Posted in | News | Agricultural Robotics

AI Offers New Hope to Solving Wheat Disease

Fusarium head blight, commonly called scab disease, is a highly destructive wheat disease that leads to substantial yield loss and contamination of wheat grain with deoxynivalenol.

AI Offers New Hope to Solving Wheat Disease

Jessica Rutkoksi, pictured, is part of a University of Illinois team using cell phone images and AI to detect fungal toxins in wheat kernels. The goal is to quickly identify wheat lines with lower susceptibility to the fungus, making it easier to breed for disease resistance in the crop. Image Credit: University of Illinois College of ACES

Deoxynivalenol (DON) is a mycotoxin that can cause adverse health effects in humans and animals. Phenotyping for Fusarium-damaged kernels (FDKs) provides an accurate assessment of resistance to accumulation of DON; however, it is a time-consuming and subjective process.

A study published in The Plant Phenome Journal implemented sophisticated object recognition technology for filtering out DON-contaminated wheat kernels from the food supply chain and to assist scientists in developing wheat that has stronger resistance to FHB.

Fusarium Head Blight – A Significant Threat to Wheat

Fusarium head blight (FHB) is a serious disease for wheat, causing billions of dollars of losses in crops to date. FHB causes deoxynivalenol buildup in wheat grains. DON is a mycotoxin belonging to the trichothecene family of vomitoxins. FHB is of great concern since DON ingestion in people and animals from infected wheat end products has detrimental effects on health.

In humans, DON consumption may cause nausea, headaches, vomiting, and diarrhea. The adverse health consequences of DON consumption differ amongst animals, but most typically result in weight loss, nutritional deficiencies, and immunological deficiencies.

Detecting Fusarium-Damaged Kernels Using AI

FDK is a well-established visual grain damage caused by Fusarium, which is observed post-harvest. It is used as a ‘proxy’ phenotype to indirectly select for resistance to DON accumulation within the grain.

The team developed a simple and user-friendly method to identify FDKs by training a convolutional neural network (CNN) model on images of healthy and infected wheat kernels.

The images were taken with a smartphone and uploaded to the app, which then used the trained CNN model to determine the percentage of infected kernels. The model achieved an accuracy of around 90% in detecting FDKs in wheat, which was comparable to manual FDK counting.

While alternative techniques for quantifying DON levels in wheat grain samples exist, they entail lab-intensive tests like mass spectrometry (MS) and enzyme-linked immunosorbent tests, which can be costly and time-consuming.

The CNN model used in the study was trained on numerous images of wheat kernels taken with a smartphone, half of which were healthy, and the other half were infected with Fusarium graminearum.

The model was then used to classify new images of kernels as healthy or infected. The researchers tested the model on additional images of wheat kernels, achieving a high accuracy in detecting FDKs in wheat.

Girish Chowdhary, an author of the study, remarked on the novelty of their research, “One of the unique things about this advance is that we trained our network to detect minutely damaged kernels with good enough accuracy using just a few images. We made this possible through meticulous pre-processing of data, transfer learning, and bootstrapping of labeling activities.”

Potential Applications

According to the researchers, the mobile app has the potential to make the process of phenotyping for FDKs more accessible and affordable, especially in developing countries where laboratory assays are not readily available.

It can also be used in the field to identify infected wheat kernels, enabling farmers to monitor FHB in real time and take necessary measures to minimize yield loss and mycotoxin contamination.

The app can also help researchers and industries to screen large numbers of wheat varieties for resistance to FHB and DON accumulation.

The CNN model can be fine-tuned to identify specific resistance mechanisms and to develop wheat varieties that are resistant to FHB and have low DON levels, thus contributing to global food safety and security.

Fusarium head blight remains one of the most destructive diseases in wheat, resulting in significant yield losses and the contamination of wheat grain with deoxynivalenol.

Phenotyping for Fusarium-damaged kernels is a critical component of identifying resistance to DON accumulation in wheat, but manual phenotyping can be time-consuming.

This study has developed and tested an open-access and easy-to-use method for the phenotyping of FDKs using a convolutional neural network trained on cell phone images.

The method achieved an accuracy of around 90% when tested on a separate dataset, demonstrating its potential to greatly improve the efficiency and accuracy of FDK phenotyping.

Future research in this area could focus on further refining the CNN model, as well as combining this method with other technologies to develop a more comprehensive system for monitoring crop health and identifying disease outbreaks.

Reference

Wu, J., Ackerman, A., Gaire, R., Chowdhary, G., & Rutkoski, J. (2023). A neural network for phenotyping Fusarium-damaged kernels (FDKs) in wheat and its impact on genomic selection accuracy. The Plant Phenome Journal, 6(1). https://doi.org/10.1002/ppj2.20065

Source: 

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Shaheer Rehan

Written by

Shaheer Rehan

Shaheer is a graduate of Aerospace Engineering from the Institute of Space Technology, Islamabad. He has carried out research on a wide range of subjects including Aerospace Instruments and Sensors, Computational Dynamics, Aerospace Structures and Materials, Optimization Techniques, Robotics, and Clean Energy. He has been working as a freelance consultant in Aerospace Engineering for the past year. Technical Writing has always been a strong suit of Shaheer's. He has excelled at whatever he has attempted, from winning accolades on the international stage in match competitions to winning local writing competitions. Shaheer loves cars. From following Formula 1 and reading up on automotive journalism to racing in go-karts himself, his life revolves around cars. He is passionate about his sports and makes sure to always spare time for them. Squash, football, cricket, tennis, and racing are the hobbies he loves to spend his time in.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Rehan, Shaheer. (2023, March 28). AI Offers New Hope to Solving Wheat Disease. AZoRobotics. Retrieved on April 21, 2024 from https://www.azorobotics.com/News.aspx?newsID=13759.

  • MLA

    Rehan, Shaheer. "AI Offers New Hope to Solving Wheat Disease". AZoRobotics. 21 April 2024. <https://www.azorobotics.com/News.aspx?newsID=13759>.

  • Chicago

    Rehan, Shaheer. "AI Offers New Hope to Solving Wheat Disease". AZoRobotics. https://www.azorobotics.com/News.aspx?newsID=13759. (accessed April 21, 2024).

  • Harvard

    Rehan, Shaheer. 2023. AI Offers New Hope to Solving Wheat Disease. AZoRobotics, viewed 21 April 2024, https://www.azorobotics.com/News.aspx?newsID=13759.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type
Submit

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.