Derin Öğrenme ile Bitki Hastalıklarının Tespiti

Aslan, Muzaffer

doi:10.31590/ejosat.883787

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…This method was implemented using a pre-trained AlexNet model and tested on a dataset containing real disease images from the TRB1 region. 99.30% accuracy was achieved in the experiments[4]. In the study byTerzi et al (2023), a new CNN…”

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confidence: 85%

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

Acar,

Özcan

2024

Balkan Journal of Electrical and Computer Engineering

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Fruit and vegetable diseases have an important place in the food sector in terms of sustainable agricultural policies. Thus, ıt affects tissues, targeting and negatively impacting the food supply. In this study, Two separate Deep Learning (CNN, AlexNet) models were employed to detect this difference, visual damage and surface marker seen in fruits and vegetables. 22 strawberries and 18 tomato images were used for this analysis, and than data augmentation was implemented 600 images out of 40 images using the image reproduction broadcast. As a result, 83.3% success was achieved.

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mentioning

confidence: 85%

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

Acar,

Özcan

2024

Balkan Journal of Electrical and Computer Engineering

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“…However, this increases the transaction costs and leads to time loss [1]. Today, methods based on image processing technology are frequently used for the detection of plant diseases [2]. Convolutional Neural Network (CNN) models, one of the most up-to-date image processing technologies, are widely used in many areas such as face recognition, climate, health, and agriculture.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Standard and Pretrained CNN Models for Potato, Cotton, Bean and Banana Disease Detection

Kiziloluk¹

2021

NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University

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Plant diseases lead to a significant decrease in product efficiency and economic losses for producers. However, early detection of plant diseases plays an important role in preventing these losses. Today, Convolutional Neural Network (CNN) models are widely used for image processing in many fields such as face recognition, climate, health, and agriculture. But in these models, the weights of the layers are randomly initialized during training, which increases training time and decreases performance. With the method known as Transfer Learning in the literature, CNN models are trained on large databases such as ImageNet. Then, pretrained CNN models are created using the weights obtained in this training. Thus, training time decreases while performance improves. In this study, standard and pretrained versions of popular CNN models DarkNet-19, GoogleNet, Inception-v3, Resnet-18, and ShuffleNet have been used for automatic classification of diseases from leaf images of potato, cotton, bean, and banana. In the experimental study, the classification performances of all these standard and pretrained CNN models are presented comparatively. Experimental results have shown that the performance of CNN models is significantly improved by transfer learning, even in a small number of epochs.

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“…Deneysel çalışmalarda %99,30 doğruluk oranında hastalık tespiti yapılmıştır. Mevcut çalışmalardan %1,44 daha yüksek doğruluk oranına sağlanmıştır (Aslan 2021). Bir diğer çalışmada (Doğan,2019) bitkilerde görülen hastalıkların derin öğrenme yöntemleriyle tespiti ve sınıflandırılmasını yapmıştır.…”

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Salata-Marulda Derin Öğrenme Yöntemleri Kullanılarak Robotik Hasat Kriterlerinin Tespiti

KAHYA,

ÖZDÜVEN,

ASLAN

2023

Türk Tarım Ve Doğa Bilimleri Dergisi

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Bu çalışmada, salata marul yetiştiriciliğinde derin öğrenme metodlarından YOLOv5n, YOLOv5s ve Yolov5m kullanılarak hasat zamanı tespiti belirlenmeye çalışılmıştır. Herbir metot için 640x640 çözünürlük üzerinden eğitim yapılmıştır. Bu eğitim metodlarından hangi metodun ve hangi çözünürlüğün tam sonuç vereceği incelenmiştir. Oluşturan üç modelin tüm metrik değerleri incelenmiştir. En başarılı model YOLOv5n algoritmasıyla, 640x640 boyutundaki görselleri 10 batch size olarak 150 epoch ile eğitilmiş “Model 1” model olduğu görülmüştür. Model değerleri sonuçları “metrics/precision”, “metrics/recall”, “metrics/mAP_0.5” ve “metrics/mAP_0.5:0.95” olarak incelenmiştir. Bunlar, bir modelin tespit başarısını ölçen anahtar metriklerdir ve ilgili modelin doğrulama veri kümesinde gösterdiği performansı belirtmektedir. “Model 1” modelinin metrik verileri, diğer modellerle kıyaslandığında daha yüksek olduğu tespit edilmiştir. Ölçülen değer Model 1: Size: 640x640, Batch: 10, Epoch: 150, Algorithm: YOLOv5n’dir. Buradan “Model 1” in robotik marul hasadında, marulun hasat kriterin bulunması için kullanılacak en iyi tespit modeli olduğu anlaşılmıştır.

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Derin Öğrenme ile Bitki Hastalıklarının Tespiti

Cited by 5 publications

References 27 publications

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

Comparison of Standard and Pretrained CNN Models for Potato, Cotton, Bean and Banana Disease Detection

Salata-Marulda Derin Öğrenme Yöntemleri Kullanılarak Robotik Hasat Kriterlerinin Tespiti

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