A Survey on Training Issues in Chili Leaf Diseases Identification Using Deep Learning Techniques

Kanaparthi, Kantha Raju; Ilango, S. Sudhakar

doi:10.1016/j.procs.2023.01.188

Cited by 13 publications

(6 citation statements)

References 11 publications

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“…Meanwhile, the DenseNet201 model achieved 92% accuracy with a success rate of 87%, accurately predicting 13 out of 15 images. Drawing from the previous research [18]- [20], this study shares similarities in terms of using relatively small datasets. However, the CNN model proposed in this study differs from the CNN models used in the mentioned studies.…”

Section: Literature Reviewmentioning

confidence: 99%

Chili Leaf Health Classification using Xception Pretrained Model

Wulandari,

Munggaran,

Setiawan

et al. 2024

SISTEMASI

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As one of the high-demand horticultural crops, chili peppers have a significant impact on the economy of Indonesia. However, despite the growing demand and interest in chili peppers, their production often faces disruptions due to crop failures. One of the leading causes of such failures is pests and diseases. Among all parts of the chili plant, chili leaves are the most susceptible to damage. Distinguishing between healthy and unhealthy chili leaves can serve as an early detection step for chili diseases and preventive measures to contain their spread. Convolutional Neural Network (CNN) are effective algorithms for image classification. The development of CNN has led to the use of models previously trained on large datasets to accurately classify relatively small datasets. One such pretrained model known for its exceptional classification capabilities is Xception. By utilizing the pretrained Xception model trained on the ImageNet dataset for the classification of healthy and unhealthy chili leaf images, our model achieved an accuracy of 91% on a dataset containing 2136 images. Furthermore, the model achieved a 100% success rate by correctly predicting all 10 out of 10 given images.

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Section: Literature Reviewmentioning

confidence: 99%

Chili Leaf Health Classification using Xception Pretrained Model

Wulandari,

Munggaran,

Setiawan

et al. 2024

SISTEMASI

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“…In this paper ( Kanaparthi and Ilango, 2023 ), DL methods investigated the training issues on the Chilli leaf diseases dataset. This research uses 160 images from the public domain repository on Kaggle to assess the efficacy of the Squeeze-Net training architecture in identifying Geminivirus and Mosaic-infected Chilli leaves.…”

Section: Ai-based Automated Vegetables Disease Detection Classificationmentioning

confidence: 99%

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Jafar,

Bibi,

Naqvi

et al. 2024

Front. Plant Sci.

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Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

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“…Squeeze-Net training architecture is proposed by Kantha Raju Kanaparthi et al [ 30 ] to train chilli leaves for identifying Gemini and Mosaic viruses. Considering several training features like CNN optimizers stochastic gradient descent with momentum (SGDM), Adaptive Moment (ADAM), and Root Mean Squared Propagation (RMSPROP), the resulting training accuracy can range from 50 % to 100 %.…”

Section: Related Workmentioning

confidence: 99%

A novel hierarchical framework for plant leaf disease detection using residual vision transformer

Vallabhajosyula,

Sistla,

Kolli

2024

Heliyon

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A Survey on Training Issues in Chili Leaf Diseases Identification Using Deep Learning Techniques

Cited by 13 publications

References 11 publications

Chili Leaf Health Classification using Xception Pretrained Model

Chili Leaf Health Classification using Xception Pretrained Model

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

A novel hierarchical framework for plant leaf disease detection using residual vision transformer

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