Comparison of Machine Learning and Deep Learning Methods for Grape Cluster Segmentation

Mohimont, Lucas; Roesler, Mathias; Rondeau, Marine; Gaveau, Nathalie; Alin, François; Steffenel, Luiz Angelo

doi:10.1007/978-3-030-88259-4_7

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Nonetheless, these methods can obtain good performance and are capable of distinguishing between green grapes and leaves in some conditions (natural lighting, natural background, and well-calibrated distances and angles). We have shown in previous work [113] that better performance can be achieved with a fully convolutional network such as U-Net by allowing segmentation over uncalibrated images of white grapes. Our comparative study showed that pixel-wise classification is limited by its small input size and by the unbalanced nature of the vine images (the fruit area represents only a small part of the image).…”

Section: Performance Comparisonmentioning

confidence: 99%

Computer Vision and Deep Learning for Precision Viticulture

Mohimont

Alin

Rondeau³

et al. 2022

Agronomy

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During the last decades, researchers have developed novel computing methods to help viticulturists solve their problems, primarily those linked to yield estimation of their crops. This article aims to summarize the existing research associated with computer vision and viticulture. It focuses on approaches that use RGB images directly obtained from parcels, ranging from classic image analysis methods to Machine Learning, including novel Deep Learning techniques. We intend to produce a complete analysis accessible to everyone, including non-specialized readers, to discuss the recent progress of artificial intelligence (AI) in viticulture. To this purpose, we present work focusing on detecting grapevine flowers, grapes, and berries in the first sections of this article. In the last sections, we present different methods for yield estimation and the problems that arise with this task.

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Section: Performance Comparisonmentioning

confidence: 99%

Computer Vision and Deep Learning for Precision Viticulture

Mohimont

Alin

Rondeau³

et al. 2022

Agronomy

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“…The results showed difficulty to detect the bunches in the early and middle growing stage overall 66.96% detection accuracy. Lucas Mohimon et al, [37] have been compared deep-learning and machine-learning techniques for grape bunches cluster segmentation. In dataset had 200 images of white color grape in normal light condition and reached 86% of accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Literature Review of the Detection and Categorization of various Arecanut Diseases using Image Processing and Machine Learning Approaches

Puneeth¹,

Nethravathi

2021

IJAEML

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Background/Purpose: Every scholarly research project starts with a survey of the literature, which acts as a springboard for new ideas. The purpose of this literature review is to become familiar with the study domain and to assess the work's credibility. It also improves with the subject's integration and summary. This article briefly discusses the detection of disease and classification to achieve the objectives of the study. Objective: The main objective of this literature survey is to explore the different techniques applied to identify and classify the various diseases on arecanut. This paper also recommends the methodology and techniques that can be used to achieve the objectives of the study. Design/Methodology/Approach: Multiple data sources, such as journals, conference proceedings, books, and research papers published in reputable journals, were used to compile the essential literature on the chosen topic and collect information from the arecanuts research centre and many farmers in the south Canara and Udupi districts, before narrowing down the literature that is relevant to the research work. The shortlisted literature was carefully assessed by reading each paper and taking notes as appropriate. The information gathered is then examined to identify the potential gap in the study. Findings/Result: Based on the analysis of the papers reviewed, discussion with farmers and research center officers, it is observed that, not much work is carried out in the field of disease identification and classification on arecanut using machine learning techniques. This survey paper recommends techniques and the methodology that can be applied to identify and classify the diseases in arecanut and to classify them in to healthy and unhealthy. Research limitations/implications: The literature review mentioned in this paper are detection and classification of different diseases in arecanut. Originality/Value: This paper focuses on various online research journals, conference papers, technical books, and web articles. Paper Type: Literature review paper on techniques and methods used to achieve the objectives.

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Leveraging Computer Vision for Precision Viticulture

Vrochidou

Papakostas

2023

Computer Vision and Machine Learning in Agriculture, Volume 3

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Comparison of Machine Learning and Deep Learning Methods for Grape Cluster Segmentation

Cited by 3 publications

References 28 publications

Computer Vision and Deep Learning for Precision Viticulture

Computer Vision and Deep Learning for Precision Viticulture

A Literature Review of the Detection and Categorization of various Arecanut Diseases using Image Processing and Machine Learning Approaches

Leveraging Computer Vision for Precision Viticulture

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