Igathinathane Cannayen

Associate Professor

North Dakota State University

United States

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About

Igathinathane Cannayen is an Associate Professor at North Dakota State University, located in the United States. His research focuses on agricultural technologies, particularly utilizing computer vision, remote sensing, and machine learning for crop management and phenological studies. Recent publications include works on UAV-based weed identification, computer vision applications for agricultural produce measurement, and the development of automation tools for harvesting.

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CANADA

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Paulo Flores

North Dakota State University

UNITED STATES

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Ademola Ajayi-Banji

North Dakota State University

UNITED STATES

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Igathinathane Cannayen

Research Interests

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About

Articles (19)

Jasmine Flower Color Degradation User-Coded Computer Vision Image Analysis Tool and Kinetics Modeling

Evaluating Remote Sensing Resolutions and Machine Learning Methods for Biomass Yield Prediction in Northern Great Plains Pastures

A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance

Assessing the intrapore volume and surface area of single corn kernel using CT scan imaging and machine vision

Optimized Yolov5s-Im for real-time apple flower detection in drone-based pollination

Development and field evaluation of a straddle-frame honeysuckle harvesting robot

Crop row identification and plant cluster segmentation for stand count from UAS imagery based on profile and geometry

Computer Vision-Based Multiple-Width Measurements for Agricultural Produce

PhenoCam Guidelines for Phenological Measurement and Analysis in an Agricultural Cropping Environment: A Case Study of Soybean

Applications of Raspberry Pi for Precision Agriculture—A Systematic Review

Infield corn kernel detection using image processing, machine learning, and deep learning methodologies under natural lighting

The Long‐Term Agroecosystem Research cropland common experiment at Northern Plains

Comprehensive wheat lodging detection after initial lodging using UAV RGB images

Machine learning and handcrafted image processing methods for classifying common weeds in corn field

Measuring soybean iron deficiency chlorosis progression and yield prediction with unmanned aerial vehicle

Forage economics calculator web tool: A decision support system for forage management

Tools for Predicting Forage Growth in Rangelands and Economic Analyses—A Systematic Review

Biomass bales infield aggregation logistics energy for tractors and automatic bale pickers — A simulation study

Rating Iron Deficiency in Soybean Using Image Processing and Decision-Tree Based Models

Collaborators (7)

Alireza Pourreza

Cengiz Koparan

Kevin K. Sedivec

Yiannis Ampatzidis

Shahab Sokhansanj

Paulo Flores

Ademola Ajayi-Banji

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