Apple Leaf Disease Detection and Analysis Using Deep Learning Technique

Kalwad, Priyadarshini D.; Kanakaraddi, Suvarna G.; Preeti, T.; Ichalakaranji, Sindhoori; Salimath, Shreyas; Nayak, Shubha R

doi:10.1007/978-981-16-0739-4_76

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…The feature vector obtained through Eqs. ( 9)- (11), is further passed to the next stage of processing in which features are further cleaned based on K-Means Clustering and Euclidean Distance. Through K-Means Clustering, two sets of features are defined as follows:…”

Section: Improved Artificial Butterfly Optimizationmentioning

confidence: 99%

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A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Rehman¹,

Khan²,

Alhaisoni³

et al. 2023

Computers, Materials &Amp; Continua

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Identifying fruit disease manually is time-consuming, expertrequired, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.

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Section: Improved Artificial Butterfly Optimizationmentioning

confidence: 99%

“…A white powdery coating on the leaves distinguishes this disease [9,10]. Cedar apple or yellow patches on the leaf undersides are surrounded by a red band, and small black spots in the center are called aecia form [11].…”

Section: Introductionmentioning

confidence: 99%

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Rehman¹,

Khan²,

Alhaisoni³

et al. 2023

Computers, Materials &Amp; Continua

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show abstract

“…Recently, deep learning-based methods have begun to be widely discussed and studied in crop disease identification [9,10]. Deep learning-based object detection algorithms do not need to extract image features by manually constructing operators, and the extracted features are more robust.…”

Section: Introductionmentioning

confidence: 99%

HSSNet: A End-to-End Network for Detecting Tiny Targets of Apple Leaf Diseases in Complex Backgrounds

Gao,

Tang,

Deng

et al. 2023

Plants

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Apple leaf diseases are one of the most important factors that reduce apple quality and yield. The object detection technology based on deep learning can detect diseases in a timely manner and help automate disease control, thereby reducing economic losses. In the natural environment, tiny apple leaf disease targets (a resolution is less than 32 × 32 pixel2) are easily overlooked. To address the problems of complex background interference, difficult detection of tiny targets and biased detection of prediction boxes that exist in standard detectors, in this paper, we constructed a tiny target dataset TTALDD-4 containing four types of diseases, which include Alternaria leaf spot, Frogeye leaf spot, Grey spot and Rust, and proposed the HSSNet detector based on the YOLOv7-tiny benchmark for professional detection of apple leaf disease tiny targets. Firstly, the H-SimAM attention mechanism is proposed to focus on the foreground lesions in the complex background of the image. Secondly, SP-BiFormer Block is proposed to enhance the ability of the model to perceive tiny targets of leaf diseases. Finally, we use the SIOU loss to improve the case of prediction box bias. The experimental results show that HSSNet achieves 85.04% mAP (mean average precision), 67.53% AR (average recall), and 83 FPS (frames per second). Compared with other standard detectors, HSSNet maintains high real-time detection speed with higher detection accuracy. This provides a reference for the automated control of apple leaf diseases.

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“…LICM (Leaf Image Classification Model) was introduced by the authors to prove that technological advancements in smart farming are necessary in order to avoid spraying pesticides on apple trees that affect human health. XGBoost and Gaussian-Blur methods were also employed, along with LICM, where Kaggle VGA datasets were used with 9876 images on 5 different types (13) . An improvised CNN-based detection system was proposed for detecting the guava leaf diseases at an early stage, and the RGBT method was deployed to mark the edges and measure the search space to compare the values and get the optimal value.…”

Section: Introductionmentioning

confidence: 99%

Real Time Classification and Detection of Apple Leaf Diseases at Early Stage by Employing Enhanced Paddy Field Pattern Search Method (EPF-PSM) with 2LC

Donu,

Santhi

2023

IJST

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Objectives: To propose a suitable machine learning approach to detect and classify the leaf diseases in apple trees at the pre-mature state to reduce plant degradation. In order to boost the accuracy level, EPF-PSM (Enhanced Paddy Field-Based Pattern Search Method) is employed along with 2LC (Two Level Classifier) technique. Methods: Preprocessing images, which include FS and FE, is done by employing EPF-PSM to detect the apple leaf disease. 2LC classifier is utilized to classify the type of disease based on the available features like gradient, pixel, edges, etc. The decision tree algorithm is employed to search for patterns in images and compare them for accurate prediction. With MC-Apriori (Multi Label Classification Apriori), the relationship between infected and non-infected leaves in search space is explored, and RGB scheme (RGBCS) is employed to discover the colour depth ratio of infected leaves. EPF-PSM extracts attributes from the ALDD-NW dataset (collected at the Apple Experiment Station of Northwest A&F University, China) and selects the suitable function to perform ALD classification, pre-mature detection, and accuracy. MATLAB software is utilized for implementation and assessment of the novel ML approach. The findings are compared with contemporary models such as the IFPA-GA with SVM-SVI, the FR-CNN, the R-SSD, and the INARSSD. Findings: The suggested model gives 98.01% accuracy rate, 97.06% detection speed in 29 seconds, 98.04% sensitivity, 95.92% specificity, 98.01% precision, 96.17% recall, 98.4% TPR, 99.03% TNR, and 96.07% F-Score, which is comparatively higher than the existing methods in terms of detection and classification of ALD. Novelty: The results shows that the suggested machine learning (ML) technique EPF-PSM with 2L-C has the ability to produce accurate detection and classification of ALD at the pre-mature stage, which helps the farmers to treat apple plants properly. The evident results outperform the prevailing methods IFPA-GA with SVM-SVI, FR-CNN, R-SSD, and INARSSD. https://www.indjst.org/

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Apple Leaf Disease Detection and Analysis Using Deep Learning Technique

Cited by 11 publications

References 8 publications

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

HSSNet: A End-to-End Network for Detecting Tiny Targets of Apple Leaf Diseases in Complex Backgrounds

Real Time Classification and Detection of Apple Leaf Diseases at Early Stage by Employing Enhanced Paddy Field Pattern Search Method (EPF-PSM) with 2LC

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