Deteksi Penyakit Tomat melalui Citra Daun menggunakan Metode Convolutional Neural Network

Sigitta, Rito Cipta; Saputra, Roni Halim; Fathulloh, Fathulloh

doi:10.28989/avitec.v5i1.1404

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The ability to process images has demonstrated that CNN is the best option for identifying diseases in mango trees. Because CNN mimics the image recognition system found in the human visual cortex and can analyze picture data with high accuracy, it is a useful method for recognizing images [4].…”

Section: Introductionmentioning

confidence: 99%

Detection of mango leaf disease using the convolution neural network method

Saragih,

Nur Azizi,

Alfattah Atalarais

et al. 2024

tekno

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Mango trees are a common plant in Indonesia. In 2021, 2.84 million tonnes of mangoes were produced in Indonesia, according to the Central Statistics Agency (BPS). You can eat mangoes either ripe or unripe. Additionally, this fruit can be turned into meals and beverages. Many farmers grow mango plants, however, Some illnesses can infect mangoes and lead to crop failure or poor fruit quality. Fungal infections like anthracnose and black fungus, often known as black fungus, affect mango plants, but many farmers continue to mistakenly believe they are identifying plant illnesses and pests. To the type of disease in mango plants, the Convolutional Neural Network (CNN) method was applied in this study. It has been demonstrated that CNN is a very efficient way of processing images and identifying key elements for intricate pattern recognition. A total of 1,405 leaf photos from three different categories—525 anthracnose images, 656 black sooty mold images, and 224 healthy images—were used as samples for CNN to identify illnesses in mango plants. This image data was taken from the kaggle.com website. The CNN model is trained using distinct datasets into training data and validation data after data collection and preprocessing. On training data, the model is 95% accurate, while on validation data, it is 98% accurate. By feeding photos of mango leaves into the model and evaluating the predictions, the detection is put into practice. Action can be taken to control the illness in these mango trees based on prediction findings showing the presence of disease with a decent amount of confidence

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Section: Introductionmentioning

confidence: 99%

Detection of mango leaf disease using the convolution neural network method

Saragih,

Nur Azizi,

Alfattah Atalarais

et al. 2024

tekno

View full text Add to dashboard Cite

show abstract

“…Pada proses cascade classifier ini akan terdapat 38 cascade yang akan menentukan bahwa obyek yang terdeteksi merupakan wajah dan bukan wajah. Berdasarkan hal tersebut, dari 38 cascade tersebut hanya basis wajah yang melewati semua cascade tersebut di mana basis yang bukan wajah akan terlebih dahulu gugur pada cascade awal[12].…”

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Implementasi Pengenalan Wajah dengan Metode Haar Cascade Classifier pada Akses Boarding House

Malo,

Dirgantara,

Subairi

2023

Jurnal Teknik Elektro AVITEC

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Penelitian ini ditujukan untuk membuat sistem keamanan termonitor dengan pengenalan wajah pada boarding house dengan mengontrol akses keluar-masuk boarding house. Pengenalan wajah dilakukan menggunakan metode Haar Cascade Classifier. Metode ini merupakan salah satu metode yang digunakan dalam pendeteksian wajah manusia dan memiliki kelebihan komputasi yang cepat karena hanya bergantung pada jumlah piksel dalam persegi dari sebuah image. Implementasi sistem ini dibuat dengan menggunakan kontroler Raspberry Pi dengan kamera, ESP32, sensor ultrasonik, motor DC beserta driver BTS7960, prototipe akses boarding house, buzzer, dan kartu RFID. Penelitian ini berhasil dibangun dan mengimplementasikan metode Haar cascade classifier pada akses boarding house dengan hasil pengenalan wajah yang akurat dari 8 data sampel serta rata-rata akurasi yang diperoleh sebesar 80% dengan intensitas cahaya 100±4 lux.

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Identification of Disease in Tomato Plants Based on Image: Systematic Literature Review

Mansur,

Mustopa,

Habibullah

et al. 2024

Engineering Headway

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Plant diseases are taken into consideration as one of the major factors affecting food production and minimizing losses in production, and plant diseases must have rapid detection and recognition. Tomato (Lycopersicon esculentum) is one of the foodstuffs that are rich in nutrition and nutrients. Tomatoes are widely consumed by many countries including Indonesia and are hunted to be created in various spices, so tomatoes have a role in the economy due to the large demand. The recent enlargement of device studying techniques has found its application in plant disease detection especially tomato plants, presenting a powerful tool with relatively accurate effects. In this study, we present a systematic literature review aimed at identifying disease images in tomato plants. In this regard, we review 16 studies selected in the last five years with different approaches to address aspects related to tomato plant disease detection.

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Deteksi Penyakit Tomat melalui Citra Daun menggunakan Metode Convolutional Neural Network

Cited by 3 publications

References 11 publications

Detection of mango leaf disease using the convolution neural network method

Detection of mango leaf disease using the convolution neural network method

Implementasi Pengenalan Wajah dengan Metode Haar Cascade Classifier pada Akses Boarding House

Identification of Disease in Tomato Plants Based on Image: Systematic Literature Review

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