Elma Bitkisindeki Hastalıkların Yapay Zekâ Yöntemleri ile Tespiti ve Yapay Zekâ Yöntemlerinin Performanslarının Karşılaştırılması

Aksoy, Bekir; Halis, Helin Diyar; Salman, Osamah Khaled Musleh

doi:10.47933/ijeir.772514

Cited by 8 publications

(5 citation statements)

References 55 publications

(51 reference statements)

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“…The study obtained 70% and 85% accuracy rates, respectively. Kang and Chen (2020) showed 85% accuracy with LedNet architecture (Aksoy et al , 2020) 98% with ResNet-34 architecture (Gai et al , 2021), it is seen that they have achieved 70% accuracy with the YOLOv4 architecture and 85% accuracy with the YOLOv4-dense architecture. The YOLOv5 architecture, which is more up-to-date, faster and more accurate than other architectures, was preferred in the study.…”

Section: Discussionmentioning

confidence: 93%

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Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Akdoğan,

Özer,

Oğuz

2024

RIA

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Purpose Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV). Design/methodology/approach Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV. Findings In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%. Originality/value An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

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Section: Discussionmentioning

confidence: 93%

“…The highest accuracy in AI models was obtained with the Vgg16 model. Aksoy et al (2020) received F1 score values in the range of 97%–98% using AlexNet, DenseNet-121, ResNet-34, VGG16-BN and SqueezeNet1_0 architectures for the detection of diseases in apple fruit. They stated that the ResNet-34 model is more successful than other models.…”

Section: Discussionmentioning

confidence: 99%

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Akdoğan,

Özer,

Oğuz

2024

RIA

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“…Artificial intelligence applications in agriculture have become common with the technology's advancement (Aksoy et all., 2020). In recent years, developments in automation and smart technologies have made production in the aquaculture sector more controlled and efficient.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning technique for disease outbreak prediction in rainbow trout ( Oncorhynchus mykiss ) farms

Yılmaz

Çakır

Oral

et al. 2022

Aquaculture Research

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Aquaculture is the fastest growing food production industry in the world due to the increasing demand for pan sized fish (Pridgeon & Klesius, 2012). Rainbow trout production in Turkey's inland waters reached the highest production amount in the last decade in 2021 with 134,174 (Turkish Statistical Institute-TUIK, 2022) tons. Furthermore, Turkey is in the leading position in Europe for rainbow trout production (Lasner et al., 2017), 17.2% of which was realized in Muğla province.In intensive fish farming, providing optimum living conditions for the fish and preserving the health of them are the two most important factors to obtain high quality and high quantity products. Diseases can cause billions of dollars loss in fish production as in other husbandry operations (

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“…In this approach, feature extraction from apple and banana images is performed with pretrained VGG-16 and AlexNet models, and then classification is performed with the Support Vector Machine (SVM). Aksoy et al [8] performed disease detection with AlexNet, DenseNet-121, Resnet-34, Squeezenet, and VGG-16 CNN models using apple leaf images and compared the performances of these models. Hassan et al [9]performed disease detection with Inception-v3, InceptionResNet-v2, MobileNet-v2, and EfficientNet-b0 CNN models using leaf images of 14 different plants.…”

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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Elma Bitkisindeki Hastalıkların Yapay Zekâ Yöntemleri ile Tespiti ve Yapay Zekâ Yöntemlerinin Performanslarının Karşılaştırılması

Cited by 8 publications

References 55 publications

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Using machine learning technique for disease outbreak prediction in rainbow trout ( Oncorhynchus mykiss ) farms

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

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