Archive for the ‘Pest diagnostics’ Category

How to diagnose a pest problem using the diagnostic tool

The Plantwise Knowledge Bank brings together plant health information from across the world. It includes a diagnostic tool, factsheet library, pesticide lists and pest alerts. For those seeking to diagnose a pest problem, the Plantwise Knowledge Bank’s Pest Diagnostic Tool is particularly useful, providing information to help identify the symptoms observed on a crop.

Tuta Absoluta © CABI

Diagnosing a pest problem

To be able to provide effective solutions to a plant health problem, it’s first important to diagnose a pest problem accurately. The diagnostic tool allows you to diagnose a crop problem through the symptoms observed and the part of the plant affected.

From the Plantwise Knowledge Bank home page, navigate to the diagnose a pest problem tile and click ‘identify a pest’.

Plantwise Knowledge Bank
Navigate to ‘Identify a pest’

Identify the pest problem

Once clicking on ‘identify a pest’, you are able to search by country or region from the drop-down list. The crop name is then typed into the search box and a list of suggested crops will appear to choose from.

Plantwise Knowledge Bank
Search by country and crop

Narrow down cause

The first step in narrowing down the cause of a plant health problem is to determine which part of the plant is affected by the pest. In some cases, several parts of the plant can be damaged, but to help diagnose the problem, the main part of the affected plant needs to be determined. The options provided in the tool include leaves, stems, whole plant, seeds, fruit, growing point, inflorescence, roots and vegetative organs.

Plantwise Knowledge Bank
Main part of affected plant selection

If the type of problem is already known it will help to narrow down your search further, otherwise a user would select ‘unsure’. The types of problems in the tool include mites, insects, fungi, nematodes and weeds.

The steps in the diagnostic tool mirror the plant clinic prescription form found on the data collection app.

Plantwise Knowledge Bank
Type of problem selection

Pest and disease results

Results from the diagnostic search are given as a list of possible pests or diseases, each with an image, and a link to a technical factsheet further describing the problem. The technical factsheet provides information on crop symptoms, preventative methods and effective solutions to the problem.

For further information on the pest or disease problem; the search tool on the Plantwise knowledge bank has over 15,000 pieces of content available for free.https://blog.plantwise.org/2021/08/12/what-is-the-plantwise-knowledge-bank/embed/#?secret=wMPC0G48cQ

Further reading

Contact us via email to share links to factsheets or any queries: plantwise@cabi.org

Visit the The Plantwise Knowledge BankPlantwise Knowledge Bankcrop healthdiagnostic searchdiagnostic supportplant healthCrop healthDevelopment communication and extension

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Digital Engagement and Training Helps Increase Agro-Dealer and Farmer Knowledge on Integrated Pest Management in East Africa

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Integrated Pest Management Innovation Lab

Aug 19, 2021

A group of people training with the Tanzanian Agricultural Research Institute (TARI)

This post is written by Sara Hendery, communications coordinator for the Feed the Future Innovation Lab for Integrated Pest Management

Given Tanzania’s diverse geographical landscape, it’s no surprise the country is among the world’s top 20 producers of vegetables. Nevertheless, farmers remain in search of ways to combat the pests and diseases that threaten crop yields every season.

Results of a survey conducted by Feed the Future Innovation Lab for Integrated Pest Management partners at the Tanzanian Agricultural Research Institute (TARI) show that the majority of Tanzanian farmers receive key knowledge on how to manage pests and disease not only from extension personnel, but often from agricultural supply dealers, or agro-dealers. While agro-dealers do carry valuable information, resources and inputs, the survey also shows that many agro-dealers have limited formal knowledge on vegetable production or protective measures for applying chemical pesticides.

To address these gaps, TARI began providing cohesive training to agro-dealers, farmers and extension officers on vegetable production and pest and disease management. Training covers such areas as Good Agricultural Practices (GAPs), Integrated Pest Management (IPM) and safe handling and use of agricultural inputs, including pesticides. Thus far, 500 participants have been trained in the Coast and Morogoro regions. The GAP training in particular helps farmers build capacity in reporting and record-keeping, assessing input quality and crop hygiene, and training in IPM provides information on bio- and botanical pesticides, pruning, developing seedlings in a nursery environment and how to apply pesticides with minimal body exposure.   

“Knowing that farmers receive their pest and disease management knowledge from agro-dealers provides us important insight into how to best reach farmers with up-to-date information,” said Dr. Fred Tairo, principal agricultural research officer at TARI-Mikocheni. “If we want farmers to adopt sustainable, climate-smart and productive inputs that might be outside of their typical use, an important pathway to reaching them is through the people that farmers already trust and are familiar with.” 

In a group of 69 agro-dealers surveyed, only 49 were registered and licensed to run agricultural shops. The 20 unregistered participants had received no formal training in crop production or pesticide safety and use, and most participants not only had no prior knowledge on how to dispose of expired pesticides, but did not sell bio-pesticides or chemical pesticide alternatives at their shops. Since registering as an agro-dealer can cost nearly $200, TARI is collaborating with the Tropical Pesticides Research Institute (TPRI), a regulatory authority for pesticides in Tanzania, to consider lowering the costs.  

