Classification Of Rice Plant Diseases Using the  Convolutional Neural Network Method

Priyangka, A A Je Veggy; Kumara, I Made Surya

doi:10.24843/lkjiti.2021.v12.i02.p06

Cited by 21 publications

(14 citation statements)

References 14 publications

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“…Model yang digunakan dalam klasifikasi penyakit padi tergantung pada Jaringan Konvolusi (CNN). CNN nantinya akan memproses data gambar, yang akan dibangun oleh jaringan menggunakan informasi dari proses yang akan dieksekusi [3]. Oleh karena itu, arsitektur LeNet mungkin juga tidak pantas dalam kasus-kasus tertentu yang menghasilkan kinerja yang buruk dari model.…”

Section: Pendahuluanunclassified

Sistem Klasifikasi Limbah Menggunakan Metode Convolutional Neural Network (CNN) Pada Webservice Berbasis Framework Flask

Dacipta

Putra²

2022

JINACS

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Abstrak— Di berbagai negara sampah merupakan permasalahan yang dihadapi sehari-hari seluruh aktivitas tidak bisa terhindar dari kata sampah. banyak sekali jenis–jenis sampah di lingkungan kita, dan kebanyakan masyarakat Indonesia masih belum bisa membedakan jenis sampah apa yang setiap hari mereka buang sehingga banyak sekali sampah yang tidak diklasifikasikan. Yang menyebabkan proses klasifikasi limbah pada pabrik daur ulang sangat susah untuk dilakukan. Maka dari itu bagaimana jika limbah yang ada diklasifikasikan menjadi 9 macam dan setiap masyarakat dapat membuang limbah sampah mereka sesuai dengan 9 macam class yang telah dibedakan yaitu battery, pakaian, e-limbah, kaca, bola lampu, metal, organic, kertas, plastic. sehingga mempermudah pabrik daur ulang dalam proses klasifikasi sampah. Oleh karena itu Sistem Klasifikasi Limbah Pada Web Service Berbasis Framework Flask akan sangat membantu masyarakat dalam mengklasifikasikan limbah sampah yang dihasilkan. Sistem klasifikasi limbah pada aplikasi berbasis web ini menggunakan metode Convolutional Neural Network (CNN). Metode CNN merupakan suatu metode deep learning yang dapat mengidentifikasi dan mengklasifikasi sebuah object pada citra digital. Penelitian ini menggunakan arsitektur VGGNet. VGGNet merupakan arsitektur dari Convolutional Neural Network. Penelitian ini menggunakan 9 class limbah sampah. Dataset yang digunakan yaitu 8371 citra limbah sampah. Dimana dataset tersebut digunakan untuk melakukan training data yang telah dibagi menjadi 1.122 citra battery, 729 pakaian, 624 e-limbah, 773 kaca, 651 bola lampu, 1092 metal, 671 organic, 1468 kertas dan 1241 plastic. Pada proses training dilakukan sebanyak 28 epoch, yang mendapatkan akurasi tertinggi 69,77% dengan loss terendah 0,34. Untuk data testing didapatkan hasil 64,45% accuracy.

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Section: Pendahuluanunclassified

Sistem Klasifikasi Limbah Menggunakan Metode Convolutional Neural Network (CNN) Pada Webservice Berbasis Framework Flask

Dacipta

Putra²

2022

JINACS

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“…With the enhancement of computing power of computer hardware and the explosive growth of data, deep learning technology has achieved very good results and been broadly applied in many areas such as speech recognition, image processing, and natural language processing (Domingues et al, 2022;Ning et al, 2022). The convolutional neural network is a very representative deep learning technology (Priyangka and Kumara, 2021). Its performances in computer vision missions such as image semantic segmentation, object detection, and classification recognition are far superior to other traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

Ning,

Liu,

Zhu

et al. 2023

Front. Plant Sci.

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There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human experience. But the method often could not achieve the desired effect, and was difficult to popularize on a large scale. Convolutional neural networks are good at extracting localized features from input data, converting low-level shape and texture features into high-level semantic features. Models trained by convolutional neural network technology based on existing data can extract common features of data and make the framework have generalization ability. Applying ensemble learning or transfer learning techniques to convolutional neural network can further improve the performance of the model. In recent years, convolutional neural network technology has been applied to the automatic recognition of rice diseases, which reduces the manpower burden and ensures the accuracy of recognition. In this paper, the applications of convolutional neural network technology in rice disease recognition are summarized, and the fruitful achievements in rice disease recognition accuracy, speed, and mobile device deployment are described. This paper also elaborates on the lightweighting of convolutional neural networks for real-time applications as well as mobile deployments, and the various improvements in the dataset and model structure to enhance the model recognition performance.

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“…Today, the data mining approach has developed rapidly. It has already been applied in more expansive fields [1], some of which are [2] and [3] used a data mining approach to the agriculture sector [4] in the health sector, [5], [6], and [7] in the biology sector, and [8] who applied a data mining approach in the financial industry. One of the data mining algorithms, which is often used, is a decision tree.…”

Section: Introductionmentioning

confidence: 99%

Efforts of Performance Optimization: The Experiment on Ten Accounting Datasets

Domas¹,

Rizkiawan²,

Rakhmadi

2022

LKJITI

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In the big data and digitalization era, fast-accurate decision-making has become a basic need, so data mining has a crucial role. The decision tree algorithm is quite commonly applied for classification functions, but performance level must always be evaluated for optimizing accuracy rate. Several optimization methods to accommodate these objectives include GA-bagging, PSO-bagging, forward selection, backward elimination, SMOTE, under-sampling, GA-Adaboost, and ABSMOTE-WIGFS. The results of the decision tree experiment on ten types of accounting-finance datasets used in this study obtained results with an average accuracy of 83.46%, an average precision of 65.64%, and an average AUC of 71.9%, while the majority of various optimizations are proven in improving the performance of decision tree algorithm where the application of ABSMOTE-WIGFS method is proven in providing the best rate with an average accuracy 87.71%, an average precision 87.09%, and an average AUC 84.87%, so it can be concluded that various optimization efforts are worth to be applied in case of accounting-finance themes for increasing the performance rate. Furthermore, the next research can prove these methods in other fields outside of accounting cases.

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Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method

Cited by 21 publications

References 14 publications

Sistem Klasifikasi Limbah Menggunakan Metode Convolutional Neural Network (CNN) Pada Webservice Berbasis Framework Flask

Sistem Klasifikasi Limbah Menggunakan Metode Convolutional Neural Network (CNN) Pada Webservice Berbasis Framework Flask

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

Efforts of Performance Optimization: The Experiment on Ten Accounting Datasets

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