A Review: Custard Apple Leaf Parameter Analysis and Leaf Disease Detection using Digital Image Processing

Gargade, Appasaheb; Khandekar, Shridhar

doi:10.1109/iccmc.2019.8819867

Cited by 18 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, eating stale or spoiled fruits can trigger outbreaks of foodborne diseases, potentially leading to serious public health issues [4]. Among numerous fruits, the sugar apple (Annona squamosa) [5], which belongs to the genus Annona of the family Annonaceae, is often cultivated in tropical and subtropical regions and is also known as the custard apple and sweetsop. Due to its sweet and distinctive taste, it has gained favor among a vast number of consumers [6].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

Xie,

Ke,

Chen

et al. 2024

Agriculture

View full text Add to dashboard Cite

The sugar apple (Annona squamosa) is valued for its taste, nutritional richness, and versatility, making it suitable for fresh consumption and medicinal use with significant commercial potential. Widely found in the tropical Americas and Asia’s tropical or subtropical regions, it faces challenges in post-harvest ripeness assessment, predominantly reliant on manual inspection, leading to inefficiency and high labor costs. This paper explores the application of computer vision techniques in detecting ripeness levels of harvested sugar apples and proposes an improved deep learning model (ECD-DeepLabv3+) specifically designed for ripeness detection tasks. Firstly, the proposed model adopts a lightweight backbone (MobileNetV2), reducing complexity while maintaining performance through MobileNetV2′s unique design. Secondly, it incorporates the efficient channel attention (ECA) module to enhance focus on the input image and capture crucial feature information. Additionally, a Dense ASPP module is introduced, which enhances the model’s perceptual ability and expands the receptive field by stacking feature maps processed with different dilation rates. Lastly, the proposed model emphasizes the spatial information of sugar apples at different ripeness levels by the coordinate attention (CA) module. Model performance is validated using a self-made dataset of harvested optical images categorized into three ripeness levels. The proposed model (ECD-DeepLabv3+) achieves values of 89.95% for MIoU, 94.58% for MPA, 96.60% for PA, and 94.61% for MF1, respectively. Compared to the original DeepLabv3+, it greatly reduces the number of model parameters (Params) and floating-point operations (Flops) by 89.20% and 69.09%, respectively. Moreover, the proposed method could be directly applied to optical images obtained from the surface of the sugar apple, which provides a potential solution for the detection of post-harvest fruit ripeness.

show abstract

Section: Introductionmentioning

confidence: 99%

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

Xie,

Ke,

Chen

et al. 2024

Agriculture

View full text Add to dashboard Cite

show abstract

“…ML and DL comparison is done for effective detection techniques with few drawbacks and limitations (12,13) . PD2SEN, an automatic computer-assisted leaf disease diagnosis system, is identified and the system estimates the plant life based on the severity and infection and CA Leaf parameter analysis was done using digital image processing and the same was implemented in the real-time crop results with an accuracy level of 86% in terms of detection rate (14) . The Virus Particle Swarm algorithm is used for failure detection in CNN and the same is achieved in leaf detection with the random walk method with a failure rate of 14%.…”

Section: Introductionmentioning

confidence: 99%

Early Detection and Classification of Apple Leaf Diseases by utilizing IFPA Genetic Algorithm with MC-SVM, SVI and Deep Learning Methods

Jose¹,

Santhi²

2022

IJST

View full text Add to dashboard Cite

Objectives:To propose a new model for early detection and classification of apple leaf diseases by means of genetic algorithms and advanced deep learning methods in order to minimize plant degradation and maximize detection accuracy. Methods: A feature selection and extraction task is carried out by using an Improvised Flower Pollination Technique (IFPA) based genetic algorithm. Deep learning techniques such as Multi Class Support Vector Machine (MC-SVM) and Spectral Vegetation Indices (SVI) are used to classify and detect disease. The Region Growing Algorithm (RGA) and the Stochastic Gradient Descent Method (SGDM) are used to detect leaf colour segments and identify the disease at an early stage based on shape, texture, and colour features. IFPA-GA with MC-SVM and SVI extracts the features from the apple leaf dataset (collected from the Apple Experiment Station of Northwest A&F University, China) and then selects the appropriate function to perform classification and detection of disease at early occurrence and to improve the accuracy level. To demonstrate the efficiency of the proposed algorithm, MATLAB is used for implementation. The performance results are evaluated and compared to the existing models such as Faster R-CNN, R-SSD, and INAR-SSD. Findings: Early disease detection and classification of leaf diseases are achieved with 94.09% accuracy level, 93.07% Speed, 94.01% sensitivity, 93.38% specificity, 95. 01% precision, 93.17% recall, 190 True Positive, 110 True Negative, and 92.07% F-Score to detect the disease and classify it in an optimized manner, which is high compared to the existing versions. Novelty: According to the findings of the comprehensive study, the proposed detection and classification method IFPA-GA with SVM-SVI outperforms Faster R-CNN, R-SSD, and INAR-SSD in terms of accuracy and speed of apple leaf disease detection at an early occurrence by classifying it in a robust manner.

show abstract

“…Today, with the development of technology, it is seen that auxiliary tools have been created for farmers. Recently, pre-network-based methods and image processing algorithms are frequently used in the classification and automatic detection of leaf diseases of various plants (Gargade & Khandekar, 2019;Gobalakrishnan et al, 2020;Shrivastava, 2021;Tahir et al, 2021). When the studies are looked back, it is seen that mostly classical machine learning algorithm methods are used (Annabel et al, 2019;Gobalakrishnan et al, 2020;Shrivastava, 2021).…”

Section: Introductionmentioning

confidence: 99%

Öneri̇len Konvolüsyon Si̇ni̇r Aği Yaklaşimi Kullanarak Elma Yapraği Hastaliklarinin Siniflandirilmasi

ÇETİNER

2021

Mühendislik Bilimleri Ve Tasarım Dergisi

View full text Add to dashboard Cite

It is difficult to constantly control apple trees in farmland. In case of a disease on tree leaves, the risk of disease transmission to other leaves is high. It is necessary to prevent further deterioration of the plant by performing automatic detection of the disease in the early period. If the disease detection is delayed, the planned production cannot be realized. It is too late if diseases are detected by a farmer or agronomist. In addition, as the agricultural lands grow, the number of experts needed increases accordingly. For these reasons, leaf images of apple trees are grouped into 4 different classes: apple peel, leaf rust, healthy apple and multiple disease states. In the proposed method, noise removal in the images, detection of the relevant area and histogram equalization on the YUV color space are performed. Due to the unbalanced class distribution in the data set used, data augmentation was applied for the minority classes with the SMOTE method. Afterwards, features are extracted using pre-trained network models DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, ResNet50V2. Extracted features were classified with a CNN-based method developed with an accuracy of 99%.

show abstract

A Review: Custard Apple Leaf Parameter Analysis and Leaf Disease Detection using Digital Image Processing

Cited by 18 publications

References 12 publications

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

Early Detection and Classification of Apple Leaf Diseases by utilizing IFPA Genetic Algorithm with MC-SVM, SVI and Deep Learning Methods

Öneri̇len Konvolüsyon Si̇ni̇r Aği Yaklaşimi Kullanarak Elma Yapraği Hastaliklarinin Siniflandirilmasi

Contact Info

Product

Resources

About