Anand Koirala
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View article: Intelligent weed management using aerial image processing and precision herbicide spraying: An overview
Intelligent weed management using aerial image processing and precision herbicide spraying: An overview Open
View article: Intelligent Weed Management Using Aerial Image Processing and Precision Herbicide Spraying: An Overview
Intelligent Weed Management Using Aerial Image Processing and Precision Herbicide Spraying: An Overview Open
View article: Development of intelligent suite for malaria pathogen detection in microscopy images
Development of intelligent suite for malaria pathogen detection in microscopy images Open
The identification of malaria infection using microscope images of blood smears is considered as a 'gold standard'. The diagnosis of malaria needs expert microscopists which are scarce in remote areas where malaria is endemic. Therefore, i…
View article: Developing Machine Vision in Tree-Fruit Applications—Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting
Developing Machine Vision in Tree-Fruit Applications—Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting Open
Recent developments in affordable depth imaging hardware and the use of 2D Convolutional Neural Networks (CNN) in object detection and segmentation have accelerated the adoption of machine vision in a range of applications, with mainstream…
View article: Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks
Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks Open
The Part 1 prequel to this review evaluated the evolution of modelling techniques used in evaluation of fruit quality over the past three decades and noted a progression towards the use of artificial neural networks (ANNs) and convolutiona…
View article: Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning
Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning Open
Forward estimates of harvest load require information on fruit size as well as number. The task of sizing fruit and vegetables has been automated in the packhouse, progressing from mechanical methods to machine vision over the last three d…
View article: In-Orchard Sizing of Mango Fruit: 1. Comparison of Machine Vision Based Methods for On-The-Go Estimation
In-Orchard Sizing of Mango Fruit: 1. Comparison of Machine Vision Based Methods for On-The-Go Estimation Open
Estimation of fruit size on-tree is useful for yield estimation, harvest timing and market planning. Automation of measurement of fruit size on-tree is possible using RGB-depth (RGB-D) cameras, if partly occluded fruit can be removed from …
View article: Deep Learning for Real-Time Malaria Parasite Detection and Counting Using YOLO-mp
Deep Learning for Real-Time Malaria Parasite Detection and Counting Using YOLO-mp Open
Malaria in the rural and remote regions of tropical countries remain a major public health challenge. Early diagnosis and prompt effective treatment are the basis for the management of malaria and for reducing malaria mortality and morbidi…
View article: Evaluation of Depth Cameras for Use in Fruit Localization and Sizing: Finding a Successor to Kinect v2
Evaluation of Depth Cameras for Use in Fruit Localization and Sizing: Finding a Successor to Kinect v2 Open
Eight depth cameras varying in operational principle (stereoscopy: ZED, ZED2, OAK-D; IR active stereoscopy: Real Sense D435; time of flight (ToF): Real Sense L515, Kinect v2, Blaze 101, Azure Kinect) were compared in context of use for in-…
View article: Estimation of Fruit Load in Australian Mango Orchards Using Machine Vision
Estimation of Fruit Load in Australian Mango Orchards Using Machine Vision Open
The performance of a multi-view machine vision method was documented at an orchard level, relative to packhouse count. High repeatability was achieved in night-time imaging, with an absolute percentage error of 2% or less. Canopy architect…
View article: Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning
Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning Open
Machine vision from ground vehicles is being used for estimation of fruit load on trees, but a correction is required for occlusion by foliage or other fruits. This requires a manually estimated factor (the reference method). It was hypoth…
View article: Attempting to Estimate the Unseen – Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision With Deep Learning
Attempting to Estimate the Unseen – Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision With Deep Learning Open
Imaging systems mounted to ground vehicles are used to image fruit tree canopies for estimation of fruit load, but frequently need correction for fruit occluded by branches, foliage or other fruits. This can be achieved using an orchard &l…
View article: Deep Learning for Mango (Mangifera indica) Panicle Stage Classification
Deep Learning for Mango (Mangifera indica) Panicle Stage Classification Open
Automated assessment of the number of panicles by developmental stage can provide information on the time spread of flowering and thus inform farm management. A pixel-based segmentation method for the estimation of flowering level from tre…
View article: Deep Learning for Mango (<em>Mangifera Indica</em>) Panicle Stage Classification
Deep Learning for Mango (<em>Mangifera Indica</em>) Panicle Stage Classification Open
A pixel-based segmentation method was demonstrated to be confounded by developmental stage in estimation of flowering of mango. Categorization of panicles into three developmental stages was undertaken with a single and a two-stage deep le…
View article: Mango Fruit Load Estimation Using a Video Based MangoYOLO—Kalman Filter—Hungarian Algorithm Method
Mango Fruit Load Estimation Using a Video Based MangoYOLO—Kalman Filter—Hungarian Algorithm Method Open
Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree (“dual-view”) underestimates the fruit yield as fruit can be hidden f…
View article: In Field Fruit Sizing Using A Smart Phone Application
In Field Fruit Sizing Using A Smart Phone Application Open
In field (on tree) fruit sizing has value in assessing crop health and for yield estimation. As the mobile phone is a sensor and communication rich device carried by almost all farm staff, an Android application (“FruitSize”) was developed…
View article: Fruit load estimation in mango orchards - a method comparison
Fruit load estimation in mango orchards - a method comparison Open
The fruit load of entire mango orchards was estimated well before harvest using (i) in-field machine vision on mobile platforms and (ii) WorldView-3 satellite imagery. For in-field machine vision, two imaging platforms were utilized, with …