of the most important aspects of good agricultural practice. This remains true for smallholder farmers. For smallholder farmers who often borrow money for seed and equipment before a harvest, a pest outbreak can not only destroy a harvest, it can also mean serious financial setback. Better equipping smallholder farmers to manage pest outbreaks will lead to stronger crop yields and increased food security. However, this is easier said than done.
What is the Fall Armyworm and How Bad Is It?
In 2016, the fall armyworm, a pest native to the Americas that can demolish a large number of crops, arrived as an invasive species in Africa. A smallholder farmer in Africa is already saddled with everyday challenges that range from weather to accessing financial services. The fall armyworm outbreak further endangered food stability and increased the hurdles of everyday life.
Compared to other pests, the fall armyworm is especially damaging because it eats both the vegetative and reproductive parts of plants. The destructive nature of the fall armyworm makes it critical and expensive to exterminate. Brazil, for example, spends up to $600 million annually fighting it. And while scientists and farmers in the Americas are knowledgeable about and prepared for fall armyworm, its appearance in Nigeria in January 2016 took African farmers by surprise. While they had seen a local armyworm before, they had never encountered this invasive species. Nearly two years after initially being spotted in Nigeria, with the help of its quick reproductive cycle and unique migratory capacity, in December 2017, the fall armyworm had spread to 38 other African countries.
In trying to resolve development challenges rapidly and efficiently, development practitioners increasingly turn to technology to produce quick, efficient and scalable solutions. For that reason, mSTAR and Digital Development for Feed the Future (D2FTF) decided to explore the possibility of developing a mobile application for pest management. The goal was to enable smallholder farmers to diagnose and find treatment for pests. The mobile application would enable farmers to quickly identify the pest and decide on the treatment plan. Before investing in the application, the team conducted a landscape assessment of existing technologies and interviewed farmers and extension workers in Ghana to identify the feasibility of such a high-tech intervention.
Diagnostic vs. Management Support Technology
While analyzing existing pest management technologies, it became apparent that agriculture development organizations generally use two types: diagnostic and management support technology. An example of a diagnostic technology is Plantix, a machine learning application, which assists farmers and extension workers to identify pests. An example of management support technology is using WhatsApp messaging groups as a management tool to enable trained extension workers, plant doctors and farmers to diagnose plant infections and determine the best pesticide for the specific pest.
Most organizations working on pest management have been focused on using management support technology solutions rather than diagnostic technologies. For example, USAID/Ghana’s Agriculture Development and Value Chain Enhancement (ADVANCE) project, which improves the competitiveness of agricultural value chains, implemented pheromone traps and GIS mapping to model the movement of fall armyworm. ADVANCE employees, in partnership with extension officers, collected and analyzed data from 57 traps to track the spread of fall armyworm. Also in Ghana, CABI, an organization that supports farmers, uses WhatsApp as a place for plant doctors and extension workers to share information and ask questions about pests. In Zambia, CABI created Pest Risk Information Service which notifies plant doctors when there is a risk of pest infection.
While implementing strong management technologies, CABI and USAID/Ghana’s ADVANCE project have begun to implement diagnostic systems as well. These include hotlines, training extension workers and plant doctors on how to identify pests, and recommending the best solutions to manage them. CABI’s WhatsApp groups have been used by a limited number of extension workers and plant doctors as a source of identifying pests through picture sharing. While the adoption of these support technologies has expanded, most organizations researched have not used more sophisticated diagnostic support technologies, such as Plantix.
Development organizations have also been using low-tech solutions to provide more information on managing pests. With the recent invasion of fall armyworm, they now focus especially on spreading fall armyworm information. In many cases, however, these solutions were reactive to the invasion and not proactive. For example, Farm Radio International, which uses radio programming to share information on agricultural best practices, began integrating pest management for fall armyworm into programming only after the outbreak in Ghana in May 2017. This was nearly a year and a half after the initial outbreak in Nigeria. Similarly, Farmerline, Esoko andViamo began sharing information on pest management and fall armyworm via messages in local languages after the outbreak.
