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Bioresource and Agricultural Engineering Commons

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Full-Text Articles in Bioresource and Agricultural Engineering

Characterization Of Physical And Biochemical Traits In Wheat And Corn Plants Using High Throughput Image Analysis, Kantilata Thapa Apr 2023

Characterization Of Physical And Biochemical Traits In Wheat And Corn Plants Using High Throughput Image Analysis, Kantilata Thapa

Department of Biological Systems Engineering: Dissertations and Theses

Plant phenotyping has been recognized as a rapidly growing field of research due to the labor-intensive, destructive, and time-consuming nature of traditional phenotyping methods. These phenotyping bottlenecks can be addressed by advancements in image-based phenotyping like RGB and hyperspectral imaging for the assessment of plant traits important for breeding purposes. This study aims (1) to characterize the physical and biochemical traits of wheat and corn plants using RGB and hyperspectral imaging in the greenhouse, and (2) to estimate leaf nitrogen (N), phosphorus (P), and potassium (K) content using hyperspectral imaging and an analytical spectral device (ASD spectrometer) and compare the …


Osc-Co2: Coattention And Cosegmentation Framework For Plant State Change With Multiple Features, Rubi Quiñones, Ashok Samal, Sruti Das Choudhury, Francisco Muñoz-Arriola Jan 2023

Osc-Co2: Coattention And Cosegmentation Framework For Plant State Change With Multiple Features, Rubi Quiñones, Ashok Samal, Sruti Das Choudhury, Francisco Muñoz-Arriola

Department of Biological Systems Engineering: Papers and Publications

Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object’s pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and …