TARI and the IPM Innovation Lab are increasing communication through digital platforms to reach more agricultural actors with safe and effective approaches to pest and disease management. A Kiswahili-based (Swahili) WhatsApp group named “Kilima cha Mboga kisasa,” or modern vegetable cultivation, currently shares information with 154 farmers, extension agents and agro-dealers in Tanzania who can use the app to cite crop threats and receive expert management guidance in return.

Participants post a picture or video of the crop problem for immediate diagnosis. Not only do agro-dealers in the group directly learn about farmers’ most pressing problems, but they can use the platform to market agri-inputs, including the IPM products they learn about through the platform. 

“Even if members of this group do not necessarily follow up with formal training we offer, this is a low-stakes knowledge-sharing space that they can be a part of and receive guidance from,” Tairo added. 

To increase access to information and inputs, the IPM Innovation Lab is also collaborating with Real IPM, a private company based in Kenya that develops low-cost biological and holistic crop solutions available in Kenya and Tanzania. In just one year, the company has provided training to thousands of farmers in seven counties in Kenya by targeting farmer groups, the majority of which are made up of women. Real IPM has developed training manuals on IPM, a WhatsApp group for crop health assistance and a free web portal for diagnosis and IPM recommendations of specific crop threats. 

“Our goal is to make IPM solutions more accessible,” said Ruth Murunde, research and development manager at Real IPM. “When you enter a pest or disease into our web portal, those images, diagnosis and IPM recommendations stay posted. We know that many farmers are experiencing similar issues to one another and collective action against crop threats is an effective way to combat them more long-term.”

While technology constraints remain — including smartphone, internet and electricity access — making learning spaces available for a range of crop production actors is critical to adoption of sustainable, effective farming solutions. 

Currently, the Real IPM database hosts over 7,000 participants and has collected over 200 infected crop images.

“The Real IPM technical team is actively working to support farmers by providing biopesticides as a solution for mitigating pests and diseases on vegetable crops to ensure sustainable agriculture for smallholder farmers,” added Murunde. “Our information networks help disseminate best practice methods for using those tools.”  

For more information on IPM training or Real IPM products, contact saraeh91@vt.edu.FILED UNDER:AGRICULTURAL PRODUCTIVITYEDUCATION AND EXTENSION



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Study on tomato leaf diseases classification based on leaf images

Tomato production can be greatly reduced due to various diseases, such as bacterial spot, early blight, and leaf mold. Rapid recognition and timely treatment of diseases can minimize tomato production loss. Nowadays, a large number of researchers (including different institutes, laboratories, and universities) have developed and examined various traditional machine learning (ML) and deep learning (DL) algorithms for plant disease classification.

However, through pass survey analysis, the team found that there are no studies comparing the classification performance of ML and DL for the tomato disease classification problem. The performance and outcomes of different traditional ML and DL (a subset of ML) methods may vary depending on the datasets used and the tasks to be solved. This study generally aimed to identify the most suitable ML/DL models for the PlantVillage tomato dataset and the tomato disease classification problem. For machine learning algorithm implementation, the team used different methods to extract disease features manually. In this study, the team extracted a total of 52 texture features using local binary pattern (LBP) and gray level co-occurrence matrix (GLCM) methods and 105 color features using color moment and color histogram methods. Among all the feature extraction methods, the COLOR+GLCM method obtained the best result.

By comparing the different methods, the team found that the metrics (accuracy, precision, recall, F1 score) of the tested deep learning networks (AlexNet, VGG16, ResNet34, EfficientNet-b0, and MobileNetV2) were all better than those of the measured machine learning algorithms (support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF)). Furthermore, the team found that, for the dataset and classification task, among the tested ML/DL algorithms, the ResNet34 network obtained the best results, with accuracy of 99.7%, precision of 99.6%, recall of 99.7%, and F1 score of 99.7%.

Read the complete research at www.researchgate.net.

Tan, Lijuan & Lu, Jinzhu & Jiang, Huanyu. (2021). Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods. AgriEngineering. 3. 542-558. 10.3390/agriengineering3030035. 