A Continental Solution that Combines Both?
With the understanding that most technology solutions used by agricultural development organizations revolve around management, mSTAR and D2FTF decided to explore the feasibility of the development of a mobile application that would both diagnose and provide a treatment plan. However, during the research it became apparent that with the spread and rate of the fall armyworm outbreak, interventions need to be deployed not only at the country level but on a continental level. Therefore, D2FTF decided to launch the Fall Armyworm Tech Prize instead of developing a mobile application specifically for Ghana. This prize will assist USAID in creating innovative digital tools and approaches to track the path of the pest, communicate interventions to smallholders and relay information to agriculture decision-makers and agents. The Fall Armyworm Tech Prize opened for applications on March 28, 2018. To learn more, follow the link here.
Munni Akhter, a villager based in the Patuakhali district, receives training on the Plantix — Your Crop Doctor application. Photo Credit: Atanu Bhattacharjee, DAI.
“We are devoting a larger and larger share of our budget to humanitarian assistance, because there are so many more climate-related disasters happening,” Power explained in a recent interview with National Public Radio’s (NPR) Ari Shapiro. “A whopping 1.7 billion people, in fact… since 2000, have been affected by climate-related disasters.”
The last seven years have been some of the hottest on record, and despite worldwide efforts to mitigate climate change, temperatures are expected to climb dramatically in coming years. “We will help support more than 500 million people to adapt to climate change through efforts like scaling drought-tolerant agriculture, establishing early-warning systems for storms and creating new insurance schemes that can support people when their harvests fail or livestock perish,” Power announced in her “A New Vision for Global Development” speech.
The changing climate will have a particularly profound impact on agricultural production and food security. “Even as the global population continues to grow and the climate crisis threatens more corners of the world each year, it seems each passing day, we still have an opportunity to harness agricultural research and innovation to grow the pipeline of crop varieties that can protect the world’s food supply,” Power remarked. Farmers around the world are already suffering the consequences of severe climate change, including drought, erosion, flooding, crop disease and falling crop yields. In the face of such vast and interrelated threats, development practitioners everywhere are looking to the frontiers of science and technology to develop and pilot innovative solutions.
“As smallholder farmers across the globe navigate the increasingly dire challenges presented by climate change, artificial intelligence offers a solution to help support decision-making at the farm-level,” posited Araba Sapara-Grant, a digital specialist with DAI. “This is critical because, as we know, with a changing climate comes increasingly volatile weather patterns that can — and have — forced farmers to make increasingly risky decisions on how best to use resources like water and inputs such as seeds and fertilizer,” she explained.
The ever-growing field of digital agriculture must turn its attention to cutting-edge emerging tools and technologies. Aware of artificial intelligence’s (AI) potential as an important tool in agricultural adaptation, USAID’s Feed the Future Bangladesh Digital Agriculture Activity (BDAA), as a part of DAI’s Digital Frontiers Project, recently supported the pilot of Plantix, a highly specialized and AI-driven smartphone application for farmers and extension workers in Bangladesh.
“In Bangladesh right now, we have the second generation of farmers. If we look at the previous generation of people, they knew how to farm,” said Tasnuba Sinha, BDAA digital tools specialist. “They were experts in the sense that they could look at the sky and they could understand whether it would rain or what the weather would be like. But now, with climate change, the weather is not as certain as it used to be. Agriculture right now is a bit unpredictable, and what Plantix can do is actually help you instead of the usual trial and error.”
When downloaded for the first time, the free “Plantix — Your Crop Doctor” application allows users to select their preferred language, location, crops of interest and growing conditions. Plantix can provide users with customized recommendations for the amount of water, light, pesticide and fertilizer necessary for a successful yield. The application interface also acts as a weather monitor, providing farmers with updates on rainfall, temperature and other sudden environmental changes pertinent to the user’s crops. The application’s most unique feature, however, is its remarkable ability to diagnosis a pest-infested, disease-ridden or malnourished crop from a simple picture.
