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

Yao, Jianping; Tran, Son N.; Sawyer, Samantha; Garg, Saurabh

doi:10.1007/s10462-023-10610-4

Cited by 7 publications

(4 citation statements)

References 92 publications

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“…Model quality and fine-tuning strategies, as suggested by [10], ensure a robust and accurate classification process. The emphasis on results interpretation and deployment aligns with the practical insights derived from [7], which highlights the importance of continuous monitoring and integration into real-world applications. This methodology synthesis draws from a collaborative examination of various papers, identify optimal practices for effective plant disease classification.…”

Section: Review Methodology Figure 2: Block Diagram Of Proposed Systemmentioning

confidence: 77%

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Machine Learning Framework for Early Detection of Crop Disease

et al. 2024

IJFMR

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This review paper looks at recent advancements in crop disease detection through deep learning techniques. Crop diseases significantly lowers agricultural productivity, and accurate diagnosis is essential for effective disease management. In order to identify crop illnesses, the study offers a thorough examination of a number of deep learning models, such as hybrid architectures, recurrent neural networks (RNNs), and convolutional neural networks (CNNs). The prospects, challenges, and future directions of incorporating deep learning for precise and quick crop disease identification are also explained in this research. The insights provided offer hope for the development of sustainable agricultural practices through the application of cutting-edge technologies in disease diagnosis and management.

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Section: Review Methodology Figure 2: Block Diagram Of Proposed Systemmentioning

confidence: 77%

“…The paper [7] briefs about visual data, machine learning for leaf disease classification creates models that can recognize and classify plant illnesses. Tagged photos showing different plant species and the associated disease statuses are usually included in the datasets utilized in these applications.…”

Section: Diseasementioning

confidence: 99%

Machine Learning Framework for Early Detection of Crop Disease

et al. 2024

IJFMR

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“…Early detection and identification of these diseases are crucial for reducing the infection and spread among tomato plants, with initial symptoms often appearing on the leaves. Therefore, accurate disease identification becomes crucial ( Yao et al., 2023 ). Traditional disease detection methods rely on the experiential judgment of agricultural experts, which are not only inefficient but also limited in accuracy, unable to meet the needs of modern high-efficiency agriculture.…”

Section: Introductionmentioning

confidence: 99%

Tomato leaf disease detection based on attention mechanism and multi-scale feature fusion

Wang,

Zhang,

Tian

2024

Front. Plant Sci.

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When detecting tomato leaf diseases in natural environments, factors such as changes in lighting, occlusion, and the small size of leaf lesions pose challenges to detection accuracy. Therefore, this study proposes a tomato leaf disease detection method based on attention mechanisms and multi-scale feature fusion. Firstly, the Convolutional Block Attention Module (CBAM) is introduced into the backbone feature extraction network to enhance the ability to extract lesion features and suppress the effects of environmental interference. Secondly, shallow feature maps are introduced into the re-parameterized generalized feature pyramid network (RepGFPN), constructing a new multi-scale re-parameterized generalized feature fusion module (BiRepGFPN) to enhance feature fusion expression and improve the localization ability for small lesion features. Finally, the BiRepGFPN replaces the Path Aggregation Feature Pyramid Network (PAFPN) in the YOLOv6 model to achieve effective fusion of deep semantic and shallow spatial information. Experimental results indicate that, when evaluated on the publicly available PlantDoc dataset, the model’s mean average precision (mAP) showed improvements of 7.7%, 11.8%, 3.4%, 5.7%, 4.3%, and 2.6% compared to YOLOX, YOLOv5, YOLOv6, YOLOv6-s, YOLOv7, and YOLOv8, respectively. When evaluated on the tomato leaf disease dataset, the model demonstrated a precision of 92.9%, a recall rate of 95.2%, an F1 score of 94.0%, and a mean average precision (mAP) of 93.8%, showing improvements of 2.3%, 4.0%, 3.1%, and 2.7% respectively compared to the baseline model. These results indicate that the proposed detection method possesses significant detection performance and generalization capabilities.

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“…To machining algorithms can discover the relationship between soil nutrients, weather conditions and apple quality. Thus, these algorithms will give us the formation that will be helpful in improving practices [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

Ibrahim,

Khodadadi,

Khodadadi

et al. 2024

Journal of Artificial Intelligence in Engineering Practice

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This study concentrates on the evaluation of apple quality, which is a vital part of the agricultural industry. The quality of apples is examined through several factors, such as the cultivation techniques, the harvesting methods, and the post-harvest procedures. The dataset, titled "Apple Perfection," contains important characteristics such as the size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and overall quality of the apple. To make the apple quality prediction more accurate, we used different feature selection algorithms, mainly the binary Waterwheel Plant Algorithm (bWWPA), which, in fact, had the lowest average error of 0.52153, and several of the types of classification models, especially Logistic Regression, which had the highest accuracy of 0.88625. The attribute selection process found the most important attributes, which cut down the dimensionality, and hence, the model performance became better. The results of the study show that the combination of bWWPA for feature selection and logistic regression for classification can predict apple quality with high accuracy. This way of dealing with the problem gives us information that is useful for the improvement of the cultivation techniques and the post-harvest handling to the extent that we will be able to have the best quality apples. The findings of this research have a great impact on the farming industry, meaning a strong way to evaluate the quality of apples.

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

Cited by 7 publications

References 92 publications

Machine Learning Framework for Early Detection of Crop Disease

Machine Learning Framework for Early Detection of Crop Disease

Tomato leaf disease detection based on attention mechanism and multi-scale feature fusion

Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

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