Identifikasi Penyakit Tanaman Ubi Kayu Berdasarkan Citra Daun Menggunakan Metode Probabilistic Neural Network (PNN)

Sari, Yuslena; Alkaff, Muhammad; Rahman, Mijani

doi:10.31603/komtika.v5i1.4605

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…This makes orange plants susceptible to pest and disease attacks. This finding aligns with research conducted by (Lestari et al, 2018), where the reasons for decreased harvests were linked to pest and disease infestations. The impact of these diseases can result in lower-quality fruits, emphasizing the importance for farmers to ensure their crops remain free from pests and disease-causing agents (Mangla et al, 2022).…”

Section: Introductionsupporting

confidence: 90%

Detection And Classification of Citrus Diseases Based on A Combination of Features Using the Densenet-169 Model

Firdaus,

Utami,

Ariatmanto

2023

SinkrOn

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This research is motivated by the urgent need to improve the capability of detecting diseases in citrus plants, which play a crucial role in maintaining agricultural sector productivity. Diseases such as blackspot, canker, and greening can have a serious impact on harvest yields and overall plant health. Therefore, this research aims to enhance the accuracy in classifying diseases in citrus plants by applying a Deep Learning approach. In this study, we chose to adopt the DenseNet-169 architecture and conducted experiments with two different scenarios: one using original features and the other using a combination of features. This method was employed to classify four different classes, namely blackspot, canker, greening, and healthy plants, using an LDI dataset consisting of 3,000 images. This dataset was divided into three parts, namely training, testing, and validation sets. The experimental results indicate that the DenseNet-169 model with the use of feature combination achieved the highest accuracy rate at 96.66%, whereas the model using only original features achieved 91.33%. This significant improvement of 5.33% in accuracy provides strong evidence that the feature combination approach has a highly meaningful positive impact on the model's ability to identify and classify diseases in citrus plants. These findings confirm that the use of feature combinations is a highly effective strategy in improving the model's performance in disease classification tasks in citrus plants.

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Section: Introductionsupporting

confidence: 90%

Detection And Classification of Citrus Diseases Based on A Combination of Features Using the Densenet-169 Model

Firdaus,

Utami,

Ariatmanto

2023

SinkrOn

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“…It is essential to understand both causal agents and the severity of the symptoms for effective pest and disease management [3]. If not treated appropriately, these diseases can cause significant leaf damage and crop fatality [4].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

2022

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Coffee is the world’s most traded tropical crop, accounting for most export profits, and is a significant source of income for the countries in which it is produced. To meet the needs of the coffee market worldwide, farmers need to increase and monitor coffee production and quality. Coffee leaf disease is a significant factor that decreases coffee quality and production. In this research study, we aim to accurately classify and detect the diseases in four major types of coffee leaf disease (phoma, miner, rust, and Cercospora) in images using deep learning (DL)-based architectures, which are the most powerful artificial intelligence (AI) techniques. Specifically, we present an ensemble approach for DL models using our proposed layer. In our proposed approach, we employ transfer learning and numerous pre-trained CNN networks to extract deep characteristics from images of the coffee plant leaf. Several DL architectures then accumulate the extracted deep features. The best three models that perform well in classification are chosen and concatenated to build an ensemble architecture that is then given into classifiers to determine the outcome. Additionally, a data pre-processing and augmentation method is applied to enhance the quality and increase the data sample’s quantity to improve the training of the proposed method. According to the evaluation in this study, among all DL models, the proposed ensemble architecture outperformed other state-of-the-art neural networks by achieving 97.31% validation. An ablation study is also conducted to perform a comparative analysis of DL models in different scenarios.

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“…Kemudian pada penelitian [13] yaitu melakukan pengklasifikasian dengan membandingkan Naïve Bayes Classifier (NBC) dan Decision Tree, didapatkan hasil NBC lebih akurat dibandingkan dengan Decision tree dengan tingkat akurasi sebesar 93.96%. Selanjutnya pada penelian lainnya dilakukan pengklasifikasian dengan menggunakan Algoritma Probabilistic Neural Network (PNN) didapatkan hasil bahwa kinerja dari PNN sangat baik dibandingkan dengan algoritma lain [14].…”

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Penerapan Algoritma Supervised Learning untuk Klasifikasi Program Keluarga Harapan

Hartawan,

Erkamim,

Rachmawati

et al. 2023

MALCOM

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Penelitian ini dimaksudkan untuk melakukan klasifikasi terhadap penerima bantuan Program Keluarga Harapan (PKH) Kota Pekanbaru dengan membandingkan tiga metode sekaligus yaitu K-Nearest Neighbor (KNN), Probalistic Neural Network (PNN) dan Naive Bayes Classifier (NBC).Atribut yang digunakan dalam proses klasifikasi adalah Jumlah Anak SD, Jumlah Anak SMP, Jumlah Ibu Hamil, dan Jumlah Anak dibawah lima tahun (Balita), atribut ini berdasarkan panduan Tim Nasional Penangulangan dan Penanganan Kemiskinan (TNP2K) Kementrian Sosial Republik Indonesia. Proses klasifikasi dilakukan terhadap Rumah Tangga Sangat Miskin (RTSM) sebagai data training dengan jumlah 450 data sebagai data testing dengan jumlah 10 data, sehingga dengan melakukan perbandingan didapatlah hasil akurasi yang bervariasi diantara ke-tiga metode. Metode Nave Bayes (NBC) memiliki hasil akurasi yanng paling tinggi dengan hasil akurasi 80%, yang dibandingkan dengan metode KNN 20% dan PNN 10% maka Metode Nave Bayes(NBC) ditetapkan menjadi metode terbaik untuk kasus pengklasifikasian program keluarga harapan (PKH) Kota Pekanbaru.

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Identifikasi Penyakit Tanaman Ubi Kayu Berdasarkan Citra Daun Menggunakan Metode Probabilistic Neural Network (PNN)

Cited by 3 publications

References 12 publications

Detection And Classification of Citrus Diseases Based on A Combination of Features Using the Densenet-169 Model

Detection And Classification of Citrus Diseases Based on A Combination of Features Using the Densenet-169 Model

Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

Penerapan Algoritma Supervised Learning untuk Klasifikasi Program Keluarga Harapan

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