“You can take a picture of a crop or plant, and the app, using its AI technology, will assess, and it will tell you what the problem is with the plant and how to take care of it,” detailed Sinha. “If it’s suffering from a disease, if it’s underwatered or it’s overwatered, or if it [needs] fertilizer…” the application is capable of diagnosing over 400 different ailments of 60 different crops and prescribing recommended solutions or treatments. When delivering advice, the Plantix application also considers data from a soil map and reports of any previous cases of crop disease in the user’s area.
If the response received from the AI feature is not sufficient, however, Plantix users also have the opportunity to connect with local experts and discuss potential remedies through the “community tab” on the application. “They have another tab in which anyone can ask the question, and then the active group members answer the question… It’s very active… I tested that [feature myself], and I posted a few questions and got answers in a couple of hours,” Sinha confirmed.
The community tab also allows users to directly engage with local agricultural experts and extension officers. Typically, agricultural extension officers are responsible for providing services to as many as 800 different farming communities, significantly limiting their ability to visit all farms in need of assistance. This circumstance usually shifts the travel obligations and costs to the farmers and their families. Thus, a free mobile application provides a great alternative to traveling to the nearest agricultural extension office, a process both inefficient and expensive for the farmer. “For people who reside in a very rural area, they have to travel a distance in order to reach these extension offices…” shared Sinha. “There’s also the cost of transportation to consider and, also, in regard to time, because for some it could mean like a half day’s journey.”
Changing weather patterns experienced across Bangladesh in recent years have led to an increased dependency on agricultural expertise — in person or digitally. Due to climate change, farmers are also having to contend with completely new crop diseases, many of which even agricultural extension workers are not yet familiar. If unable to predict the weather patterns and anticipate or even identify crop disease, many farmers who are unable to access real-time, collectivized information have had to resort to trial-and-error strategies. This, BDAA experts explain, is ineffective given the speed and magnitude of climate change. A.S.M. Monirujjaman, a DAI expert working on the ground to socialize the Plantix application, commented, “The recent seasonal shift in the Barishal region of Bangladesh… relabeled the October-November months as a part of the rainy season rather than winter, which put additional pressure on rural farmers.”
The Plantix tool has also proved uniquely beneficial for rural women. “Women can also use this Plantix app, because in rural areas lots of women are involved in homestead gardening… And [in] the cultural context of Bangladesh, women are not allowed to go far from their homes,” said BDAA technical expert Sutapa Biswas. “So, if they use the Plantix app, they can gain similar benefits [to visiting an agricultural extension office].” By working within the sociocultural framework of Bangladesh, the Plantix tool provides women with direct access to agricultural resources that may not have been previously available due to social or familial restrictions on traveling or conducting business as a woman.
Agri-input businesses have also adapted Plantix’s features to their unique needs and services. Agri-input businesses are engaging with the application to improve their reputation among customers and to increase seed sales by “identify[ing] what their clients suffer [from] and what solutions they need to provide their customers in the future,” elaborated Monirujjaman. The community tab feature allows agri-input business owners and operators to establish legitimacy, build relationships with local farming communities and increase sales of seeds, fertilizers, pesticides, etc. While the tool was not explicitly created for this use, it seems that the tool can be easily adapted to serve the wider agricultural value chain.
“The majority of farmers in Bangladesh face crop losses due to pest and disease, lack of knowledge, overfertilization or soil nutrient deficiencies and climate change,” reported BDAA. “They depend on other farmers or experts like extension officers… to resolve the problems. Most of the time they cannot reach the experts in time.” By using the Plantix image recognition and intelligent automation technology, farmers, homestead gardeners and other value chain actors can receive immediate access to highly accurate diagnoses and recommendations for treatments and corrective measures.
