Soil Color Detection Using Knn Classifier

Maniyath, Shima Ramesh; Hebbar, Ramachandra; Akshatha, K N; Architha, L S; S, Rama Subramoniam

doi:10.1109/icdi3c.2018.00019

Cited by 14 publications

(9 citation statements)

References 4 publications

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“…In several works, SVM was applied to classify satellite images, becoming a standard choice for visual recognition in agriculture [63][64][65][66]. At the same time, k Nearest Neighbors (KNN) algorithm was an alternative learning-based approach, used in various soil classification tasks [67][68][69][70].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

et al. 2021

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Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

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Section: Machine Learning Methodsmentioning

confidence: 99%

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

et al. 2021

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“…A paper developed a more efficient algorithm for detecting soil color using the KNN algorithm. In this algorithm, the results obtained are good enough to classify the soil type based on soil color [28]. In the following paper, the classification of soil uses CNN algorithms.…”

Section: Related Workmentioning

confidence: 99%

Effective Soil Type Classification Using Convolutional Neural Network

Ronaldo¹

2021

ijicom

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Soil classification is a growing research area in the current era. Various studies have proposed different techniques to deal with the issues, including rule-based, statistical, and traditional learning methods. However, the plans remain drawbacks to producing an accurate classification result. Therefore, we propose a novel technique to address soil classification by implementing a deep learning algorithm to construct an effective model. Based on the experiment result, the proposed model can obtain classification results with an accuracy rate of 97% and a loss of 0.1606. Furthermore, we also received an F1-score of 98%.

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“…KNN ( K-Nearest Neighbor) adalah algoritma pembelajaran AI, algoritma ini termasuk dalam supervised learning. Algoritma ini mengasumsikan bahwa tiap instance dari data adalah bertetangga dan instance yang berdekatan dianggap memiliki kelas yang sama, tujuan dari algoritma ini adalah untuk mengklasifikasikan objek baru berdasarkan atribut dan sampel dari training data [4].…”

Section: K-nearest Neighborunclassified

Rancang Bangun Aplikasi Web Pendeteksi Warna Pada Pixel Gambar Dengan KNN Classifier

Sakti

Mardiana

Pradipta

2023

JITET

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Abstrak. Manusia memiliki keterbatasan dalam mendeteksi ratusan warna dalam gambar secara presisi. Banyak orang awam mengalami kesulitan dalam mengklasifikasikan sebuah warna pada benda ,pada umumnya orang awam hanya mengetahui warna dasar atau warna yang umum. Sehingga aplikasi web bernama DYSCO ini dibuat untuk mempermudah dalam mengklasifikasikan sebuah warna. DYSCO merupakan aplikasi pendeteksi warna berbasis web dengan menggunakan metode K-Nearest Neighbors. Aplikasi web ini dibuat dengan menggunakan bahasa pemrograman Python dan kerangka kerja mikro Flask. Aplikasi web ini dapat menampilkan informasi warna pixel suatu gambar yang di-upload pengguna ke dalam web, dengan mengektraksi fitur RGB pada gambar tersebut. Metode pengembangan yang digunakan adalah AI Project Cycle dengan 6 tahapan yaitu Problem Scoping, Data Acquisition, Data Exploration, Modelling ,Evaluation dan Deployment. Pembuatan aplikasi web pendeteksi warna pada pixel ini telah berhasil dilakukan dan sudah mampu mendeteksi dan memberikan informasi warna yang di klik pengguna pada gambar, dengan akurasi yang tinggi yaitu 0,844. Aplikasi web juga sudah dapat memenuhi fungsi fungsi utamanya, ditunjukan melalui pengujian menggunakan teknik Usability Testing berdasarkan ISO 9421-11 untuk mengukur Efectivity, Eficiency dan Satisfaction, dengan 10 orang responden awam. Hasil Usability Testing mendapatkan skor yang sangat bagus dengan nilai efectivity yaitu 100% dengan predikat sangat efektif, nilai eficiency yaitu 32,6 detik dengan predikat sangat cepat, dan nilai satisfaction yaitu 84 dengan predikat dapat diterima (acceptable).

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Soil Color Detection Using Knn Classifier

Cited by 14 publications

References 4 publications

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

Effective Soil Type Classification Using Convolutional Neural Network

Rancang Bangun Aplikasi Web Pendeteksi Warna Pada Pixel Gambar Dengan KNN Classifier

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