Publication date: Mon 19 Jul 2021

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About PestLens


Thursday, August 19, 2021 Notification

First detections of the tobamovirus Tomato brown rugose fruit virus (ToBRFV) in Switzerland, Austria, and Slovenia
Source: Hortidaily, Slovenia Times
Event:  Detection In June and July of 2021, the tobamovirus Tomato brown rugose fruit virus (ToBRFV) was detected in cultivated Solanum lycopersicum (tomato) plants in Switzerland and Austria. Additionally, molecular assays detected ToBRFV in a Capsicum annuum (pepper) seed lot in Slovenia. The infected seed lot was imported from the Czech Republic from seed originating from China. Some seeds from the infected lot were planted in Slovenia. Phytosanitary measures have been implemented in Switzerland, Austria, and Slovenia. These are the first detections of ToBRFV in Switzerland, Austria, and Slovenia. ToBRFV primarily infects S. lycopersicum and Capsicum spp. (pepper). ToBRFV has been reported from Egypt, Turkey, Israel, Jordan, China, and Mexico and has been detected in other parts of Europe and New Zealand. In the United States, ToBRFV has been detected in and eradicated from California. Tobamoviruses are transmitted mechanically and by seed, and ToBRFV can be transmitted by the bumble bee Bombus terrestris, which is not known to occur in the United States. The 2019 PPQ Prioritized Offshore Pest List includes ToBRFV as a pest of concern. References: Hortidaily. 2021. First report of Tomato brown rugose fruit virus in Austria and Switzerland. Hortidaily. August 9, 2021. Last accessed August 19, 2021, from https://www.hortidaily.com/article/9342639/first-report-of-tomato-brown-rugose-fruit-virus-in-austria-and-switzerland/. Slovenia Times. 2021. Tomato brown rugose fruit virus confirmed in Slovenia. Slovenia Times. August 13, 2021. Last accessed August 19, 2021, from https://sloveniatimes.com/tomato-brown-rugose-fruit-virus-confirmed-in-slovenia/.
Other PestLens articles about this pest:
First detections of the tobamovirus Tomato brown rugose fruit virus (ToBRFV) in Norway, Hungary, and Bulgaria
First detection of the tobamovirus Tomato brown rugose fruit virus (ToBRFV) in Malta
First detection of the tobamovirus Tomato brown rugose fruit virus in Belgium
First detection of the tobamovirus Tomato brown rugose fruit virus (ToBRFV) in New Zealand
Disinfection of Solanum lycopersicum (tomato) seeds from the tobamovirus Tomato brown rugose fruit virus (ToBRFV) If you have any questions or comments for us about this article, please e-mail us at pestlens@usda.gov or log into the PestLens web system and click on “Contact Us” to submit your feedback.

First report of Malaysian fruit fly, Bactrocera latifrons (Diptera: Tephritidae), in the Democratic Republic of the Congo
Source: EPPO Bulletin
Event:  New Location Recently, Malaysian fruit fly, Bactrocera latifrons (Diptera: Tephritidae), adults were observed emerging from postharvest Solanum aethiopicum (Ethiopian eggplant) fruits in the Democratic Republic of the Congo. This is the first report of B. latifrons in the Democratic Republic of the Congo. Bactrocera latifrons is primarily a pest of Solanaceae and Cucurbitaceae. Bactrocera latifrons has been reported from other parts of Africa, Iran, and Asia. In the United States, it has been detected in and eradicated from California and has been reported from Hawaii. References: Ndayizeye, L. and C. K. Balangaliza. 2021. First report of Bactrocera latifrons Hendel in the Democratic Republic of Congo. EPPO Bulletin DOI: 10.1111/ epp.12746. Last accessed August 19, 2021, from https://onlinelibrary.wiley.com/doi/10.1111/epp.12746.
Other PestLens articles about this pest:
Detection of Malaysian fruit fly, Bactrocera latifrons (Diptera: Tephritidae), in Italy
Assessment of Citrus sinensis (sweet orange) and C. reticulata (tangerine) as hosts of Malaysian fruit fly, Bactrocera latifrons, and melon fruit fly, B. cucurbitae (Diptera: Tephritidae)
New host records for Malaysian fruit fly, Bactrocera latifrons (Diptera: Tephritidae) If you have any questions or comments for us about this article, please e-mail us at pestlens@usda.gov or log into the PestLens web system and click on “Contact Us” to submit your feedback.

First report of the fungus Phaeoacremonium oleae (Sordariomycetes: Togniniales) in Italy
Source: Plant Disease
Event:  New Location From 2013 to 2019, cultivated Olea europaea (olive) plants in Italy exhibited shoot death, trunk and branch vascular discoloration, and wood necrosis. Morphological and molecular analyses, as well as fulfillment of Koch’s postulates, confirmed that the causal agent was the fungus Phaeoacremonium oleae (Sordariomycetes: Togniniales). This is the first report of P. oleae in Italy. Phaeoacremonium oleae infects O. europaea. Phaeoacremonium oleae has also been reported from South Africa and is not known to occur in the United States. References: Raimondo, M. L., F. Lops, and A. Carlucci. 2021. First report of Phaeoacremonium oleae and P. viticola associated with olive trunk diseases in Italy. Plant Disease DOI: 10.1094/PDIS-06-21-1198-PDN. Last accessed August 19, 2021, from https://apsjournals.apsnet.org/doi/pdf/10.1094/PDIS-06-21-1198-PDN. If you have any questions or comments for us about this article, please e-mail us at pestlens@usda.gov or log into the PestLens web system and click on “Contact Us” to submit your feedback.

Four new scale species described from Colombia
Source: Zoosystema
Event:  New Description/Identification A recent publication describes four new scale species, Newsteadia andreae (Hemiptera: Ortheziidae), Distichlicoccus takumasae (Hemiptera: Pseudococcidae), Paraputo nasai (Hemiptera: Pseudococcidae), and Pseudococcus luciae (Hemiptera: Pseudococcidae), collected from roots of Coffea arabica (coffee) plants in Colombia. References: Caballero, A. 2021. Four new scale insect species (Hemiptera: Coccomorpha) associated with coffee roots in Colombia, South America, with identification keys for genera Newsteadia Green, 1902, Distichlicoccus Ferris, 1950, and Paraputo Laing, 1929. Zoosystema 43(18):341-363. Last accessed August 19, 2021, from https://bioone.org/journals/zoosystema/volume-43/issue-18. If you have any questions or comments for us about this article, please e-mail us at pestlens@usda.gov or log into the PestLens web system and click on “Contact Us” to submit your feedback.
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‘Ten years ago this was science fiction’: the rise of weedkilling robots
A robot made by Carbon Robotics kills weeds on farmland using lasers. Photograph: Carbon Robotics

MON, 16 AUG, 2021 – 14:16PÁDRAIG BELTON

In the corner of an Ohio field, a laser-armed robot inches through a sea of onions, zapping weeds as it goes.