“Through AI-enabled mobile phone applications, farmers and digitally-enabled extension officers have the opportunity to receive time-sensitive decision support on issues from how best to treat crop diseases (some of which are spreading or increasing in severity due to changing climates),” reiterated Sapara-Grant. Since the pilot began in Bangladesh, Plantix has demonstrated this capacity to protect and prepare the country’s next generation of farmers for what could be challenging days ahead.
AI technologies are still novel in digital agriculture, and experts call for further research and refinement of AI-enhanced digital tools. “While the international development community must still address barriers to the use of AI, such as unavailable or incomplete data sets, AI is a critical tool in helping smallholder farmers combat the effects of climate change and maintain productivity through forthcoming shocks,” remarked Sapara-Grant. While AI technology alone cannot mitigate climate change and foster resilience in farming communities, its potential for high impact, sustainable change and scalability cannot be overlooked.
Robot lasers weeds from the fields without herbicide
Seattle autonomous robotics company Carbon Robotics aims to confront the multi-billion dollar global herbicide market with its laser-armed weed elimination robot. The machine, named “Bud”, rolls through farm fields using artificial intelligence to discern weeds from crops and using a high-power laser to kill the weeds. This will enable farmers to cultivate crops with less herbicide and reduced labor, improving crop yields and saving money.
Bud’s robot brain is an Nvidia AI processor that gathers information from a dozen high-resolution cameras to feed its crop and weed computer vision models. Bud carries lighting so that it can illuminate the scene to let the cameras spot weeds at night.
UTA to use tiny sensors to track bugs and combat infestations
The University of Texas at Arlington is helping develop tiny sensors that attach to insects, tracking their movements and life cycles in an effort to combat infestations and increase farm production.
The project is led by computer science Professor Gautam Das and electrical engineering Professor Wei-Jen Lee, working with the U.S. Department of Agriculture (USDA). The $122,057, USDA grant runs through June 2023.
“This is a unique approach to the problem of infestations, and we hope to produce results that will allow us to expand our research later,” Das said. “The use of artificial intelligence in agriculture is a growing field, and this is just one small example of how it can make an impact.”
Das will work to develop a sensor that can be attached to the tarnished plant bug, a plant-feeding insect known to ruin crops of small fruits and vegetables. The sensors would relay information to a base station that tracks the insect’s coordinates and movements. Das and Jianzhong Su, professor and chair of mathematics, will perform data analysis to find patterns.
Lee will work on a radio-frequency identification (RFID) tag for the insects and use multiple readers to pinpoint their locations. A wireless sensor network will transmit data for analysis.
Wei-Jen Lee The researchers must also develop a way to provide power to the sensor, possibly by tapping into the insect’s movements. The team is working with University of Central Florida mechanical engineering Assistant Professor Wendy Shen.
“Insects can positively or negatively affect agricultural quality and production,” Lee said. “Understanding their behavior is an important step to taking advantage of their benefits and mitigating potential damages. Applying advanced sensor technologies and artificial intelligence will have a profound impact on the future development of agriculture.”
Jianzhong Su The insects will be released into special rooms maintained by the USDA that have large spaces where plants are grown, and insects can fly around in a controlled environment. This way, the team can test its technology without worrying about negative impacts on actual crops.
Since 2020, the USDA and the National Science Foundation have poured millions of dollars into artificial intelligence research in agriculture. Su has led a university-wide research collaboration with the USDA since 2018 with researchers from the Colleges of Science and Engineering, through funding from an earlier USDA Hispanic Serving Institution grant focused on agriculture data and Internet of Things.
“We have built a good relationship with the USDA, and we are happy that they have provided funding for this project,” Das said. “Hopefully, this is the beginning of a series of opportunities.”
