Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Aminuddin, Nuramin Fitri; Joret, Ariffuddin; Zulkifli, Shamsul Aizam; Kadir, Herdawatie Abdul; Morsin, Marlia; Tukiran, Zarina

doi:10.30880/emait.2022.03.02.002

Cited by 3 publications

(2 citation statements)

References 68 publications

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“…The analysis encompassed testing 2500 samples. The review presented in Aminuddin et al [42] explored the application of SVM and Random Forest (RF) as classifiers for discerning five distinct types of chili disease symptoms. These symptoms included spots, mottled mosaics, wrinkles, yellowed chili leaves, and folded veins.…”

Section: -1-related Workmentioning

confidence: 99%

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Moya,

Quito,

Pilco

et al. 2024

Emerg Sci J

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In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model’s efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model’s potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PDF

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Section: -1-related Workmentioning

confidence: 99%

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Moya,

Quito,

Pilco

et al. 2024

Emerg Sci J

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“…Saad et al (2020) applied a deep learning approach to detect chili and its flower in plant images, achieving high accuracy in classification and detection. Aminuddin et al (2022) provided a comprehensive review of computational approaches for automated disease identification in chili leaf images, emphasizing their potential. In Suwarningsih et al's (2022) study, a dataset comprising images of chili leaves with 12 distinct classes of variety showed classification accuracy ranging from 70.18% to 78.37%.…”

Section: Introductionmentioning

confidence: 99%

Emphasizing Data Quality for the Identification of Chili Varieties in the Context of Smart Agriculture

Suwarningsih,

Kirana,

H Khotimah

et al. 2024

IJIKM

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Aim/Purpose: This research aims to evaluate models from meta-learning techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic Meta-Learning (MAML), and Reptile meta-learning, to obtain high-quality metadata. The goal is to utilize this metadata to increase accuracy and efficiency in identifying chili varieties in smart agriculture. Background: The identification of chili varieties in smart agriculture is a complex process that requires a multi-faceted approach. One challenge in chili variety identification is the lack of a large and diverse dataset. This can be addressed using meta-learning techniques, which allow the model to leverage knowledge learned from other related tasks or artificially expand the dataset by applying transformations to existing data. Another challenge is the variation in growing conditions, which can affect the appearance of chili varieties. Meta-learning techniques can help address this challenge by allowing the model to adapt to variations in growing conditions with task-specific embeddings and optimizations. With the help of meta-learning techniques, such as data augmentation, data characterization, selection of datasets, and performance estimation, quality metadata for accurate identification of chili varieties can be achieved even in the presence of limited data and variations in growing conditions. Furthermore, the use of meta-learning techniques in chili variety identification can also assist in addressing challenges related to the computational complexity of the task. Methodology: The research approach employed is quantitative, specifically comparing three models from meta-learning techniques to determine which model is most suitable for our dataset. Data was collected from the variety assembly garden in the form of images of chili leaves using a mobile device. The research successfully gathered 1,974 images of chili leaves, with 697 images of large red chilies, 649 images of curly red chilies, and 628 images of cayenne peppers. These chili leaf images were then processed using augmentation techniques. The results of image data augmentation were categorized based on leaf characteristics (such as oval, lancet, elliptical, serrated leaf edges, and flat leaf edges). Subsequently, training and validation utilized three models from meta-learning techniques. The final stage involved model evaluation using 2-way and 3-way classification, as well as 5-shot and 10-shot learning scenarios to select the dataset with the best performance. Contribution: Improving classification accuracy, with a focus on ensuring high-quality data, allows for more precise identification and classification of chili varieties. Enhancing model training through an emphasis on data quality ensures that the models receive reliable and representative input, leading to improved generalization and performance in identifying chili varieties. Findings: With small collections of datasets, the authors have used data augmentation and meta-learning techniques to overcome the challenges of limited data and variations in growing conditions. Recommendations for Practitioners: By leveraging the knowledge and adaptability gained from meta-learning, accurate identification of chili varieties can be achieved even with limited data and variations in growing conditions. The use of meta-learning techniques in chili variety identification can greatly improve the accuracy and reliability of the identification process. Recommendation for Researchers: Using meta-learning techniques, such as transfer learning and parameter optimization, researchers can overcome challenges related to limited data and variations in growing conditions in chili variety identification. Impact on Society: The findings from this research can help identify superior chili seeds, thereby motivating farmers to cultivate high-quality chilies and achieve bountiful harvests. Future Research: We intend to verify our approach on a more extensive array of datasets and explore the implementation of more resilient regularization techniques, going beyond image augmentation, within the meta-learning techniques. Furthermore, our goal is to expand our research to encompass the automatic learning of parameters during training and tackle issues associated with noisy labels. Building on the insights gained from our observed outcomes, a future objective is to enhance the refinement of model-agnostic meta-learning techniques that can effectively adapt to intricate task distributions with substantial domain gaps between tasks. To realize this aim, our proposal involves devising model-agnostic meta-learning techniques specifically designed for multi-modal scenarios.

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Turmeric Farm Monitoring and Automation Using Deep Learning and Fuzzy Logic on Raspberry Pi: A Low-cost and Energy Efficient Solution

Mervin Paul Raj,

Vijayakumar

2024

2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)

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Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Cited by 3 publications

References 68 publications

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Emphasizing Data Quality for the Identification of Chili Varieties in the Context of Smart Agriculture

Turmeric Farm Monitoring and Automation Using Deep Learning and Fuzzy Logic on Raspberry Pi: A Low-cost and Energy Efficient Solution

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