Deep Feature Extraction for Detection of Tomato Plant Diseases and Pests based on Leaf Images

Altuntaş, Yahya; Kocamaz, Adnan Fatih

doi:10.18466/cbayarfbe.812375

Cited by 11 publications

(9 citation statements)

References 28 publications

(33 reference statements)

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“…Singh and Misra (2017) classified five different diseases and four different plant species using SVM. Conventional machine-learning techniques are only effective when feature extraction is done properly (Altunta and Kocamaz, 2021). Furthermore, segmentation is important for feature extraction (Altunta and Kocamaz, 2021).…”

Section: Viroidmentioning

confidence: 99%

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Tomato Leaf Classification Using Computer Vision and Deep Learning: Comparing Different EfficientNets

Eroltu

2024

IJAIML

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Agriculture is an essential field that includes crop production, plant and fruit growing, and livestock. Plant disease is a significant challenge in agriculture, which can drastically impact crop production and lead to reduced productivity and potentially severe shortages. Hence, it is essential to detect plant diseases as fast as possible in order to start separating diseased ones from healthy ones. However, this process is arduous and challenging to accomplish manually. This paper shows a possible automation technique using EfficientNet CNN models. Gray-level, binarized, and color-level datasets were separately given in this study. The images in the dataset were resized. In order to increase the training data's variety, the input images underwent a horizontal flip. The data was rotated, helping the model in handling minor orientation variances. Randomized zoom was implemented to improve the model's ability to recognize leaf images from varying distances and sizes. The highest training accuracy achieved is 99.81% with the EfficientNetB5 model at 50 epochs and a batch size of 128. The EfficientNetB0 model achieves the lowest training accuracy with 97.17% accuracy at 20 epochs and a batch size of 16, however. The highest testing accuracy achieved is 97.83% by the EfficientNetB7 model with 50 epochs and a batch size of 64; on the other hand, the lowest testing accuracy is 77.84% by the EfficientNetB4 model with 20 epochs and a batch size of 32.

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Section: Viroidmentioning

confidence: 99%

“…Conventional machine-learning techniques are only effective when feature extraction is done properly (Altunta and Kocamaz, 2021). Furthermore, segmentation is important for feature extraction (Altunta and Kocamaz, 2021). There are major differences between traditional machine learning algorithms and deep learning techniques.…”

Section: Viroidmentioning

confidence: 99%

Tomato Leaf Classification Using Computer Vision and Deep Learning: Comparing Different EfficientNets

Eroltu

2024

IJAIML

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“…𝑥∈𝑐𝑘 𝑦∈𝑐𝑘 (8) Where: d is a measure of the similarity of the data with the Euclidean equation, p(x,y) is the data feature, k is the number of data clusters (number of image segmentations), and ck is the data centroid.…”

Section: Image Segmentation Using Enhanced K-means Clusteringmentioning

confidence: 99%

“…Several studies have produced the right method for recognizing or identifying tomato diseases with various techniques, one of which is using the convolutional neural network (CNN) technique with good accuracy results above 90% [7] [8]. Another technique uses a combination of random forest and k-nearest neighbor (kNN) with an accuracy of up to 96% [9].…”

Section: Introductionmentioning

confidence: 99%

KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering

Nasution

Alvin²,

Siregar³

et al. 2022

KINETIK

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Image segmentation is an important process in identifying tomato diseases. The technique that is often used in this segmentation is k-means clustering. One of the main problems in this technique is the case of local minima, where the cluster that is formed is not suitable due to the incorrect selection of the initial centroid. In image data, this case will have an impact on poor segmentation results because it can erase parts that are actually important to be lost or there is still background in the recognition process, which has an impact on decreasing accuracy results. In this research, a method for image segmentation will be proposed using the k-means clustering algorithm, which has been added with the cosine similarity method as the proposed contribution. The use of the cosine method will determine the initial centroid by calculating the level of similarity of each image feature based on color and dividing them into several categories (low, medium, and high values). Based on the results obtained, the proposed algorithm is able to segment and distinguish between leaf and background images with good results, with the kNN reaching a value of 94.90% for accuracy, 99.50% for sensitivity, and 93.75% for specificity. The results obtained using the kNN method with k-means segmentation obtained a value of 92.46% for accuracy, 96.30% for sensitivity, and 91.50% for specificity. The results obtained using the kNN method without segmentation obtained a value of 90.22% for accuracy, 93.30% for sensitivity, and 89.45% for specificity.

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“…Tam tersi olarak modelin yanlış tahmin ettiği erkek cinsiyeti için yanlış negatif (False Negative -FN), kadın cinsiyeti için yanlış pozitif (False Positive -FP) olarak belirlenmiştir. Doğruluk metriğinin hesaplanması Denklem-1 de verilmiştir [23].…”

Section: Değerlendirmeunclassified

Gender Identification from Turkish Tweets Using Pre-Trained Language Models

Hanbay

Sel

2021

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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Öz: Yazar profili oluşturma (Author Profiling) bir metnin üslup ve içeriğine bakarak yazarın çeşitli özelliklerinin ortaya çıkarılmasına yönelik bir metin kümesi analizidir. Bu özellikler yaş, cinsiyet, kişilik özellikleri ve hatta meslek gibi unsurları barındırır. Cinsiyet belirleme yazar profili oluşturma çalışmalarının alt alanlarından birisidir. Siber suçlar başta olmak üzere sahte haber yayma gibi adli olayların yanında pazarlama (reklamcılık), sosyolojik ve psikolojik olayların incelenmesinde cinsiyet belirleme oldukça önemlidir. Ayrıca İletişim partnerinin cinsiyetini belirlemek, e-posta, bloglar, forumlar gibi sosyal medya aracılığıyla gerçekleşen sahtekarlık ve suistimallerin önlenmesine yardımcı olabilmektedir. Türkçe dili için kısa gönderilerden cinsiyet tespiti yapılması, diğer dillere oranla çok fazla çalışılmayan bir alandır. Bu çalışmada Türkçe Twitter gönderilerinden cinsiyet tespiti yapılmaya çalışılmıştır. Twitter gönderileri dil kurallarına uymayan, kısaltılmış kelimeler ve anlamsız cümle yapıları da içerme ihtimallerine rağmen cinsiyet belirleme görevi için yaygın bir şekilde kullanılmaktadır. Problem bir sınıflandırma görevi olarak ele alınmıştır. Yapılan çalışmada makine öğrenmesi metotları(TF-IDF + SVM), derin öğrenme yöntemleri (LSTM, CNN) ve Türkçe için ön eğitimli dil modelleri(BERT, DistilBert, Electra) kullanılmıştır. Yapılan deneyler sonucunda en yüksek başarımı (%80.1) kelime boyutunun 128k olduğu Bert modeli sağlamıştır. Bu çalışma diğer metin sınıflandırma görevleri için de detaylı bir çalışma olma özelliği göstermektedir.

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Deep Feature Extraction for Detection of Tomato Plant Diseases and Pests based on Leaf Images

Cited by 11 publications

References 28 publications

Tomato Leaf Classification Using Computer Vision and Deep Learning: Comparing Different EfficientNets

Tomato Leaf Classification Using Computer Vision and Deep Learning: Comparing Different EfficientNets

KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering

Gender Identification from Turkish Tweets Using Pre-Trained Language Models

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