This is what you will see from an automated camera trap: a photo of sticky paper delineating target pests, in this example codling moth, provided by CropVue Technologies of British Columbia. Camera traps, manufactured by several companies but typically packaged with a service that includes weather sensors, artificial intelligence and entomology expertise, are becoming more common in tree fruit orchards.(Courtesy CropVue Technologies)
Editor’s note: Teah Smith did not collaborate with Washington State University in her 2015 test of Semios camera traps, as reported in the print version of the October 2021 issue. This online version of the story has been corrected. Good Fruit Grower regrets the error.
The combination of increasingly affordable technology and improved artificial intelligence has led to a rise in automated camera traps for growers considering new precision pest control tools.
Camera traps allow users to check pest pressure over hundreds of acres from a computer, as often as needed, instead of driving through row after row to check by hand maybe once a week, while artificial intelligence recognizes key insects and alerts growers when target bugs are caught.
Camera traps are an important tool for the future in the management of many pests, including codling moth, especially since market trends have the industry shifting toward organic methods, said Chris Adams, an Oregon State University assistant professor of tree fruit entomology and chair of the Washington Tree Fruit Research Commission codling moth task force.
“What we have left is to make smarter and more timely decisions, and I think camera traps help us do that,” Adams said.
Adams has trials underway with two camera trap vendors, Semios and Trapview.
The traps themselves catch insects on old-fashioned sticky paper, and an internal camera uploads one or more photos in the wee hours of each morning. But the traps are provided as part of a service plan that includes data from the traps, artificial intelligence to identify pests, miniature weather stations and pheromone emitters, all connected remotely. Vendors typically have a team of entomologists to monitor and provide quality control for the artificial intelligence.
Three examples
Good Fruit Grower interviewed representatives from Semios and CropVue Technologies, both based in Vancouver, British Columbia, and Trapview, a Slovenia-based company with North American offices in Vancouver, Washington. Other examples include Isagro of Italy, Adama of Israel, FarmSense of the United Kingdom, DTN of Minnesota and Pessl Instruments of Austria.
Semios has about 10,000 camera traps in specialty crops throughout the world, working with tree fruit growers since 2015, said James Watson, director of sales and marketing.
Trapview has thousands of clients in specialty crops globally but has been operating in the United States only since 2018, said Jorge Pacheco, the North American managing director. So far, the company has more presence in California vegetables than Northwest tree fruit.
CropVue Technologies entered the arena in 2019. The company currently supports about 5,000 acres with a few Washington pilot growers but is poised for a full commercial launch next year, said Terry Arden, CEO.
Left: CropVue camera traps come with a solar panel to charge the camera battery. Center: Camera traps built by Trapview, based in Slovenia, come with self-scrolling replacement sticky paper, a solar panel and a weather node. Right: A Semios camera trap hangs in a Washington apple orchard.(Left to right: Courtesy CropVue Technologies, Courtesy Trapview, Courtesy Semios)
All three use similar technology but different business models.
Semios directly works with and sells to growers. Its software acts as a one-stop shop, which will allow growers to pull data from companies Semios acquired over the summer, including the company that owns the ApRecs online spray recommendation writing tool. The company hangs the traps, replenishes liners, installs the tools, remotely monitors the functions and maintains all equipment.
Trapview and CropVue distribute through suppliers and management companies such as Wilbur-Ellis, G.S. Long or Chamberlin Agriculture.
“They have a direct connection with those growers with other inputs, not just pest monitoring,” said Pacheco of Trapview.
CropVue’s Arden agreed. “The distributors have long-term relationships with growers,” he said.
The trap companies also differ in connectivity. Hinging their future on the build-out of cellular IoT (Internet of Things) service, CropVue and Trapview install a network in which each device independently uploads data to the cloud.
Semios, which built its infrastructure before IoT, relies on a “meshed network” with repeaters that talk to a gateway, which in turn uploads bigger lumps of data to its cloud. However, the company has already deployed IoT in some spots.
