Identifying tomato leaf diseases under real field conditions using convolutional neural networks and a chatbot

Cheng, Hsueh-Hung; Dai, Yu-Lun; Lin, Yuanhua; Hsu, Hao-Chun; Lin, Chu-Ping; Huang, Jin-Hsing; Chen, Shih‐Fang; Kuo, Yan-Fu

doi:10.1016/j.compag.2022.107365

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…Therefore, the mobile DBOQS is applicable to a variety of real-world issues. Temniranrat et al [ 73 ] were able to detect five common rice diseases using a CNN with YOLOv3 architecture hosted on a cloud service and LINE, an instant messaging application that is maintained by its development team [ 74 ]. A DBOQS was placed on an instant messaging application (LINE) to access CNNs hosted on a cloud service; therefore, the system offered automatic responses and was always accessible.…”

Section: Related Workmentioning

confidence: 99%

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

et al. 2022

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A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17–43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after thae trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.

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Section: Related Workmentioning

confidence: 99%

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

et al. 2022

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“…Dataset Type of Dataset [7,19,21,22,27,31, PlantVillage Public [101,102] AIChallenger Public [17,18] Own dataset Public [103] Tomato Leaf Disease Detection Public [104] Dataset of Tomato Leaves Public [105] PlantVillage + Plant Disease Severity Public [106] PlantVillage + New Plant Diseases Public [107] PlantVillage + AIChallenger Public [33] PlantVillage + AIChallenger + PlantDoc Public [32,108] PlantVillage + PlantDoc Public [109][110][111][112][113][114][115][116][117] PlantVillage + own dataset Public + Private [8,23,24,[118][119][120][121][122][123][124][125][126][127] Own dataset Private…”

Section: Citationsmentioning

confidence: 99%

“…The impact of CNNs' ability to accurately classify diseases in tomato plants will be profound in agriculture, as it will facilitate rapid identification and treatment of diseased plants by farmers, thereby minimizing crop losses and maximizing yields. The utilization of CNNs in the classification of tomato diseases represents a significant development in the field, with substantial potential to enhance crop management practices and ensure food security [119]. In summary, the use of CNNs in the classification of tomato diseases holds immense promise and is poised to revolutionize crop management techniques in the agriculture industry.…”

Section: Trends 821 Deep Learningmentioning

confidence: 99%

Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato (Solanum lycopersicum) Disease Management for Global Food Security: A Comprehensive Review

Sundararaman,

Jagdev,

Khatri

2023

Sustainability

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The growing global population and accompanying increase in food demand has put pressure on agriculture to produce higher yields in the face of numerous challenges, including plant diseases. Tomato is a widely cultivated and essential food crop that is particularly susceptible to disease, resulting in significant economic losses and hindrances to food security. Recently, Artificial Intelligence (AI) has emerged as a promising tool for detecting and classifying tomato leaf diseases with exceptional accuracy and efficiency, empowering farmers to take proactive measures to prevent crop damage and production loss. AI algorithms are capable of processing vast amounts of data objectively and without human bias, making them a potent tool for detecting even subtle variations in plant diseases that traditional techniques might miss. This paper provides a comprehensive overview of the most recent advancements in tomato leaf disease classification using Machine Learning (ML) and Deep Learning (DL) techniques, with an emphasis on how these approaches can enhance the accuracy and effectiveness of disease classification. Several ML and DL models, including convolutional neural networks (CNN), are evaluated for tomato leaf disease classification. This review paper highlights the various features and techniques used in data acquisition as well as evaluation metrics employed to assess the performance of these models. Moreover, this paper emphasizes how AI techniques can address the limitations of traditional techniques in tomato leaf disease classification, leading to improved crop yields and more efficient management techniques, ultimately contributing to global food security. This review paper concludes by outlining the limitations of recent research and proposing new research directions in the field of AI-assisted tomato leaf disease classification. These insights will be of significant value to researchers and professionals interested in utilizing ML and DL techniques for tomato leaf disease classification and ultimately contribute to sustainable food production (SDG-3).

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“…Similarly, in a study byKhan et al (2020), a deep learning-based approach was used for the detection of tomato leaf diseases using images. The study used a pretrained CNN model and achieved an accuracy of over 98% in detecting diseases such as early blight and late blight.3.…”

mentioning

confidence: 99%

Leaf Blight Detection Using Deep Learning

Khan,

Murshad,

Momin

et al. 2023

IJEAST

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Plant leaf blight is a common disease that affects many crops, causing significant economic losses in agriculture. Early detection of blight is essential for farmers to prevent the spread of the disease, control its severity, and improve crop yields. Manual detection of blight is time-consuming and often unreliable, making it necessary to use advanced technologies for accurate and efficient detection. Plant leaf blight detection systems can help farmers monitor their crops in real-time and identify blight at an early stage. This enables farmers to take appropriate measures such as crop rotation, timely application of fungicides, and improving plant nutrition to prevent the spread of the disease. By using plant leaf blight detection systems, farmers can reduce the economic losses caused by the disease and ensure the availability of healthy crops for consumption. This project presents a smart system for leaf blight detection that uses deep learning and convolutional neural networks (CNN) and is implemented on an Android platform. The system comprises an image acquisition module, a pre-processing module, and a leaf blight classification module. The image acquisition module captures images of leaves from a mobile camera, while the pre-processing module enhances the quality of images by removing noise and enhancing contrast. The leaf blight classification module uses a pre-trained CNN model to accurately classify images into healthy or diseased categories and provide the Confidence level of the Blight (disease) present.

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Identifying tomato leaf diseases under real field conditions using convolutional neural networks and a chatbot

Cited by 11 publications

References 26 publications

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato (Solanum lycopersicum) Disease Management for Global Food Security: A Comprehensive Review

Leaf Blight Detection Using Deep Learning

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