This field doesn’t belong to a dystopian future but to Shay Myers, a third-generation farmer who began using two robots last year to weed his 30-acre crop. The robots – which are nearly three metres long, weigh 4,300kg and resemble a small car – clamber slowly across a field, scanning beneath them for weeds which they then target with laser bursts.

“For microseconds, you watch these reddish colour bursts. You see the weed, it lights up as the laser hits, and it’s just gone,” said Myers. “Ten years ago this was science fiction.” Other than engine sounds, the robots are almost silent and each one can destroy 100,000 weeds an hour, according to Carbon Robotics, the company that makes them.

Carbon Robotics, in common with other agri-robotic startups, emphasizes the environmental benefits these machines can bring to farming by helping to reduce soil disturbance, which can contribute to erosion, and allowing farmers to heavily reduce or even eradicate the use of herbicides.

Farmers across the globe are under increasing pressure to reduce their use of herbicides and other chemicals, which can contaminate ground and surface water, affect wildlife and non-target plants, and have been linked to increased cancer risk. At the same time, they are battling a rise in herbicide-resistant weeds, giving extra impetus to the search for new ways to kill weeds.

“Reduced herbicide usage is one of the spectacular outcomes of precision weeding,” said Gautham Das, a senior lecturer in agri-robotics at the University of Lincoln in the UK. Destroying weeds with lasers or ultraviolet light uses no chemicals at all. But even with robots that do use herbicides, their ability to precisely target weeds can reduce the use by about 90% compared with conventional blanket spraying, Das said.

Five years ago there were almost no companies specializing in farm robots, said Sébastien Boyer, the French-born head of San Francisco-based robot weeding company FarmWise, but it’s now “a booming field”.

The global market for these agricultural robots – which can also be designed to perform tasks such as seeding, harvesting and environmental monitoring – is predicted to increase from $5.4bn (€4.58bn) in 2020 to more than $20bn (€16.98bn) by 2026. “Things scale up very quickly in agriculture,” said Myers.

FarmWise found its first customers in California’s Salinas Valley, which grows lettuce, broccoli, cauliflower and strawberries and is known as “America’s salad bowl”. Ten of the US’s 20 largest vegetable growers, in California and Arizona, now use the company’s robot weeders, according to Boyer. “In the beginning, they started working with us as an experiment, but now they are heavily relying on us”.

Removing pests, such as aphids, thrips and lygus bugs, is a next step for FarmWise. Robots can markedly reduce the use of fungicides and pesticides, said Boyer, by applying them more precisely, using computer vision.

As well as concerns over farming chemicals, labour shortages also play a part in robots’ advance into farmland. Farm labour can be “expensive, hard to come by and dangerous” for people involved, said Myers. 

There are still big challenges to wider-scale adoption. One problem is working in places where a battery recharge is not always readily available, which is a reason some robots – including those made by Carbon Robotics and FarmWise – use diesel for power, which itself produces harmful emissions and pollution.

Danish company FarmDroid’s machines and a herbicide-spraying robot made by Switzerland’s Ecorobotix are both solar-powered.

With batteries rapidly becoming lighter and gaining capacity, farm robots could soon be electrified, said Paul Mikesell, head of Carbon Robotics. This must be accompanied by charging infrastructure on farms, said Rose. “I don’t think we’re far away at all,” he added.

In the meantime, using fewer herbicides may be worth some diesel use, said Richard Smith, a weed science farm adviser from University of California at Davis. “In comparison to all the other tractor work that is done on intensive vegetable production fields, the amount used for the auto-weeders is a small per cent,” he said.

Another challenge is cost. These robots are still expensive, though broader adoption is likely to bring costs down. Carbon Robotics’s robot costs roughly the same as a mid-size tractor – in the hundreds of thousands of dollars.

FarmWise sells robots’ weeding labour, rather than the robots themselves, charging roughly $200 (€170) an acre. Selling a weeding service instead of selling robots requires less upfront investment from farmers, said Boyer, and helped get the robotics business off the ground.

“These service models should reduce the cost barrier for most farmers, and they do not have to worry too much about the technical difficulties with these robots,” Das said.

Covid has been a problem, too, impeding access to clients, investors and semiconductors from Asia. The pandemic has “squeezed startups out of the runway”, says Andra Keay, head of the non-profit Silicon Valley Robotics.

But, beyond weeding robots, Covid has also spurred interest in how robots can shorten supply chains.

Robot-run greenhouses can use hydroponics – growing plants without soil – to produce food closer to large population centres like New York, instead of in places like California where soil is richer.

Iron Ox, a robot-powered greenhouse company based in California, has devised a robotic arm which scans each greenhouse plant and creates a 3D model of it to monitor it for disease and pests.

“Not a lot has changed in agriculture, especially in fresh produce, in the last 70 years,” said Brandon Alexander, the head of Iron Ox who grew up in a large Texas farming family. “Robotic farming offers a chance for humanity to address climate change before 2050,” he said.

