Posters - Schedules

Phenotyping allows the measurement of morphometric and physiological parameters of plants in a rapid, non-destructive, accurate, and high-throughput manner. Traditional phenotyping in breeding is time-consuming, labor-intensive, and the database is insufficient to satisfy the needs of plant breeders which hampers the breeding progress. Recent advancements in electronics, and sensor technologies in agriculture have aided in the development of innovative methods for measuring phenotypic characteristics. These sensor systems can provide a high spatial and temporal resolution data to characterize crop growth parameters within the diverse environmental condition. In this study the Raspberry Pi (RPi)-based sensor imaging system was integrated with a camera (RPi Sony 8MP) in growth chamber to analyze the crop growth conditions in wheat breeding trial for automated phenotypic application. The collected digital images were suitable for extracting measurable plant traits. The plant traits studied includes morphometric parameters such as plant density, canopy cover, leaf area index, and physiological parameters such as photosynthetic rate and biomass, which represents the plant growth and health. The developed low cost digital imaging system will be integrated with internet to facilitate internet-of-things (IoT) based sensor system which helps plant breeder to make timely decisions, screen elite cultivar and monitor crop in real-time.

Phenotyping allows the measurement of morphometric and physiological parameters of plants in a rapid, non-destructive, accurate, and high-throughput manner. Traditional phenotyping in breeding is time-consuming, labor-intensive, and the database is insufficient to satisfy the needs of plant breeders which hampers the breeding progress. Recent advancements in electronics, and sensor technologies in agriculture have aided in the development of innovative methods for measuring phenotypic characteristics. These sensor systems can provide a high spatial and temporal resolution data to characterize crop growth parameters within the diverse environmental condition. In this study the Raspberry Pi (RPi) -based sensor imaging system was integrated with a camera (RPi Sony 8MP) in growth chamber to analyze the crop growth conditions in wheat breeding trial for automated phenotypic application. The collected digital images were suitable for extracting measureable plant traits. The plant traits studied includes morphometric parameters such as plant density, canopy cover, leaf area index, and physiological parameters such as photosynthetic rate and biomass, which represents the plant growth and health. The developed low cost digital imaging system will be integrated with internet to facilitate internet-of-things (IoT) based sensor system which helps plant breeder to make timely decisions, screen elite cultivar and monitor crop in real-time.

Crop diseases are a major threat to human food security. Around the world, more than 80% of agricultural production is generated by farmers, and over 50% of their yield is lost due to pests and pathogens, leading to mass disruption in food supply and a large number of hungry people. The purpose of this research was to create a free, easy-to-use, and widely accessible mobile application that efficiently and accurately, diagnoses 26 diseases of 14 crop species. Furthermore, this application provides treatment steps, common symptoms, and access to recommended curing products for each disease. The real-time crop disease diagnosis is based on a convolutional neural network (CNN) that was trained, validated, and tested on a dataset of 87,860 leaf images split into 38 classes. To design an optimal CNN, 16 different CNNs were designed and tested. MobileNetV2 using the Canny Edge Detection filter was chosen as it had the highest classification accuracy of 95.7 % and an Fl score of 96.1. This application is a novel and accessible tool for crop disease management and can be deployed as a free service to farmers for ecologically sustainable production, overall increasing food security.

Rapid and accurate assessment of poultry health can inform producers to make timely decisions to reduce the threat of disease transmission, bird suffering, and economic loss. The objective of this research was to develop a mobile application to assist farmers in assessing poultry health during their daily flock inspections. A local server was built to assign users with different usage credentials and receive uploaded images. A dataset containing fecal images of healthy or unhealthy birds (infected with Coccidiosis, Salmonella, and Newcastle Disease) was used to develop a deep learning model, VGG19. The developed model was embedded into the local server for feces image classification. An App that can be used for Android and IOS systems was developed. Via the App on a small cell phone, a fecal image can be acquired on a farm, uploaded to the server, and processed by the model. The processed image was marked with the health situation of each sample and transferred back to the user. The system achieved over 90% accuracy for health assessment, and the whole operating procedure took less than one second. The developed system will maintain farm data confidentiality while assisting in real-time poultry health assessment

Porcine reproductive and respiratory syndrome virus (PRRSV) causes substantial loss to the swine industry in many countries. Porcine alveolar macrophage (PAM) is one of the primary cellular targets for PRRSV, but only less than 2% of PAMs are infected with the virus during the acute stage of infection. To comparatively analyze the host transcriptional response between PRRSV-infected PAMs and bystander PAMs that remained uninfected but were exposed to the inflammatory milieu of an infected lung, pigs were infected with a PRRSV strain expressing green fluorescent protein (PRRSV-GFP), and GFP1 (PRRSV infected) and GFP2 (bystander) cells were sorted for RNA sequencing (RNA-Seq). Approximately 4.2% of RNA reads from GFP1 and 0.06% reads from GFP2 PAMs mapped to the PRRSV genome, indicating that PRRSV-infected PAMs were effectively separated from bystander PAMs. PRRSV-infected PAMs showed a distinctive gene expression profile and contained many uniquely activated pathways compared to bystander PAMs. Interestingly, upregulated expression of NF-kB signaling inhibitors and T-cell exhaustion molecules were observed in PRRSV- infected PAMs. Our findings provide additional knowledge on the mechanisms that PRRSV employs to modulate the host immune system.

Rapid and accurate assessment of poultry health can inform producers to make timely decisions to reduce the threat of disease transmission, bird suffering, and economic loss. The objective of this research was to develop a mobile application to assist farmers in assessing poultry health during their daily flock inspections. A local server was built to assign users with different usage credentials and receive uploaded images. A dataset containing fecal images of healthy or unhealthy birds (infected with Coccidiosis, Salmonella, and Newcastle Disease) was used to develop a deep learning model, VGG19. The developed model was embedded into the local server for feces image classification. An App that can be used for Android and IOS systems was developed. Via the App on a small cell phone, a fecal image can be acquired on a farm, uploaded to the server, and processed by the model. The processed image was marked with the health situation of each sample and transferred back to the user. The system achieved over 90% accuracy for health assessment, and the whole operating procedure took less than one second. The developed system will maintain farm data confidentiality while assisting in real-time poultry health assessment