Recipient Organization
LINCOLN UNIVERSITY
820 CHESTNUT ST
JEFFERSON CITY,MO 651023537
Performing Department
College of Agriculture, Environmental and Human Sciences (CAEHS)
Non Technical Summary
Spotted wing drosophila (SWD) Drosophila suzukii was first detected in California in 2008 and has since spread to many soft fruit and berry crop-producing states. This insect currently poses great risks to the approximately $8 billion soft fruit and berry crops industry. Like many other pests, the development of monitoring tools and predictive models is an important step toward an efficient and effective integrated pest management (IPM) program. Conventional SWD monitoring traps currently available are based on manual counting of the insect. But this is labor-intensive and time-consuming. We have developed an improved smart trap to address this issue. We are therefore proposing to increase research by fine-tuning this artificial intelligence (AI)-enabled smart trap system consisting of a camera, edge computing, and wireless network technologies to monitor SWD populations. In addition, the application of a user-friendly degree-day model into the smart traps will help improve the timing of insecticide applications to coincide with the most vulnerable stages of SWD, thus reducing the number and cost of insecticide applications. We will develop extension materials to provide education about the smart trap and IPM for small fruit farmers in Missouri. This project will also build the capacity of faculty and students at Lincoln University and its collaboration institutions in education and research in accessing emerging technologies (e.g., machine learning and AI) and advanced mathematical applications in agriculture using the degree-day models. The proposed project aligns with the goals of this capacity-building grant in building research, teaching, and extension capacity through collaborations.
Animal Health Component
60%
Research Effort Categories
Basic
10%
Applied
60%
Developmental
30%
Goals / Objectives
The overall goal of this project isto enhance the research, education, and extension capacity in Lincoln University (LU)through collaborative research and extension activities with other land-grant and state universities. This integrated project will include both research and extension components. Through research activities, we will develop, validate, and optimize a smart insect trap network to monitor the Spotted Wing Drosophila(SWD) and local climate, which will be used to improve predictive degree-day models. The research findings will be disseminated through extension activities. The proposed project will be conducted using an interdisciplinary method with close collaboration among multidisciplinary research experts, extension specialists, farmers, and other pertinent stakeholders. The project will complete the following research and extension objectivesat LU andother collaborators' sites.Objective 1 (Year 1-2): Fine-tune the AI-enabled smart insect monitoring network for acquiring insect (e.g., SWD) and micro-climate (e.g., temperature and humidity) information in Missouri small farms.Individuals responsible:Waana Kaluwasha, Maciej Pszczolkowski, and Jianfeng ZhouWe will fine-tune the smart insect monitoring network and focus on developing novel AI and machine learning algorithms for accurate detection and counting of insects. Cloud platforms (e.g., websites, smartphone apps, and text/email subscriptions) will be developed to share information about traps.Objective 2 (Years 1-3): Implemention ofa user-friendly and enhanced insect prediction model to improve IPM decisions of SWD for small fruit farmers, and evaluation of targeted insecticide control programs based on the use of pest phenology to reduce indiscriminateinsecticide use. Individuals responsible:Waana Kaluwasha, Maciej Pszczolkowski, Jianfeng Zhou, graduate students, collaborators, and small fruit producers.Insect prediction models (degree-day models) will be developed using existing trap data and new data collected using both conventional and smart traps in the three project years. A web-based predictive model and cloud-based platform will be developed to share predictions with stakeholders and send timely alerts through emails and text messages.Objective 3 (Year 3): Disseminate research findings to stakeholders through the development of an extension program to share research results through online meetings and workshops. Individuals responsible: Waana Kaluwasha, Lincoln University Innovative Small Farmers' Outreach Program staff (11), Maciej Pszczolkowski, and Jianfeng Zhou.