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Integrated pest management for indoor cultivation pt. 1

In this new article series sponsored by Hawthorne Gardening Company, we are going to explore the biggest enemies of growers: pests. With today’s article, we start investigating the most dangerous pests for crops such as cannabis, lettuce, saffron, and so on, and how they ravage our favorite plants. In the next articles of this series, we will talk about best prevention practices, and about scouting and monitoring. But before we get to how to repel these attackers, we first have to know them!

Considering the environmental conditions needed to cultivate plants indoors, it doesn’t come as a surprise that greenhouses represent a perfect breeding ground for pests to develop. Because of that, early detection and diagnosis of pest insects are necessary to make control decisions before the problem gets out of hand and growers suffer economic loss. Some common and important greenhouse arthropod pests to keep a close watch for are: aphids, fungus gnats, thrips, whiteflies, root aphids and mites. The most frequently observed diseases are: powdery mildew, botrytis, pythium and fusarium.

“Aphids (Hemiptera: Aphididae) are a typical insect pest of greenhouses which feed on a wide variety of plants by piercing leaf cells and sucking out their contents by means of their stylets,” Ian Bateman, technical service manager at Hawthorne Gardening Company. “Aphids also work as vectors for plant viruses, and they release honeydew waste products that can be spotted on leaves that appear as translucent, or wet spot. When a plant is heavily infested, other than the translucent spots, leaves can turn yellow as well as white skin residues can be found. Additionally, wet spots become a perfect breeding ground for mold or fungal diseases.”

Thrips (Thysanoptera: Thripidae) are a severe insect pest of greenhouses which feed on a wide variety of plants by piercing surface cells of leaves and sucking out the cell contents by means of their stylets. “Thrips also have a very rapid life cycle, which allows for multiple generations per year,” Ian explains. “At the end of the second larval stage, thrips enter the soil or leaf litter. Thrips tend to feed on buds and new leaves – and generally speaking, they prefer to feed on the upper leaf surface of plants. Bronze or silvery leaf scars and tiny black spots of fecal excrements are evident on leaves with heavy-feeding injury.”

Whiteflies (Diptera: Aleyrodidae) are white, soft-bodied, winged insects with a triangular shape, and are often found in clusters on the undersides of leaves. “Whiteflies use their piercing, needlelike mouthparts to suck sap from phloem, the food-conducting tissues in plant stems and leaves. Large populations can cause leaves to turn yellow, appear dry, or fall off plants. Like aphids, whiteflies excrete a sugary liquid called honeydew, so leaves may be sticky or covered with black sooty mold that grows on honeydew,” he continues.

Root aphids
Root aphids (some Pemphigus, Phylloxera, and Rhopalosiphum species) vary in color, but most are white, whitish yellow or brown. “Root aphids have piercing, sucking mouthparts that extract sugar-rich sap from underground structures such as roots, bulbs and rhizomes,” Ian points out. “They can produce a white, waxy secretion that covers their body and some is left behind as they move through the growing medium. This is often mistaken for mealybugs that are also covered with a white waxy or threadlike substance. It is best to use a hand lens and observe the roots to see the actual insect. Minor infections of root aphids do not cause significant plant damage, however, as the populations increase, wounds in plant roots can become entry points for root disease pathogens. Plant roots cannot take up nutrients and therefore can exhibit nutrient deficiencies in the leaves. Plants often have a lack of vigor, are smaller and can wilt, especially during the heat of the day. Root aphids do not travel rapidly, so infections are often restricted to a few plants and spread slowly initially.”

Fungus Gnats
Fungus gnats (Bradysia species) are small, delicate-bodied flies that develop in the growing medium. Adults are 3 mm long, delicate, black flies with long legs and antennae. “Larvae primarily feed on fungi, algae and decaying plant matter as well as plant roots,” Ian remarks. “The larvae are wormlike and translucent, with a black head capsule. Larvae usually are located in the top 2 to 3 inches of the growing medium, depending on moisture level. Moist growing media containing high amounts of peat moss are particularly attractive to adult females. Plants infested by numerous fungus gnat larvae can have stunted growth.”

Spider mites
Spider mites (Acari:Tetranychidae) such as the two-spotted spider mite (Tetranichus urticae) are arthropods that feed on a variety of plant species by sucking the plant cell content through a pair of sharp stylets. Spider mites are tiny (approximately 0.5 mm in length) arachnids with 8 legs and a cream color appearance. “Spider mite populations can develop exponentially in a very short period of time. Eggs are laid in clusters on the underside of leaves. The initial stage of colonization commonly starts from the bottom third of the plant. Injury initially appears as stippling or yellowish-reddish brown spots on the leaves which are located in correspondence of the colony clusters typically found on the underside of the leaf. Leaves initially turn yellow and, with high population density, desiccate and die. On mature plants, higher branches and those directly under growing lights are more likely to become infested with spider mites.”

Stay tuned for the second part of this special series on pest management, where we will discuss hemp russet mites, broad mites, powdery mildew, botrytis, pythium, and fusarium!

