Source: LINCOLN UNIVERSITY submitted to NRP
OUT-SCALING A GROWER-FRIENDLY CLOUD-BASED MONITORING SYSTEM AND DEGREE-DAY MODEL DEVELOPMENT TO MANAGE THE INVASIVE SPOTTED WING DROSOPHILA IN SMALL FRUIT PRODUCTION.
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
OTHER GRANTS
Reporting Frequency
Annual
Accession No.
1032013
Grant No.
2024-38821-42140
Cumulative Award Amt.
$548,179.00
Proposal No.
2023-09222
Multistate No.
(N/A)
Project Start Date
Apr 1, 2024
Project End Date
Mar 31, 2027
Grant Year
2024
Program Code
[EWE]- Extension Project
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%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
21111991130100%
Knowledge Area
211 - Insects, Mites, and Other Arthropods Affecting Plants;

Subject Of Investigation
1199 - Deciduous and small fruits, general/other;

Field Of Science
1130 - Entomology and acarology;
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

Progress 04/01/24 to 03/31/25

Outputs
Target Audience:The project team participated in some capacity building, outreach and knowledge sharing events between April 1, 2024 and March 31, 2025 to share about the project as well as share progress made. These events included Lincoln University of Missouri's annual field day at the George Washington Carver Farm, which took place in June 2024 attracting variuos stakeholders from the farming, research and industry communities. During this event, a poster titled "A smart spotted wing drosophila monitoring system" was presented to field day attendees and a live demonstration was provided of the smart trap that is being tested in this project for monitoring the spotted wing drosophila (SWD). The next event we participated in was the 2024 Missouri State Fair which took place in Sedalia, MO, in August and attracted a broader audience. A demonstration was provided using the smart trap and flyers on the spotted wing drosophila obtained from North Central IPM Center were shared with the public. The third event was the Great Plains Growers conferencewhich took place in January 2025 anddrew hundreds of attendees representing farmers, researchers, extension personell, industry representatives and students from the Midwest. A poster presentation entitled 'Multilocation monitoring of spotted wing drosophila in Missouri small fruit farms' based on results obtained during the 2024 season were made by a Mastersgraduate studentsupported by this grant. In addition, an oralpresentation titled 'Smart monitoring system for spotted wing drosophila management in small fruit production' was made to introduce the audience to the on-going work as well as share progress made. In summary, we were able to reach a diverse group of stakeholders by targeting field days, grower conferences and even public events such as the State Fair. Changes/Problems:There were some challenges faced the previous year which are described below, alot of which we still need to make improvements too and hence why the upgrades described on the previous page. Challenges in Hardware Development and Deployment Power Supply Issues in Extreme Weather: Continuous rainy or low-light conditions prevent sufficient solar charging. Backup battery depletes, causing temporary shutdowns of smart traps. Unstable Data Transmission. LTE/4G signals are weak or unstable in remote deployment areas, affecting real-time data transmission. Device Maintenance and Cost. Sticky trap cards must be replaced every two weeks. Field inspections are conducted monthly, increasing labor and maintenance costs. Challenges in Model Training and Algorithm Optimization Small Object (SWD) Detection Accuracy. SWD adults are very small (2-3mm) and difficult to detect in complex backgrounds (e.g., varying light, vegetation occlusions). Data Annotation Efficiency and Quality Manually labeling large datasets is time-consuming and requires expert entomological knowledge. High labor costs for manual annotations. Model Generalization Issues. Model performance decreases when tested on new field environments not included in training. ?There were also a few identified hardware issues including. Solar power shortages occurred during extended rainy periods, leading to brief shutdowns. Rainfall affectedimage quality because rainwater stayedon the sticky cards What opportunities for training and professional development has the project provided?The participation in grower conferences and outreach events has resulted in expanded networks as there is interest in the project outcomes. In the coming year, we will have even more participation at both scientific and non-scientific conferences, as well as collaborations with growers as that will be crucial for the project. How have the results been disseminated to communities of interest?During the reporting period, we participated in three outreach events including a conference (Great Plains Growers conference), field day (Carver annualField day) and 2024 Missouri State Fair. During the field day, we had a live demonstration of AI- powered solar smart traps, and introduced attendees to the concept of real-time pest detection and its role in integrated pest management. This provided a hands-on experience for farmers andagricultural professionals. During the Missouri State Fari, we demonstrated real-time SWD detection using instance segmentation and spoke to attendees about how AI could potentatially reduce labor costs and improve pest control efficiency. Finally a presentation given during the Great Plains Growers conference was focused on introducing growers to the project and sharing research progress on SWD monitoring in Missouri small fruit farms. What do you plan to do during the next reporting period to accomplish the goals?In addition to increasing the number of monitoring locations andsmart traps deployed. We plan to monitor weekly and also address some of the challenges faced with the smart trap. Some of these plans include: Hardware System Optimization and Upgrades to enhance environmental adaptability Increase battery capacity from 12V 7Ah to a higher-capacity battery for improved power supply during prolonged rainy periods. Optimize solar panel efficiency and MPPT controller performance to enhance charging under low-light conditions. Develop a refined low-power mode that automatically reduces energy consumption during rainy weather to extend operational time. Improvements in Enclosure and Protectionfor better waterproofing, dust resistance, and corrosion protection; and to enhance durability and reliability for long-term outdoor deployment. Enhancing Communication Stability by: Testing and integrating high-gain LTE antennas or satellite communication modules to improve network stability in remote areas. Optimizinglocal data caching mechanisms to improve on-device storage and cloud synchronization, ensuring data security and completeness. Model Performance Improvement and Generalization by adopting advanced deep learning models and dataset expansion Evaluate Transformer-based architectures (e.g., Mask2Former, Segment Anything Model) for SWD instance segmentation. Assess the robustness and generalization of these models and deploy the best-performing model on edge devices. Collect more data under varied conditions, including different crops, backgrounds, and lighting scenarios, to build a more comprehensive and generalized dataset. Explore weakly supervised and semi-supervised learning techniques to reduce the need for manual annotation and enable automated dataset updates. Improving Algorithm Efficiency Implement model quantization (e.g., INT8 quantization) and pruning for deployment on low-power edge devices (e.g., Raspberry Pi). Evaluate domain adaptation techniques to enhance model performance in new environments. Smart Monitoring System and Agri-IoT Integration Develop and deploy a remote monitoring and real-time alert system for pest population dynamics. Design a user-friendly management and interaction platform for unified data management, aiding farmers and agricultural experts in data-driven decision-making. Integration with Sage Continuum Edge Computing Platform Incorporate the smart trap system with Sage Continuum's edge computing infrastructure for efficient real-time data processing and collaborative decision-making. Utilize Sage Continuum's AI toolkit to optimize model inference efficiency, reducing computational loads on edge devices. Building a Cyberinfrastructure for AI at the Edge Leverage the Sage Continuum network architecture to enable multi-device collaboration and data sharing for enhanced agricultural intelligence. Develop and deploy a distributed real-time alert system to support precision pest control strategies. Developing an SWD Population Prediction Model Collect long-term environmental data (temperature, humidity, light conditions) and historical image data for comprehensive analysis. Build predictive models using time series analysis techniques (e.g., ARIMA, Prophet, or deep learning-based LSTM) to forecast pest outbreaks and provide farmers with proactive control strategies. Develop a real-time dashboard with historical data tracking and trend analysis for farmers and researchers. Implement automated report generation to simplify the interpretation of complex data and aid in decision-making. We will also continue to share about the research and plan to give presentations and/or demonstrations at the Carver farm annual field day, International Elderberry symposium, and Entomological Society of America annual meeting. We will utilise all the newly formed networks to disseminate this information. This year we will also engage with high school students during a week-long high school STEAM camp.

