Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks

Qur'ania, Arie; Harsani, Prihastuti; Triastinurmiatiningsih, Triastinurmiatiningsih; Wulandhari, Lili Ayu; Gunawan, Alexander Agung Santoso

doi:10.21512/commit.v14i1.5952

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…The researchers use Red, Green, and Blue (RGB) colour and Sobel edge detection for leaf shape recognition, as well as ANN for the identification procedure, to create the application of nutritional differentiation identification in cucumber [13]. The accuracy of the classification is less than 71%.…”

Section: Related Workmentioning

confidence: 99%

Citrus Tree Nutrient Deficiency Classification: A Comparative Study of ANN and SVM Using Colour-Texture Features in Leaf Images

Kamelia,

Khawa,

Nurmalasari

et al. 2024

IJCDS

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Nutrient deficiency in Citrus reticulata (mandarin orange) plants is a critical agricultural issue that has far-reaching implications. Such deficiencies not only compromise the overall health and vigor of the mandarin orange trees but also render them more susceptible to diseases and pest infestations. Addressing this concern is of principal importance to ensure sustainable citrus cultivation and enhance crop yield. In this research, we propose a comprehensive approach to tackle the pervasive problem of nutrient deficiencies in Citrus Reticulata var. Fremont leaves. Our approach leverages cutting-edge techniques in image processing and machine learning to provide accurate and efficient detection of these deficiencies. The image data is divided into four classes: N Deficiency, P Deficiency, K Deficiency and Normal. The file sizes are compressed using a lossless compression method, resulting in an average file size reduction of 96.99%. The second stage involves applying segmentation processes to the images using the Canny and Sauvola methods.Third stage involves extracting colour and texture features from the images. The feature values will be used for the classification process in the next stage. The segmentation process employs two methods, namely Canny and Sauvola, which effectively separate the leaves from the background. The detection process is evident in the feature extraction phase, which utilizes two features: colour and texture.The fourth stage involves the classification process based on the segmentation results methods, performed separately using Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. These process results in four datasets: Canny-ANN, Canny-SVM, Sauvola-ANN, and Sauvola-SVM. The highest accuracy is achieved by the Sauvola-ANN method, with a value of 93.75%.

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Section: Related Workmentioning

confidence: 99%

Citrus Tree Nutrient Deficiency Classification: A Comparative Study of ANN and SVM Using Colour-Texture Features in Leaf Images

Kamelia,

Khawa,

Nurmalasari

et al. 2024

IJCDS

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“…Feature extraction can be done using several methods based on the information characteristics. Statistical features in the RGB, HSV, and YUV color models represent color information from objects [24], [32]. While some methods, such as GLCM, Sobel, etc., can represent texture and shape information on leaf objects [17].…”

Section: Related Workmentioning

confidence: 99%

“…However, those methods cannot handle data with high variance. Learning methods such as supervised learning have also been used, such as MLP, ANN, KNN, SVM, and others [24], [50], [51]. MLP shows promising results for data in different lighting conditions [52].…”

Section: Related Workmentioning

confidence: 99%

“…The percentage of macronutrient deficiency estimation using plant RGB images has been carried out [16], [23]. Color characteristics in plants have been used as in wheat using Multivariate Linear Regression, Genetic Algorithm, Back Propagation-ANN, and KNN [23], [24]. Texture features have also been developed using the Support Vector Machine (SVM) algorithm [25].…”

Section: Introductionmentioning

confidence: 99%

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Design of an Intelligent Hydroponics System to Identify Macronutrient Deficiencies in Chili

Rahadiyan¹,

Hartati²,

Wahyono³

et al. 2022

IJACSA

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Nutrient contents are important for plants. Lack of macronutrients causes plant damage. Several macronutrient deficiencies exhibit similar visual characteristics that are difficult for ordinary farmers to identify. Collaboration between Computer Vision technology and IoT has become a nondestructive method for nutrient monitoring and control, included in the hydroponic system. Computer vision plays a role in processing plant image data based on specific characteristics. However, the analysis of one characteristic cannot represent plant health. In addition, knowing the percentage of macronutrient deficiencies is also needed to support precision agriculture systems. Therefore, we propose a Multi Layer Perceptron architecture that can perform multi-tasks, namely, identification and estimation. In addition, the optimal architecture will also be sought based on the characteristics of the combination of three features in the form of texture, color, and leaf shape. Based on analysis and design, our proposed model has a high potential for identifying and estimating macronutrient deficiency at the same time as well and can be applied to support precision agriculture in Indonesia.

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“…Selain itu, pengabdian ini juga berpotensi untuk meningkatkan kesadaran pemerintah daerah terhadap potensi Desa Sukamulya, sehingga dapat menjadi prioritas dalam pembangunan di masa mendatang. Dengan demikian, pengabdian ini tidak hanya memberikan manfaat langsung bagi masyarakat setempat, tetapi juga berkontribusi pada pembangunan wilayah secara keseluruhan (Noor, 2022;Harsani 2023)…”

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Pemanfaatan Media Sosial dalam Pengembangan Desa Wisata Sukamulya, Langkaplancar, Pangandaran

Yudha,

Carli,

Sinaga

et al. 2024

ag. abdimas. galuh. jurnal. pengabdian. kepada. masy.

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yang bertujuan untuk mengembangkan potensi Desa Sukamulya dengan memanfaatkan media sosial. Metode pelaksanaan program pembinaan ini adalah hybrid (blended), menggabungkan antara kegiatan tatap muka langsung (luring) dan kegiatan secara virtual (daring) melalui media sosial seperti Instagram, YouTube, dan Tiktok. Hasil program pembinaan ini adalah beberapa konten yang telah diunggah pada Instagram, YouTube Short, dan Tiktok serta terlaksananya kegiatan workshop yang mengusung tema Pemanfaatan Teknologi Digital dan Informasi Berbasis Potensi Desa. Program Pemanfaatan Media Sosial dalam Pengembangan Desa Wisata pada Desa Sukamulya, Langkaplancar, Pangandaran merupakan upaya Pengabdian Mahasiswa kepada Masyarakat Universitas Padjadjaran. Tujuan utamanya adalah mengoptimalkan potensi Desa Sukamulya dengan memanfaatkan platform media sosial seperti Instagram, YouTube, dan Tiktok. Metode pelaksanaan program ini melibatkan kegiatan tatap muka langsung dan virtual. Hasilnya, beberapa konten telah diunggah dan kegiatan workshop dilaksanakan.

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Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks

Cited by 4 publications

References 15 publications

Citrus Tree Nutrient Deficiency Classification: A Comparative Study of ANN and SVM Using Colour-Texture Features in Leaf Images

Citrus Tree Nutrient Deficiency Classification: A Comparative Study of ANN and SVM Using Colour-Texture Features in Leaf Images

Design of an Intelligent Hydroponics System to Identify Macronutrient Deficiencies in Chili

Pemanfaatan Media Sosial dalam Pengembangan Desa Wisata Sukamulya, Langkaplancar, Pangandaran

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