Segmentation of oil palm crop bunch from tree images

Siddesha, S; Niranjan, S. K.; Aradhya, V. N. Manjunath

doi:10.1109/smarttechcon.2017.8358639

Cited by 4 publications

(2 citation statements)

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“…However, the choice of clustering method and parameters significantly influences the segmentation quality. Siddesha et al (2017) compared different clustering methods for palm fruit segmentation and found that the k-means method resulted in under-segmentation, while the fuzzy c-means (FCM) method led to oversegmentation. This emphasises the importance of selecting an appropriate clustering algorithm for the specific application.…”

Section: P R E S Smentioning

confidence: 99%

A Picture of Ripeness: Investigating Image-Based Techniques for Oil Palm Fruit Grading

ROSBI

2024

JOPR

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Oil palm is a highly efficient crop that can produce more oil per unit of land than any other type of oil seed. Palm oil it is in high demand, and its production can significantly contribute to a country's economic growth. However, the traditional method of grading palm fruit is still prevalent in Malaysia, which requires skilled workers to classify the harvested fruit according to its ripeness. This approach can be costly and labour-intensive. Therefore, several studies have investigated automated palm fruit classification techniques that could reduce costs and labour in the industry. This paper provides a review of these studies, with a specific focus on vision-based classification techniques. The article discusses approaches based on image processing encompassing pre-processing, feature extraction and classification steps. The survey's resultsindicate that there is a lack of technique to effectively address outdoor images, such as colour correction methods. Therefore, further research is necessary to develop a better segmentation and colour correction procedures. Overall, the findings of this study could help improve the efficiency and sustainability of palm oil production, thereby contributing to economic growth and environmental conservation.

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Section: P R E S Smentioning

confidence: 99%

A Picture of Ripeness: Investigating Image-Based Techniques for Oil Palm Fruit Grading

ROSBI

2024

JOPR

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“…With the goal of determining the ripeness of arecanut bunches, the author presented a computer vision-based approach for segmentation utilizing active contouring. Siddesha et al, [33] have segmented of oil palm crop bunch images using supervised and unsupervised approaches. The author used Hill climbing, Growcut, Random Walker, MSRM algorithms for supervised techniques, and K-Means, Fuzzy-C-Means algorithms for unsupervised techniques on a dataset of 100 bunch photos.…”

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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