Pendeteksian Penyakit pada Daun Cabai dengan Menggunakan Metode Deep Learning

Rosalina, Rosalina; Wijaya, Ardi

doi:10.28932/jutisi.v6i3.2857

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…In detecting chili disease, the use of Convolutional Neural Networks (CNN) has also been done by previous researchers. One such study being conducted by Rosalina and Wijaya [17]. They developed a desktop applicationbased system capable of capturing images of chili leaves using a Raspberry Pi camera, performing image processing on these images, and subsequently determining whether the leaves were healthy or diseased, along with providing probability values.…”

Section: Literature Reviewmentioning

confidence: 99%

Chili Leaf Health Classification using Xception Pretrained Model

Wulandari,

Munggaran,

Setiawan

et al. 2024

SISTEMASI

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As one of the high-demand horticultural crops, chili peppers have a significant impact on the economy of Indonesia. However, despite the growing demand and interest in chili peppers, their production often faces disruptions due to crop failures. One of the leading causes of such failures is pests and diseases. Among all parts of the chili plant, chili leaves are the most susceptible to damage. Distinguishing between healthy and unhealthy chili leaves can serve as an early detection step for chili diseases and preventive measures to contain their spread. Convolutional Neural Network (CNN) are effective algorithms for image classification. The development of CNN has led to the use of models previously trained on large datasets to accurately classify relatively small datasets. One such pretrained model known for its exceptional classification capabilities is Xception. By utilizing the pretrained Xception model trained on the ImageNet dataset for the classification of healthy and unhealthy chili leaf images, our model achieved an accuracy of 91% on a dataset containing 2136 images. Furthermore, the model achieved a 100% success rate by correctly predicting all 10 out of 10 given images.

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Section: Literature Reviewmentioning

confidence: 99%

Chili Leaf Health Classification using Xception Pretrained Model

Wulandari,

Munggaran,

Setiawan

et al. 2024

SISTEMASI

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“…Metode ini murah dan mudah untuk mendeteksi sehingga sangat bermanfaat dan membantu para petani. [5], [6].…”

Section: Pendahuluanunclassified

Pemanfaatan Yolo Untuk Deteksi Hama Dan Penyakit Pada Daun Cabai Menggunakan Metode Deep Learning

Yasen,

Rifka,

Vitria

et al. 2023

EJI

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Chili plants are one of the horticultural crops in Indonesia which have great potential in the Indonesian economy. However, crop failure often occurs. One of the main factors causing this is pest and disease attacks on chili plants. This requires early prevention which can reduce losses. With today's technological developments, prevention can be done easily and economically by using deep learning methods. YOLO is a deep learning algorithm that is commonly used to detect objects in real time. There are 4 classes that will be tested, namely leaves affected by yellow virus disease, leaf spot, thrips pests, and healthy chili leaves. Testing was carried out with a web-based application created with the flask framework. The accuracy results of the YOLO model training process with epoch 150 were 73%. The precision, recall and mAP values obtained were 77.4%, 67.1% and 75.1%. Testing produces accuracy above 74%. The results of this research still produce accuracy that is not high enough, but the application can be used to detect it well and is quite accurate.

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“…This is increasingly strengthened by evidence from data from the USDA, which ranked Indonesia as the second ASEAN country active in producing and exporting coffee (Alfian, 2021). However, Indonesia cannot meet 4.9% (Windiawan et al, 2019). This is due, in part, to a decrease in productivity and quality of coffee produced due to plants being susceptible to attacks from various pests and diseases on coffee plants (R. Lumbanraja et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Analysis Using Otsu Thresholding and Mean Denoising for the Identification Coffee Plant Diseases

Ami Rahmawati,

Yulianti,

Nurajizah

2023

J. Ris. Inform.

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In Indonesia, coffee is one of the plantation products with a relatively high level of productivity and is a source of foreign exchange income for the country. However, unfortunately, certain factors can threaten productivity and quality in cultivating coffee plants, one of which is rust leaf disease. This disease causes disturbances in photosynthesis, thereby reducing plant yields. Therefore, to maintain and control productivity in coffee cultivation, this research carried out the process of observing coffee leaf images through segmentation using the Otsu Thresholding and Mean Denoising methods. The entire series of processes in this research was carried out using the Python programming language and succeeded in providing output in the form of image comparisons showing areas affected by Rust Leaf disease using the Otsu thresholding method alone and the Otsu thresholding method combined with a non-local means denoising algorithm. The test results prove that the Otsu thresholding method with the non-local means denoising algorithm has a smaller MSE value. It is the most optimal method for handling coffee leaf disease image segmentation with an accuracy level of 88%. It is hoped that this research can support farmers in providing insight into early detection of coffee plant diseases and increasing productivity through visual analysis.

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Pendeteksian Penyakit pada Daun Cabai dengan Menggunakan Metode Deep Learning

Cited by 5 publications

References 4 publications

Chili Leaf Health Classification using Xception Pretrained Model

Chili Leaf Health Classification using Xception Pretrained Model

Pemanfaatan Yolo Untuk Deteksi Hama Dan Penyakit Pada Daun Cabai Menggunakan Metode Deep Learning

Image Segmentation Analysis Using Otsu Thresholding and Mean Denoising for the Identification Coffee Plant Diseases

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