Automated Image Capturing System for Deep Learning-based Tomato Plant Leaf Disease Detection and Recognition

Luna, Robert G. de; Dadios, Elmer P.; Bandala, Argel A.

doi:10.1109/tencon.2018.8650088

Cited by 132 publications

(28 citation statements)

References 13 publications

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“…In this paper, the authors reviewed many research articles and identified 129 studies eligible for systematic review using the PRISMA statement as presented in Figure S2 in the Supplementary Materials, these studies involve methodologies in image processing, machine learning, and deep learning particularly focused on the identification and classification of plant diseases. The study showed that the techniques most used in the literature, in general, are the support vector machine [22,59,61,65] (SVM), random forest [87] (RF), artificial neural network [84] (ANN) and convolutional neural network (CNN) [35,39,50].…”

Section: Discussionmentioning

confidence: 99%

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

2021

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The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.

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Section: Discussionmentioning

confidence: 99%

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

2021

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“…S. Prasad et al [38] used a KNN based model on the tomato data of 14,529 images in 10 classes and achieved an accuracy of 93%. R. G. de Luna et al [39] proposed an automated image capturing system that achieved an accuracy of 91.67% on the tomato dataset. X. Q. Guo et al [40] designed a new model named multiscale AlexNet, which achieved 92.7% accuracy on 5,766 images in 8 classes of the tomato dataset.…”

Section: ) Comparison With State-of-the-art Modelsmentioning

confidence: 99%

Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

et al. 2021

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“…These use highly accurate methods for identifying plant disease in tomato leaves. In addition, researchers have proposed many deep learning-based solutions in disease detection and classification, as discussed below in [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ].…”

Section: Related Workmentioning

confidence: 99%

“…The testing results show that the INAR-SSD model achieves a detection rate of 23.13 frames per second and detection performance of 78.80% mAP on the Apple Leaf Disease Dataset (ALDD). Furthermore, the results indicate that the innovative INAR-SSD (SSD with Inception module and Rainbow concatenation) model produces more accurate and faster results for the early identification of tomato leaf diseases than other methods [ 46 ].…”

Section: Related Workmentioning

confidence: 99%

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

Trivedi

Gautam

Anand

et al. 2021

Sensors

252

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Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

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Automated Image Capturing System for Deep Learning-based Tomato Plant Leaf Disease Detection and Recognition

Cited by 132 publications

References 13 publications

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

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