Impacts
What was accomplished under these goals? Some progress was made towards objective 1 and objective 3. The major accomplishments include smart trap development and validation, data collection using smart traps and research dissemination.An AI-enabled "smart" trapwas developed using a high resolution camera, humidity and temperature sensors, edge computing unit (Raspberry Pi), cellular data network and solar power system. The system was tested and validated under laboratory and field conditions.Monitoring was performed using tensmart traps and 21 conventional bucket traps deployed in 6 locations including the Lincoln University Carver farm, Llloyd's farm, University of Missouri Southfarm, University of Missouri Southwest Research, Education and Extension Center, Columbia Elderberry farm and the Missouri State University State Fruit Experimental Station.The traps were servicedbiweekly between May and October 2024, and SWD were collected, identified and counted under a microscope in the laboratory and then stored.Simultaneously, image data was collected by the camera onthe smart trap every hour from 6 am to 9 pm daily. Two imagery data were uploaded to the cloud and then downloaded for further processing. Sticky cards attached to the smart trap were changed biweekly and all SWD were identified under the microscope, and individualimages were taken of all collectedsticky cards to mark identified SWD for model validation. A data processing and analysis pipeline is still under development and it involves multiple steps of image preprocessing and modeling. A total of 11 objective recognition and classification models were evaluated including You Only Look Once (YOLO) models, instance segmentation models, and transformer-based models. The best performing model was YoLoV11s achieving a recognition accuracy of mAP@0.5=0.84. Weather data was also collected from either the smart trap sensor or local weather stations. In addition we participated in outreach events including a conference (Great Plains Growers conference), field day (Carver annualField day) and 2024 Missouri State Fair. During the outreach events we had live demonstrations of the AI-powered smart traps and pest-monitoring system and also shared research progress on SWD monitoring in small fruit production.Two graduate students, one Masters and one Doctoral were recruited to assist with these research activities which will also go toward their theses.

Publications