Project Methods
Study locations: The proposed study will be conducted in mixed cultivarsblueberry and elderberry farms belonging to Lincoln University in Jefferson City, University of Missouri Southwest Research and Education Center inMount Vernon, and Missouri State University Fruit Experiment Station,Mountain Grove all in Missouri. We will also select three additional farms in other parts of the states for this study. The elderberry farm at Lincoln University consists of 9 experimental varieties, which were planted in 2008 and have been in production since. Additional small fruit farms will be recruitedif needed.Objective 1: Fine-tune and evaluate an AI-enabled smart insect monitoring network for acquiring insect (e.g., SWD) and micro-climate (e.g., temperature and humidity) information in Missouri small farms.An AI-enabled smart trap system will be developed using low-cost cameras and edge computing units to monitor the appearance and population of insects, including SWD, at high frequency (e.g., every 30 minutes). AI algorithms will allow us to process and analyze imagery data in real time and information will be shared using a cloud platform. It is expected that the proposed smart SWD monitoring system can remotely monitor SWD and invasive insects more frequently (e.g., daily or hourly) and on a large scale (state or regional level). High temporal and spatial resolution data of SWD will provide more reliable and accurate information for early detection and support grower decision-making. The data is also expected to improve the degree-day models for predicting SWD and provide valuable information for studying the behavioral characteristics of SWD and other invasive insect pests.Objective 2: Implement a user-friendly and enhanced insect prediction model to improve IPM decisions of SWD for small fruit farmers, and evaluatetargeted insecticide control programs based on the useof pest phenology to reduce indiscriminated insecticide use.Objective 2.1 Evaluation of targeted insecticide control programs to reduce the use of insecticidesCurrent grower management standard involving calendar-based insecticide applications will be comparedto the improved and targeted insecticide-based IPM programs for managing SWD. A weekly spray of any recommended SWD insecticide (e.g. Mustang Maxx® (zeta-cypermethrin) reported to be effective against SWD and used by some growers as a standard in many parts of the country will be used. Weekly application may result in up to 7-12 sprays per growing season depending on crop and varieties. We are proposing to use the degree-day model developed by our teamfor the targeted insecticide-based applications. We hope to reduce the number of applications from seven or more to 3 or 4. Two insecticides, which have been reported to be effective against SWD (Midwest Fruit Pest Management Guide, 2023-2024 ) will be tested: i) Mustang Maxx® (zeta-cypermethrin-pyrethroid)and ii) Imidan® (phosmet-organophosphate). Although its effectiveness is unknown, we will include Actara® (thiamethoxam-neonicotinoid) to take advantage of its systemic mode of action to see if it will work on the developing larvae in the berries.Objective 2.2. Develop a web-based degree-day model and cloud platform to disseminate informationAn online degree-day models system will be developed to assist MO fruit farmers in IPM decision-making. We are planning to develop a web page for personal computers (PCs) and smartphones to show reatl-time information on trap distribution, details of each monitoring site, and micro-climate conditions. A cloud-based Internet of Things (IoT) system will be developed using Amazon IoT core or similar cloud platforms. Data from climate sensors, i.e., temperature and humidity sensors, and rain gauge, will be acquired by the edge computing devices (e.g., Raspberry Pi) used by the camera system and be uploaded to the cloud platform through a cellular wireless netowrk. All data will be stored in the Amazon AWS and fetched by the web or smartphone Apps based on request.The AI models developed in Objective 1 will be implemented in the edge-computing units to identify the species of insects and count the number of SWD. All the information will be uploaded to the cloud platform and pushed to the website.A text messaging system will be developed to send alerts to subscribers about the number of insects, unknown species, recommendations, and weather conditions each day.Objective 3: Disseminate research findings to stakeholders through the development of an extension program to share research results through online meetings and workshops.SWD trapping and management using insecticide will be conducted in Years 2 and 3, in cooperation with small fruit growers to identify and determine the effectiveness of the SWD traps for early detection. In years 2 and 3, the integration of the degree-day model will be applied to the management decision of cooperative growers in a randomized complete block design with four replicates per location. Data will be collected on SWD damage in small fruit.Extension activities will be conducted mainly during the third year of the project. We will widely circulate the results of this project to more than 100 small fruit growers and other stakeholders through in-person meetings, Zoom webinars, eXtension.org, and local and national conferences on the following topics: Trapping and degree-day model for SWD in Small fruit in Missouri, the occurrence of insects in Missouri, and the impacts of SWD on the viability of small fruit production. The team will attend other online, local, and national conferences to disseminate the knowledge gained from the project. The team will publish fact sheets on SWD and their economic impacts on small fruit production. These fact sheets will be delivered to stakeholders through LU's and University of Missouri IPM websites. Also, the LU's Innovative Small Farmers' Outreach program staff will be trained to deploy the trap and access the trap number on their phones. We will provide the factsheets to extension staff to spread the new knowledge to producers as a part of their normal activities.Management Plan: Overall management