A Study on Various Techniques for Plant Leaf Disease Detection Using Leaf Image

Raina, Sakshi; Gupta, Abhishek

doi:10.1109/icais50930.2021.9396023

Cited by 19 publications

(1 citation statement)

References 6 publications

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“…Currently, leaf disease detection in plants, including CAU, relies on visual inspection by human experts such as agricultural professionals and botanists [6]. However, this subjective approach has limitations, leading to inconsistencies in disease identification due to varied interpretations of visual symptoms [7]. Additionally, it is time-consuming and may not effectively identify early-stage or subtle diseases, resulting in increased crop losses and reduced medicinal properties for CAU [8].…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Buakum,

Kosacka-Olejnik,

Pitakaso

et al. 2024

AgriEngineering

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Leaf abnormalities pose a significant threat to agricultural productivity, particularly in medicinal plants such as Centella asiatica (Linn.) Urban (CAU), where they can severely impact both the yield and the quality of leaf-derived substances. In this study, we focus on the early detection of such leaf diseases in CAU, a critical intervention for minimizing crop damage and ensuring plant health. We propose a novel parallel-Variable Neighborhood Strategy Adaptive Search (parallel-VaNSAS) ensemble deep learning method specifically designed for this purpose. Our approach is distinguished by a two-stage ensemble model, which combines the strengths of advanced image segmentation and Convolutional Neural Networks (CNNs) to detect leaf diseases with high accuracy and efficiency. In the first stage, we employ U-net, Mask-R-CNN, and DeepNetV3++ for the precise image segmentation of leaf abnormalities. This step is crucial for accurately identifying diseased regions, thereby facilitating a focused and effective analysis in the subsequent stage. The second stage utilizes ShuffleNetV2, SqueezeNetV2, and MobileNetV3, which are robust CNN architectures, to classify the segmented images into different categories of leaf diseases. This two-stage methodology significantly improves the quality of disease detection over traditional methods. By employing a combination of ensemble segmentation and diverse CNN models, we achieve a comprehensive and nuanced analysis of leaf diseases. Our model’s efficacy is further enhanced through the integration of four decision fusion strategies: unweighted average (UWA), differential evolution (DE), particle swarm optimization (PSO), and Variable Neighborhood Strategy Adaptive Search (VaNSAS). Through extensive evaluations of the ABL-1 and ABL-2 datasets, which include a total of 14,860 images encompassing eight types of leaf abnormalities, our model demonstrates its superiority. The ensemble segmentation method outperforms single-method approaches by 7.34%, and our heterogeneous ensemble model excels by 8.43% and 14.59% compared to the homogeneous ensemble and single models, respectively. Additionally, image augmentation contributes to a 5.37% improvement in model performance, and the VaNSAS strategy enhances solution quality significantly over other decision fusion methods. Overall, our novel parallel-VaNSAS ensemble deep learning method represents a significant advancement in the detection of leaf diseases in CAU, promising a more effective approach to maintaining crop health and productivity.

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Section: Introductionmentioning

confidence: 99%

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Buakum,

Kosacka-Olejnik,

Pitakaso

et al. 2024

AgriEngineering

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Real-Time Turmeric Leaf Identification and Classification Using Advanced Deep Learning Models: Initiative to Smart Agriculture

Chathurya,

Sachdeva,

Arora

2024

Lecture Notes in Networks and Systems

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Machine learning for leaf disease classification: data, techniques and applications

Yao,

Tran,

Sawyer

et al. 2023

Artif Intell Rev

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The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular.

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A Study on Various Techniques for Plant Leaf Disease Detection Using Leaf Image

Cited by 19 publications

References 6 publications

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Real-Time Turmeric Leaf Identification and Classification Using Advanced Deep Learning Models: Initiative to Smart Agriculture

Machine learning for leaf disease classification: data, techniques and applications

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