Design of fuzzy inference system for apple ripeness estimation using gradient method

Hamza, Raja; Chtourou, Mohamed

doi:10.1049/iet-ipr.2018.6524

Cited by 15 publications

(3 citation statements)

References 25 publications

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“…This fact is supported by [16] who writes that it is commonly believed that the ideal k is between 5 and 10. Furthermore, other scholars [17][18][19] conclude that CV is better performed with k = 10 or k = 20 since these numbers have been empirically proved to give low test error.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Framework for Breast Cancer Prediction Using Classification and Regression Tree

Agaal¹,

Essgaer²

2022

Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and

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Several machine learning algorithms have been proposed in recent years to design accurate classification systems for a wide range of diseases such as cancers, hepatitis, and coronavirus. In this study, the Classification and Regression Tree (CART) is proposed to predict breast cancer in the early stage, later applied to real data collected from the Sebha oncology center. The study focuses on improving the CART accuracy through several methods: (1) cross-validation, (2) dimensionality reduction and (3) hyper-parameter tuning. However, two crossvalidation strategies have been investigated namely: The K fold and stratified fold, followed by dimensionality reduction to determine the most effective features using two methods, namely: recursive feature elimination with cross-validation and principal component analysis, and lastly, investigating the most optimal CART parameters using two optimization algorithms, namely: grid search, and random search. The experimental results have shown that the best CART model which achieved 97% accuracy uses a stratified fold as a cross-validation strategy, recursive feature elimination with cross-validation as dimensionality reduction, and grid search as parameters tuning algorithm. Moreover, when compared to the original CART, the accuracy of the proposed CART has improved from 63% to 97%.

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Section: Literature Reviewmentioning

confidence: 99%

An Optimized Framework for Breast Cancer Prediction Using Classification and Regression Tree

Agaal¹,

Essgaer²

2022

Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and

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“…Fuzzy controllers are widely used in various systems, objects, machines, and devices, including household equipment, autonomous vehicles, and complex operating systems 3 , 8 , 11 – 15 . Fuzzy controllers are also applied in agriculture 3 , mainly to control agricultural robots 16 , 17 , but also to manage greenhouses 18 , sort fruit and vegetables 19 – 21 , monitor soil parameters 22 , control irrigation systems 23 – 25 , and provide decision-making support in planning and performing farming operations 26 – 31 .…”

Section: Introductionmentioning

confidence: 99%

The use of a Mamdani-type fuzzy model for assessing the performance of a boom stabilization systems in a field sprayer

Kaliniewicz,

Szczyglak,

Lipiński

et al. 2023

Sci Rep

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Fuzzy logic models are increasingly used to control simple and complex devices, as well as entire operating systems. In this study, a fuzzy logic model was applied to assess the performance a boom stabilization system in a field sprayer. The model was tested on a field sprayer with a trapezoid system for stabilizing the sprayer boom with a length of 21 m. Measuring cables for registering the displacement of the boom's terminal segments (right and left) in the vertical and horizontal plane were installed on the sprayer. The field sprayer was connected to a tractor. The model was based on two linguistic variables: "absolute displacement of the boom's terminal segments" and "boom stability index". It was assumed that the sprayer boom was stable when the displacement of the boom's terminal segments did not exceed 0.25% of boom length. The study demonstrated that the proposed model can be reliably used to assess boom stability in real time (during field operations). The time required to achieve boom stability was more than 2.5 times shorter in the vertical than in the horizontal plane, which can be attributed mainly to the structure of the stabilization system. The proposed model is universal, and it can be applied to evaluate other boom stabilization systems in field sprayers.

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“…Meanwhile, classifier and regression trees (CART) was applied for tomato detection [17]. In another study, fuzzy classification method based on colour features was adopted to estimate apple ripeness [18]. The above‐mentioned researches adopted hand‐crafted features approach and machine learning classifier for crop detection.…”

Section: Introductionmentioning

confidence: 99%

An optimized YOLO‐based object detection model for crop harvesting system

Junos

Khairuddin

Thannirmalai³

et al. 2021

IET Image Processing

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The adoption of automated crop harvesting system based on machine vision may improve productivity and optimize the operational cost. The scope of this study is to obtain visual information at the plantation which is crucial in developing an intelligent automated crop harvesting system. This paper aims to develop an automatic detection system with high accuracy performance, low computational cost and lightweight model. Considering the advantages of YOLOv3 tiny, an optimized YOLOv3 tiny network namely YOLO-P is proposed to detect and localize three objects at palm oil plantation which include fresh fruit bunch, grabber and palm tree under various environment conditions. The proposed YOLO-P model incorporated lightweight backbone based on densely connected neural network, multi-scale detection architecture and optimized anchor box size. The experimental results demonstrated that the proposed YOLO-P model achieved good mean average precision and F1 score of 98.68% and 0.97 respectively. Besides, the proposed model performed faster training process and generated lightweight model of 76 MB. The proposed model was also tested to identify fresh fruit bunch of various maturities with accuracy of 98.91%. The comprehensive experimental results show that the proposed YOLO-P model can effectively perform robust and accurate detection at the palm oil plantation. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Design of fuzzy inference system for apple ripeness estimation using gradient method

Cited by 15 publications

References 25 publications

An Optimized Framework for Breast Cancer Prediction Using Classification and Regression Tree

An Optimized Framework for Breast Cancer Prediction Using Classification and Regression Tree

The use of a Mamdani-type fuzzy model for assessing the performance of a boom stabilization systems in a field sprayer

An optimized YOLO‐based object detection model for crop harvesting system

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