Camera traps work with a network of in-orchard weather monitors and mapping software, shown in this screenshot from Semios, to help growers make pest control decisions.(Courtesy Semios)
Costs
Semios and Trapview declined to discuss pricing because each orchard requires a unique level of service. Trapview is still in the process of setting its subscription rates.
CropVue is shooting for roughly $25 per acre per year, assuming one trap and one canopy weather node for every 10 acres, but that’s flexible. Washington State University researchers recommend a ratio of one trap per 2.5 acres for codling moth. Other entomologists’ suggestions range from one to five.
Sold individually, traps run $400 to $1,000 per season per trap, enough to be a barrier to entry, said Pete McGhee, research and development coordinator for Pacific Biocontrol Corp. in Corvallis, Oregon, and a former Michigan State University researcher who has worked with camera traps.
But the price will come down and the technology will continue to improve in all the cameras. Resolution is getting sharper, artificial intelligence is getting better at recognizing species and processing power continues to increase, McGhee said.
The main benefit to the camera traps and surrounding services is recognizing the threshold early in the season for “setting the biofix,” McGhee said — triggering the phenology model that will give predictive advice for when to spray.
After that, growers or consultants can monitor progress.
His concern is that many of the vendors have not publicly validated their approaches against the growing degree-day models and thresholds based on 30 years of university research.
All three companies in this story say they have run trials with university researchers. Meanwhile, in addition to running trusted models, their own vast datasets and artificial intelligence can refine the models and apply them uniquely to each orchard and its microclimate.
“They are changing the way decisions are made,” said Watson of Semios.
One grower’s take
Teah Smith, entomologist and agricultural consultant for Zirkle Fruit, is a fan of camera traps. The company is based in Yakima, Washington, though she is based in the Wenatchee area.
Smith is responsible for steering pest control over 6,500 acres of orchards. She used to send her team of scouts to check traps every week. Camera traps save them time for other things, she said.
She first experimented with Semios camera traps in 2015 on two 100-acre orchards. She hung standard delta sticky traps next to the automated traps, alternating locations each week, and she found comparable catch rates. She also double-checked the computer results with her visual inspections and found similar data.
Convinced, she expanded the use of camera traps for monitoring leafroller and codling moth over a lot more Zirkle acreage. If the company experienced oriental fruit moth pressure, she would use the traps for that pest, too, she said.
Smith also believes she gets more accurate pheromone emission and timing with camera traps, hung one per eight acres.
She has experienced some data processing limits in areas. Another challenge is the rise of sterile insect release. Currently, somebody or something has to smoosh a moth to find out if it’s irradiated or not, and the camera traps can’t do that.
However, technology will overcome those problems, she said.“It’s definitely the wave of the future.”
Ross Courtney is an associate editor for Good Fruit Grower, writing articles and taking photos for the print magazine and website. He has a degree from Pacific Lutheran University. — Follow the author — Contact: 509-930-8798 or email.
Just as meteorologists incorporate data into models to forecast weather, ecological scientists are using data to improve forecasting of environmental events – including pest or pathogen spread. – Photo: NCSU/Vaclav Petras
North Carolina State University researchers have developed a computer simulation tool to predict when and where pests and diseases will attack crops.
The computer modeling system is called “PoPS”, for the Pest or Pathogen Spread Forecasting Platform. Working with the U.S. Department of Agriculture’s Animal and Plant Health Inspection Service, the North Carolina State University (NCSU) researchers created the tool to forecast any type of disease or pathogen, no matter the location.
Model improves by adding data
The system works by combining information on climate conditions suitable for spread of a certain disease or pest with data on where cases have been recorded, the reproductive rate of the pathogen or pest and how it moves in the environment. Over time, the model improves as natural resource managers add data they gather from the field. This repeated feedback with new data helps the forecasting system get better at predicting future spread, the researchers said.
Increasing number of threats to crops
According to NCSU this tool can be put into the hands of a non-technical user to learn about disease dynamics and management, and how management decisions will affect spread in the future. The researchers say the tool is needed as state and federal agencies in the U.S. charged with controlling pests and crop diseases face an increasing number of threats to crops, trees and other important natural resources.