For more information:
Hawthorne Gardening

Publication date: Thu 12 Aug 2021
© HortiDaily.com / Contact

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August 11, 2021

Laura Hollis

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PlantwisePlus: detecting and responding to plant health threats

Invasive species specialist Dr Ivan Rwomushana is one of the Global Team Leaders for CABI’s new global PlantwisePlus programme. His role within the programme is to strengthen decision support systems for the detection and response to pest outbreaks and plant health threats.

detection and response

Since joining CABI in 2017 as an Invasive Species Management Senior Scientist, Dr Rwomushana has been involved with the development and implementation of climate resilient invasive species management initiatives, with the goal of providing farmers with novel solutions that reduce the heavy reliance on chemical pesticides. Dr Rwomushana will now be working with countries to develop national pest monitoring systems and coordinated response to manage plant health threats. 

Crop losses

Pests can have a devastating effect on smallholder yields, accounting for nearly 40% of all crop losses. What’s more, climate change is worsening the risk as it changes the biology, distribution and outbreak potential of agricultural pests. 

Since its inception in 2011, CABI’s Plantwise programme has helped countries to identify and manage risks posed by pests and diseases through effective communication and deployment of sustainable agricultural technologies. Building on this success, CABI’s new global PlantwisePlus Programme will enable countries to predict, prepare for and prevent plant health threats so farmers can increase their incomes and grow safer, higher quality food. 

papaya mealy bug
Papaya mealy bug © CABI

Initial activities

Initial activities will test approaches to strengthening the capability of national systems to assess, prioritise, and monitor the economic threats of pests such as fall armyworm, papaya mealybug, parthenium and cassava brown streak disease, and to respond through implementing agreed risk management plans. 

Some of the activities being undertaken in 2021 include: 

– A surveillance for species prioritized during Horizon scanning and conducting targeted Pest Risk Analysis (PRA) using CABI’s PRA tool. This online portal helps identify, assess and manage the risks of plant pest introductions. It presents information in a way that helps decision makers take the most appropriate action to prevent pests crossing borders.

– Insight reporting service on emerging pests that could pose risks to specific countries.

– Response planning with partners to mitigate against new plant health threats at national level. 

– Experimental releases of the parasitic wasp Telenomus remus for the management of fall armyworm (FAW), and advancing this to widespread releases and insitu production at the farm level.

– Entomopathogenic Nematodes (EPNs) mass culture production, formulation improvements and field testing of EPN formulations, strains and applications for FAW.

– Exploring classical biological of FAW, papaya mealybug and parthenium weed for the sustainable management of these species.

– Mass extension campaigns for apple snail and cassava brown streak disease to provide information that will reduce further spread of these invasive species.

fall armyworm
Fall armyworm © CABI

Open-access tools

A number of systems and tools will be utilised to deliver data, information and evidence, that will shape the decision making, planning and response of countries to plant health threats. Some open-access tools are already in place, including, Invasive Species Compendium, Horizon Scanning Tooland the previously mentioned Pest Risk Analysis Tool. 

Further tools and processes will also be developed to help reinforce pest management interventions. Data on pest distribution and severity will come from multiple sources, including general and specific surveillance, economic and environmental data, and will help inform decisions at country levels and on different time scales.  

About PlantwisePlus

PlantwisePlus is a global programme, led by CABI, to increase incomes and grow safer and higher quality food through sustainable approaches to crop production.

Working in close partnership with relevant actors, PlantwisePlus strengthens national plant health systems from within, enabling countries to provide farmers with the knowledge they need to lose less and feed more.

CABI gratefully acknowledges the financial support of the Directorate General for International Cooperation (DGIS, Netherlands), the European Commission Directorate General for International Partnerships (INTPA,EU), the UK Foreign, Commonwealth & Development Office (FCDO), the Swiss Agency for Development and Cooperation (SDC), for the PlantwisePlus programme.

For more information visit: https://www.plantwise.org
Facebook: https://www.facebook.com/Plantwise
Twitter: https://twitter.com/CABI_Plantwise (@CABI_Plantwise)

Further readinghttps://blog.plantwise.org/2021/07/28/plantwiseplus-toolkit-applying-digital-development-principles-to-real-life/embed/#?secret=azkFyrej5Q

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“Pest detection requires trained eye of skilled expert – or an app”

Every week, indoor growers spend hours walking around their greenhouses, analyzing crops and inspecting the type and number of flies on glue traps. The latter plays a key role in the early-stage detection of pests. The early intervention significantly reduces crop waste and the use of crop protection products — thus, reducing costs. The inspection itself, however, takes a lot of time. And it requires the trained eye of a skilled expert, writes Richard Wilms with Itility. 

So, what if we could automate it?
They are the biggest fear of any grower: pests. If you do not see them coming, yields can be ruined. Luckily, there exists such a thing as glue traps. By using these traps, pests can be foreseen at an early stage by simply monitoring whether the count of certain pest types increases over time. In order to do this, you need to be able to recognize the different pest types on a trap. This is not something that can be done by any employee; it requires the eye of an expert. And expert hours are, of course, costly and scarce. 

Fortunately, there is an alternative to the manual counting process. In a joint project with one of our customers, a cucumber grower, we automated this process of pest detection. 

Innovating with sensors and algorithms
The first step was gathering enough pictures of glue traps and flies on them, and to categorize each of these flies. So still quite a manual step as the starting point of the automation. 

For this, we built a mobile app to take pictures via a smartphone and send them to our cloud platform. Multiple weeks in a row, a team member of the grower took pictures of glue traps in their greenhouse, using this mobile application (please check our upcoming software engineering blog for more details on the creation of the app).  

