A Hybrid Approach of Preprocessing and Segmentation Techniques in Automatic Rice Variety Identification System

Komal, .; Sethi, Ganesh Kumar; Bawa, Rajesh K.

doi:10.3329/jsr.v14i1.54811

Cited by 7 publications

(4 citation statements)

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“…The study of [9] analyzes technologies for detecting roast-level coffee beans, but not all studies use ML techniques. Many studies related to smart agriculture [10,11] address machine learning techniques for classifications of other types of crops, such as rice [12][13][14][15], corn [16,17] and soybean [18]. A Comprehensive Review was used to achieve the proposed objective of synthesizing and understanding how ML techniques for coffee classification are presented in scientific research.…”

Section: Of 34mentioning

confidence: 99%

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

de C. Motta,

Vuillerme,

Pham

et al. 2024

Preprint

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In the realm of agribusiness, transformative shifts are underway, propelled by the growing demands and expanding scales of grain production. This evolution calls for a critical reevaluation of the existing paradigms in coffee production and marketing paradigms, with a specific focus on integrating Artificial Intelligence (AI). This work aims to review, synthesize, and summarize the available data regarding how Machine Learning (ML) has been used to detect and classify characteristics in coffee beans and leaves. For this purpose, a comprehensive literature review of the most significant research contributions describing the application of AI for advanced classification techniques in coffee agriculture has been carried out. Our analysis suggests that implementing AI technologies allows the classification of coffee, encompassing various attributes such as maturity, roast intensity, disease identification, flavor profiles, and overall quality. More largely, this technological advancement holds the potential to revolutionize coffee farming by providing producers and agricultural specialists with sophisticated tools to enhance production efficiency, minimize costs, and improve the accuracy and confidence of their decision-making processes.

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Section: Of 34mentioning

confidence: 99%

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

de C. Motta,

Vuillerme,

Pham

et al. 2024

Preprint

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“…Pre-processing has the major benefit of enhancing image data by removing undesired distortions and improving properties that are necessary for training the algorithm (Komal et al 2022), as shown in table 4. Scaling and segmentation are the next steps in the process.…”

Section: Public Datasetsmentioning

confidence: 99%

Revolutionizing agriculture: a comprehensive review of agribots, machine learning, and deep learning in meeting global food demands

Krishnan,

Karuppasamypandiyan,

Chandran

et al. 2024

Eng. Res. Express

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Agriculture is quickly transforming into a high-tech industry, which is drawing new professionals, investors, and firms. Technology is constantly improving, allowing farmers to increase their output capacity. This growth, modernization, and automation over time have led to a substantial increase in agricultural output. The United Nations is projecting that the population of our world will reach 9.7 billion by the year 2050. Hence, the world needs considerably more food, putting farmers under tremendous pressure to satisfy that need. The one of best solutions for this problem is using Agribots. Agribots assist farmers in a number of ways to enhance output yields. An Agribot, or agricultural robot, is a robot that is used for agricultural applications. Agribots utilize Machine Learning (ML) and Deep Learning (DL) techniques to improve agricultural production and output. ML and DL advancements have enabled agribots to locate, localize, and recognize objects in images and videos. This paper analyzes the three primary research areas in agriculture: The first area is Agricultural Operations, focusing on recent research findings regarding operations such as crop and weed detection, fruit detection, area detection, and disease detection. The next research area discusses the various hardware setups and types of agribots, and finally the machine vision systems of the Agribots. Comparative analyses of machine learning and deep learning approaches have been conducted, along with an exploration of the limitations and future research focus on Agribots.

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“…A hybrid algorithm is proposed to enhance the contrast, preprocess and segment the image for accurate classification [4]. They have also used a low pass filter, Gaussian filter to remove the noise by blurring the image so as to identify the rice varieties.…”

Section: Literature Reviewmentioning

confidence: 99%

Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique

John

Joseph

2023

IJ-AI

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Agriculture serves as the backbone of many countries. It provides food and other essential materials as per our requirement. Various kinds of diseases are affecting the agricultural crops which in turn reduce the quantity and quality of the agricultural sector. This can also lead to the decrease in food production thereby affecting the economic growth and development. Even though the symptoms and other impacts of the diseases are outwardly visible, manual identification of diseases and rectification is a tedious and time-consuming process. Therefore, detecting the diseases using an automatic computer-based model will be an effective solution. Image processing methods in conjunction with machine learning algorithms provide greater assistance in the field of plant disease detection. In the proposed work, plant leaf images of 10 crops are collected as the dataset. The images after acquisition are preprocessed using brightness preserving dynamic fuzzy histogram equalization (BPDFHE), an advanced version of histogram equalization and Gaussian filtering. The results are calculated and compared using the parameters such as peak signal to noise ratio (PSNR), structural similarity index (SSIM) and mean square error (MSE). This method performs more accurately than the existing preprocessing approaches.

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A Hybrid Approach of Preprocessing and Segmentation Techniques in Automatic Rice Variety Identification System

Cited by 7 publications

References 0 publications

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

Revolutionizing agriculture: a comprehensive review of agribots, machine learning, and deep learning in meeting global food demands

Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique

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