PoPS used to track 8 emerging pests and diseases
Researchers have been using PoPS to track the spread of 8 different emerging pests and diseases. They are improving the model to track spotted lanternfly, an invasive pest in the United States that primarily infests a certain invasive type of tree known as “tree of heaven.” Spotted lanternfly has been infesting fruit crops in Pennsylvania and neighboring states since 2014. It can attack grape, apple and cherry crops, as well as almonds and walnuts.
The study, “Iteratively Forecasting Invasions with PoPS and a Little Help From Our Friends,” was published June 3, 2021, in the journal Frontiers in Ecology and the Environment.FacebookTwitterEmailLinkedInPrint
Three main components causing the digital agriculture revolution
“The farming industry is undergoing a digital revolution. Thanks to the large-scale availability of sensors, cameras and other mobile and computing technologies that we could only dream of in the past, growers have great amounts of data at their disposal,” says Dr. Gajendra Pratap Singh.
Cause of the revolution In his view, there are three main components that are causing the digital revolution in agriculture. Firstly, there is the availability of affordable and portable sensors, that enable growers to monitor their crops more closely. Secondly, communication technology has allowed growers to be in closer contact with each other and with suppliers. But most of all, the massive availability of data analytics that the use of Artificial Intelligence (AI) technology allows for is helping to make smart decisions on time and increase productivity.
Gajendra Pratap Singh
Gajendra is Principal Investigator and Scientific Director at Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP) at Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise.
“AI technologies allow real-time interpretation of crop health data obtained from field sensors. Sensors in irrigation systems have been designed to provide water only when no rain is forecast, just to give one example. This both saves water and improves crop yield,” Dr. Singh explains.
The wealth of useful information that new technologies provide can also be used to breed resilient crops to withstand plant diseases. “In my view, AI technologies combined with sensor and mobile communication technologies have the potential to empower farmers like never before in history. This way, the huge amounts of data obtained from plants using novel sensors can help to increase farm productivity, develop new heat-tolerant varieties of crops, and to curtail the predicted food shortage due to climate change and population increase.”
Still room for improvements However, the industry is not there yet, Dr. Singh claims. “AI technology on its own is not powerful enough to boost agriculture. Collaboration is needed with sensor and communication technologies to render them useful. Right now, sensors only measure the morphology or appearance of the plants or environmental factors such as temperature. They don’t monitor the biochemical changes occurring inside the plants in real-time yet. But when a plant is stressed due to the lack of nutrients or proper light, it generates a wealth of biochemical information. Being able to use this information will help growers even further.”
And this is exactly what DiSTAP wants to achieve with their new, portable Raman sensors that measure data concerning nitrate stress, shade avoidance syndrome, and bacterial infections in plants within a few hours. “For that reason, we’ve developed nano-sensors that can measure plant hormones, giving vital feedback to the farmer on a daily basis. Thus, it is possible to monitor the health of each plant every day by measuring the plant itself and not the symptoms or the environment only. Also for vertical farms, it is technologies these nano-sensors combined with AI that have the potential to improve crop productivity several times.”
This is important, according to Dr. Gajendra Pratap Singh, as agriculture has a profound impact on every human being. “In Bangladesh, for example, just two days of heat in April this year destroyed more than 60,000 hectares of rice, affecting more than quarter-million farmers with losses of about US$40 million. The availability of mobile technology in the remotest parts of the world will allow universal participation of farmers in the digital revolution in agriculture.”
For more information: Dr. Gajendra Pratap Singh, Principal Investigator and Scientific Director Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP) Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore gajendra@smart.mit.edu distap@mit.edu www.smart.mit.edu
The University of Texas at Arlington is helping develop tiny sensors that attach to insects, tracking their movements and life cycles in an effort to combat infestations and increase farm production.
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