Then, he used that same app to categorize each insect on the picture (data labeling). And the last step: with those labeled pictures, we trained our algorithm to accurately determine the different pest types and calculate the probability of beginning pests.

AI is not magic, just plain hard work
Sounds easy — but data science is no magic bullet, it requires a lot of hard work, and a lot of labeled data. This takes time and effort. We needed to feed the pest detection algorithm with many iterations of labeled images, so it could gradually predict the pest type with increasing accuracy until it is fully trained to detect pests without any human intervention. 

We first focused on the detection of whiteflies, since those were the majority of the insects on the glue traps. Plus, they are quite easy to detect by eye (even an untrained eye, such as that of our data scientist, managed to identify them on the glue trap). Still, it took two months of taking pictures, labeling, and training and retraining the algorithm. But by then, we managed to detect the whiteflies with an average accuracy of 99%. Quite cool! 

Other pest types, like thrips, fruit flies, and sciaras, were also labeled in that period of time. This number was much lower than the number of whiteflies, but still enough to enable us to take our next step: to train our pest detection model to accurately detect these other pest types as well. That appeared to be more complicated, we did not reach the accuracy that we had with the whiteflies. So, the first idea: more pictures, more labels. But that did not bring the desired result either. Back to the hard work and common sense. We realized that the difference in accuracy might possibly be caused by the data labels. Could it be that labeling an insect using a picture taken by a camera would give other results than when you would look at the glue trap directly and note down each insect type? 

With that thought, we added a step where we compared the manual counts on paper from the expert’s daily rounds with the labels he mentioned on the pictures. And indeed, we found a difference in paper count per glue trap and labeled count per picture of that same glue trap. A tiny insect like a thrip is far more difficult to distinguish on a photo than ‘in real life’. 

So another manual step was needed: comparing the real glue traps and their pictures and adding the missing counts to the labeled data. We then retrained and finetuned the algorithm once more. And once more. And once more. But with a great result: accuracy of 93% for thrips.

More automation
With the achieved accuracy, it is possible to automate the daily or weekly count of insects on a glue trap. With that information, however, even more is possible. The pest detection application not only makes it possible to detect the type and number of flies but also gives insights into the location and increase or decrease of flies over time. A dashboard provides an overview of areas that require attention in the greenhouse, allowing the grower to take targeted preventive measures. Combined with the possibility to monitor the effect of those measures, the pest detection app thus enables cost reduction on expensive pesticides. And in the future, this can result in a fully automated pest detection process, monitoring the greenhouse on pests at any given time, enabling the expert to remotely access all required data to make (or confirm) decisions and use this data as evidence.

But when you have successfully applied or trained a model on a use case and you have proven that it works, you have spent about 10% of the effort required to productize it or embed it within a business process. For more information:

Publication date: Wed 11 Aug 2021

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Online resources available from BCPC:




·        GM / BIOTECH CROPS MANUAL (Free Access)

·        IDENTIPEST (Free Access)

·        ·      


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DNA barcodes decode the world of soil nematodes

To understand soil ecosystems and contribute to advanced agriculture





The research team of Professor Toshihiko Eki of the Department of Applied Chemistry and Life Science (and Research Center for Agrotechnology and Biotechnology), Toyohashi University of Technology used a next-generation sequencer to develop a highly efficient method to analyze soil nematodes by using the 18S ribosomal RNA gene regions as DNA barcodes. They successfully used this method to reveal characteristics of nematode communities that inhabit fields, copses, and home gardens. In the future, the target will be expanded to cover all soil-dwelling organisms in agricultural soils, etc., to allow investigations into a soil’s environment and bio-diversity. This is expected to contribute to advanced agriculture.


Similar to when the UN declared 2015 to be the International Year of Soils, there have recently been many efforts worldwide to raise awareness of the importance of the soil that covers our Earth and its conservation. Diverse groups of organisms such as bacteria, fungi, protists, and small soil animals inhabit the soil, and together they form the soil ecosystem. Nematodes are a representative soil animal; they are a few millimeters long and have a shape resembling a worm. They play an important role in the cycling of soil materials. Many soil nematodes are bacteria feeders, but they have a wide variety of feeding habits, such as feeding on fungi, plant parasitism, or being omnivorous. In particular, plant parasitic nematodes often cause devastating damage to crops. Therefore, the classification and identification of nematodes is also important from an agricultural standpoint. However, nematodes are diverse, and there are over 30,000 species. Additionally, because nematodes resemble one another, morphological identification of nematodes is difficult for anyone but experts.

The research team focused on “DNA barcoding” to identify the species based on their unique nucleotide sequences of a barcode gene, and they established a method using a next-generation sequencer that can decode huge numbers of nucleotide sequences. They used this to analyze nematode communities from different soil environments. Initially, four DNA barcode regions were set for the 18S ribosomal RNA genes shared by eukaryotes. The soil nematodes used for analysis were isolated from an uncultivated field, a copse, and a home garden growing zucchini. The PCR was used to amplify the four gene fragments from the DNA of the nematodes and determine the nucleotide sequences. Additionally, the nematode-derived sequence variants (SVs) representing independent nematode species were identified, and after taxonomical classification and analysis of the SVs, it was revealed that plant parasitizing nematodes were abundant in the copse soil and bacteria feeders were abundant in the soil from the home garden. It was also determined that predatory nematodes and omnivorous nematodes were abundant in the uncultivated field, in addition to bacteria feeders.

