AI-Powered Image-Based Tomato Leaf Disease Detection

Annabel, L. Sherly Puspha; Muthulakshmi, V.

doi:10.1109/i-smac47947.2019.9032621

Cited by 22 publications

(5 citation statements)

References 18 publications

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“…Several studies were conducted in the literature on the application of machine learning and deep learning algorithms for the identification and classification of plant diseases. Some of these studies (e.g., Hlaing and Zaw [11] and Annabel and Muthulakshmi [14]) used the traditional approach of employing image processing techniques to segment the input images (i.e., separation of the leaf or infected area from the background) and to extract texture features that reflect the disease state of the leaf. These features form the input for non-deep traditional machine learning algorithms (e.g., SVM).…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, Tm et al [13] used the AlexNet, GoogleNet, and LeNet models for the same classification problem and achieved an accuracy range of 94-95%. Annabel and Muthulakshmi [14] used masking and threshold-based segmentation to identify and isolate infected areas of a leaf image. They extracted several features (e.g., dissimilarity, homogeneity, and contrast) and used a random forest classifier to category 3 diseases plus healthy leaves with an accuracy of 94.1%.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Tomato Diseases Using Deep Transfer Learning

2022

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Global food production is being strained by extreme weather conditions, fluctuating temperatures, and geopolitics. Tomato is a staple agricultural product with tens of millions of tons produced every year worldwide. Thus, preserving the tomato plant from diseases will go a long way in reducing economical loss and boost output. Technological innovations have great potential in facilitating disease detection and control. More specifically, artificial intelligence algorithms in the form of deep learning methods have established themselves in many real-life applications in a wide range of disciplines (e.g., medicine, agriculture, or facial recognition, etc.). In this paper, we aim at applying deep transfer learning in the classification of nine tomato diseases (i.e., bacterial spot, early blight, late blight, leaf mold, mosaic virus, septoria leaf spot, spider mites, target spot, and yellow leaf curl virus) in addition to the healthy state. The approach in this work uses leaf images as input, which is fed to convolutional neural network models. No preprocessing, feature extraction, or image processing is required. Moreover, the models are based on transfer learning of well-established deep learning networks. The performance was extensively evaluated using multiple strategies for data split and a number of metrics. In addition, the experiments were repeated 10 times to account for randomness. The ten categories were classified with mean values of 99.3% precision, 99.2% F1 score, 99.1% recall, and 99.4% accuracy. Such results show that it is highly feasible to develop smartphone-based applications that can aid plant pathologists and farmers to quickly and accurately perform disease detection and subsequent control.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Tomato Diseases Using Deep Transfer Learning

2022

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“…Sherly Pushpa et al [10] Rao et al [11] studied large-scale farming through IoT and data analytics, addressing rising power consumption with a centralized sensor control unit. Zigbee connectivity was found to be superior to Wi-Fi, highlighting the potential of centralized platforms.…”

Section: Sensor Network and Agriculturementioning

confidence: 99%

Smart Agriculture: A Comprehensive Survey on IoT-Enabled Plant Disease Detection and Agricultural Automation

T. Thilagavathi,

L. Arockiam,

I. Priya Stella Mary

2024

International Journal of Information Technology, Research and A

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This research paper is dedicated to the comprehensive review and discussion of diverse techniques employed in plant disease detection within the realm of agriculture. Emphasizing notable contributions and showcasing innovative methodologies, the research work takes a critical turn to address the myriad issues and challenges intricately woven into the integration of IoT data analytics in agriculture. The paper meticulously unravels the complexities associated with plant disease detection in the era dominated by IoT and data analytics. Serving as more than just a repository of current methodologies and technologies, this work actively illuminates the challenges that await further exploration. The insights derived from this exploration will provide a substantial foundation for emerging researchers. By shedding light on the evolving landscape of plant disease detection and the nuances of IoT integration in agriculture, this paper empowers researchers to actively contribute to the resilience and sustainability of agricultural practices in the face of ongoing challenges.

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“…With an accuracy percentage of 87.20%, MLBPNN is the most accurate algorithm. Annabel et al 10 13 to use a pretrained deep learning architecture that included transfer learning concepts, in particular AlexNet and VGG16, to extract characteristics from photos of tomatoes and categorize them as either healthy or unhealthy. In terms of accuracy in classification, AlexNet and VGG16 score 97.49% and 97.23%, respectively.…”

Section: Overview Of Related Workmentioning

confidence: 99%

Classification of Tomato Leaf Disease Using a Custom Convolutional Neural Network

Kokate,

Kumar,

G. Kulkarni

2023

Curr Agri Res Jour

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A plant's genetic potential for crop production can only be realised if the plant is healthy. Infected plants produce less than their genetic potential when they are unhealthy and exposed to infection-causing agents of any kind. A disease can have an impact on a plant's metabolism. Manual checking of plant health is not feasible for anytime. Accurately identifying the disease as soon as it first manifests on the plant is crucial for controlling it in farms. Thus, taking the proper action to stop further crop and yield damage will depend heavily on an automated method of disease identification and precise disease relegation. This paper presents a convolutional neural network (CNN) model for diagnosing tomato leaf diseases. The findings are presented with an emphasis on accuracy as well as loss. About, 14240 numbers of tomato leaf image data representing nine distinct disease classes were utilized to train the model. On average, this classification was found to be 95.53 percent accurate.

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AI-Powered Image-Based Tomato Leaf Disease Detection

Cited by 22 publications

References 18 publications

Automatic Detection of Tomato Diseases Using Deep Transfer Learning

Automatic Detection of Tomato Diseases Using Deep Transfer Learning

Smart Agriculture: A Comprehensive Survey on IoT-Enabled Plant Disease Detection and Agricultural Automation

Classification of Tomato Leaf Disease Using a Custom Convolutional Neural Network

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