This DNA barcoding method using a next-generation sequencer is widely used for the analysis of intestinal microbiota, etc., but analyses of eukaryotes such as nematodes are still in the research stage. This research provides an example of its usefulness for the taxonomic profiling of soil nematodes.

Development Background

Research team leader Toshihiko Eki stated, “Through genetic research, I have been working with nematodes (mainly C. elegans) for around 20 years. As a member of our university’s Research Center for Agrotechnology and Biotechnology, I came up with this theme while considering research that we could perform that is related to agriculture. As a test, we isolated nematodes from the university’s soybean field and unmanaged flowerbed and analyzed the DNA barcode for each nematode. Bacteria feeders were abundant in the soybean field, and that was used for comparison with the flowerbed, where weed-parasitizing nematodes and their predator nematodes were abundant. This discovery was the start of our research (Morise et al., PLoS ONE, 2012). If that method using one-by-one DNA sequencing was the first generation, the current method using the next-generation sequencer is the second generation, and we were able to clarify characteristics of nematode communities representing the three ecologically different soil environments according to expectations.”

Future Outlook

Currently, the research team is developing the third-generation DNA barcoding method which involves purifying DNA directly from the soil and analyzing the organisms in the whole soil instead of isolating and analyzing any particular soil-dwelling organisms. They are currently analyzing the soil biota of cabbage fields, etc. They are aiming to precisely analyze how communities of soil-dwelling organisms including microbes change with crop growth, clarify the effects that cultivated plants have on these organisms, and investigate biota closely related to plant diseases. If this research moves forward, crops can be cultivated and managed logically based on biological data in agricultural soils, and it can contribute to advancing smart agriculture in Japan, such as in the prominent Higashi-Mikawa agriculture region and beyond.


This research was performed with the support of the Takahashi Industrial and Economic Research Foundation.


Harutaro Kenmotsu, Masahiro Ishikawa, Tomokazu Nitta, Yuu Hirose and Toshihiko Eki (2021). Distinct community structures of soil nematodes from three ecologically different sites revealed by high-throughput amplicon sequencing of four 18S ribosomal RNA gene regions.
PLoS ONE, 16(4): e0249571.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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JUNE 25, 2021

Kiwi disease study finds closely related bacterial strains display different behaviors

by American Phytopathological Society

Kiwi disease study finds closely related bacterial strains display different behaviors
Elodie Vandelle, Annalisa Polverari, Davide Danzi, Vanessa Maurizio, Alice Regaiolo, Maria Rita Puttilli, Teresa Colombo, Tommaso Libardi. Credit: Elodie Vandelle

Over the last decade, severe outbreaks of bacterial canker have caused huge economic losses for kiwi growers, especially in Italy, New Zealand, and China, which are among the largest producers. Bacterial canker is caused by the bacterial pathogen Pseudomonas syringae pv. actinidiae (Psa) and more recent outbreaks have been particularly devastating due to the emergence of a new, extremely aggressive biovar called Psa3.

Due to its recent introduction, the molecular basis of Psa3’s virulence is unknown, making it difficult to develop mitigation strategies. In light of this dilemma, a group of scientists at the University of Verona and University of Rome collaborated on a study comparing the behavior of Psa3 with less-virulent biovars to determine the basis of pathogenicity.

They found that genes involved in bacterial signaling (the transmission of external stimuli within cells) were especially important, especially the genes required for the synthesis and degradation of a small chemical signal called c-di-GMP, that suppresses the expression of virulence factors. Compared to other biovars, Psa3 produces very low levels of c-di-GMP, contributing to an immediate and aggressive phenotype at the onset of infection before the plant can corral a defense response.

“It was exciting to discover this diversified arsenal of pathogenicity strategies among closely related bacterial strains that infect the same hosts but display different behaviors,” said Elodie Vandelle, one of the scientists involved with this study. “Although their ‘small’ genomes mainly contain the same information, our research shows that bacterial populations within a pathovar are more complex than expected and their pathogenicity may have evolved throughout different strategies to attack the same host.”

Their research highlights the importance of working on a multitude of real-life pathogenic bacterial strains to shed light on the diversity of virulence strategies. This approach can contribute to the creation of wider pathogenicity working models. In terms of kiwi production, Vandelle hopes their findings can help scientists develop new mitigation methods. In the long-term, their research could lead to the identification of key molecular switches responsible for the transition between high and low bacterial virulence phenotypes.

“This identification would allow, at industrial level, to develop new targeted strategies to control phytopathogenic bacteria, weakening their aggressiveness through switch control, instead of killing them,” Vandelle explained. “This would avoid the occurrence of new resistances among bacterial communities, thus guaranteeing a sustainable plant protection.”

Explore furtherUnpacking the two layers of bacterial gene regulation during plant infection

More information: Elodie Vandelle et al, Transcriptional Profiling of ThreePseudomonas syringaepv.actinidiaeBiovars Reveals Different Responses to Apoplast-Like Conditions Related to Strain Virulence on the Host, Molecular Plant-Microbe Interactions (2020). DOI: 10.1094/MPMI-09-20-0248-RJournal information:Molecular Plant-Microbe InteractionsProvided by American Phytopathological Society

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