Kiwifruit Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

Liu, Bin; Ding, Zefeng; Zhang, Yun; He, Dongjian; He, Jinrong

doi:10.1109/compsac48688.2020.00-82

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…Table 3 and Fig. 8 offer relative outputs of the RDODL-APDC technique on the Apple dataset [25][26][27]. The obtained outputs show that the SVM and BP…”

Section: Discussionmentioning

confidence: 99%

Red Deer Optimization with Deep Learning Enabled Agricultural Plant Disease Detection and Classification Model

2023

IJIES

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Plant diseases were responsible for crop losses which directly affect global and national food production methods, ensuing in economic losses. Effectual plant disease analysis includes early-season plant disease detection, detection of several diseases from various crops and various simultaneous diseases, evaluation of the severity of diseases, an estimate of the suitable volume of pesticides for execution, and valuable stages for taking to manage diseases for limiting their spread. Plant diseases and pests were significant features defining crop and plant quality. Plant diseases and pest recognition are applied for digital image processing. Recently, deep learning (DL) is developing enhancement in the field of digital image processing, far higher than standard approaches. This article introduces a red deer optimization with deep learning enabled agricultural plant disease detection and classification (RDODL-APDC) technique. The presented RDODL-APDC technique exploits the DL technique for recognizing and categorizing plant diseases. Initially, the RDODL-APDC technique segments the plant leaf regions using the NestNet background removal process. In addition, multi-level thresholding segmentation segments the infected portions of the images of the plant leaf. For feature extraction, the RDO with the MobileNet-v3 model is exploited. Finally, the extreme gradient boosting (XGBoost) and neural network (NN) models are used for the detection process with particle swarm optimization (PSO) as a parameter tuning technique. For exhibiting the betterment of the RDODL-APDC technique, an extensive range of simulations are executed. The experimental values described the improvement of the RDODL-APDC technique over other current algorithms.

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“…Table 3 and Fig. 8 offer relative outputs of the RDODL-APDC technique on the Apple dataset [25][26][27]. The obtained outputs show that the SVM and BP…”

Section: Discussionmentioning

confidence: 99%

Red Deer Optimization with Deep Learning Enabled Agricultural Plant Disease Detection and Classification Model

2023

IJIES

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“…These advancements have enabled the processing and analysis of large data volumes at unprecedented speeds, facilitating the development of more complex and sophisticated neural network architectures. Recently, numerous scholars have initiated explorations into the application of deep learning technology in the field of flower recognition [10]. This shift towards deep learning approaches has unlocked new possibilities for more accurate and efficient identification of flower species.…”

Section: Deep Learning-based Flower Recognition Methodsmentioning

confidence: 99%

Research on a Flower Recognition Method Based on Masked Autoencoders

Li,

Lv,

Ding

et al. 2024

Horticulturae

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Accurate and efficient flower identification holds significant importance not only for the general public—who may use this information for educational, recreational, or conservation purposes—but also for professionals in fields such as botany, agriculture, and environmental science, where precise flower recognition can assist in biodiversity assessments, crop management, and ecological monitoring. In this study, we propose a novel flower recognition method utilizing a masked autoencoder, which leverages the power of self-supervised learning to enhance the model’s feature extraction capabilities, resulting in improved classification performance with an accuracy of 99.6% on the Oxford 102 Flowers dataset. Consequently, we have developed a large-scale masked autoencoder pre-training model specifically tailored for flower identification. This approach allows the model to learn robust and discriminative features from a vast amount of unlabeled flower images, thereby enhancing its generalization ability for flower classification tasks. Our method has been applied successfully to flower target detection, achieving a Mean Average Precision (mAP) of 71.3%. This result underscores the versatility and effectiveness of our approach across various flower-related tasks, including both detection and recognition. Simultaneously, we have developed a straightforward, user-friendly flower recognition and classification software application, which offers convenient and reliable references for flower education, teaching, dataset annotation, and other uses.

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“…VGGNet 2019 (13) Sugarcane Images Size: 2100 Accuracy: 95.4% Deep CNN 2020 (14) Kiwifruit Leaf Images Size: 11322 Accuracy: 98.54% CNN-Inception V3 Model 2020 (15) Plum fruit Images Size:87,848 Accuracy: 92% CNN Model Grad-CAM 2021 (16) Coffee Leaf Images Size: 1560 Accuracy: 98% AlexNet Model 2023 (17) Pomegranate Leaf Images Size: 1245 Accuracy: 97.60% Fruit/Crop disease forecasting (Data-Driven Approach) LSTM 2019 (18) Cotton: Weather parameters AUC: 0.97…”

Section: Contributionmentioning

confidence: 99%

i-PomDiagnoser: A Real-Time Pomegranate Disease Management System

Nirgude,

Rathi

2024

IJST

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Objectives: Designing and developing i-PomDiagnoser: a real-time pomegranate disease management system for disease detection, classification, prediction, recommending preventive measures, and analyzing abrupt climatic changes and their impact on pomegranates. Methods: A data collection framework has been designed and developed using an agriculture drone, sensors, camera, and other equipment to collect real field pomegranate images and micro-level parameters. Comprehensive Exploratory Data Analysis (EDA) and Feature Selection (FS) processes were carried out to improve the accuracy of disease classification and forecasting models. ML-based Binary, Multimodel, and Multilabel classifiers were implemented for disease classification. The models were trained on 11 years of historical data and tested on 5 months of actual field data. A hybrid pomegranate disease forecasting model has been developed for accurately forecasting micro-level parameters for the next 45 days to predict diseases. Findings: Micro-level (weather, soil, water) parameters specific to the agro-climatic zone were collected. The five most prominent distinct diseases are considered for experimentation namely Bacterial Blight (Telya), Anthracnose, Fruit spot, Fusarium Wilt, and Fruit borer. The proposed Improved Ensemble Multilabel Classifier (i-Ensemble-MLC) with a modified voting scheme has achieved a high classification accuracy of 95.82%, addressing model overfitting and data imbalance. Moreover, the hybrid pomegranate disease forecasting model, combining LSTM and i-Ensemble-MLC, demonstrated better performance with minimal error rates (MSE: 0.003, RMSE: 0.071, MAE: 0.048, R2: 0.7) compared to the existing model1 (MSE:0.037, MAE:0.028). Novelty: The novelty lies in the creation of the all-in-one model, i-PomDiagnoser. This innovative system helps the farmers to correctly detect and predict the most prominent diseases of pomegranate. Keywords: Pomegranate, Agriculture, Disease Forecasting, Machine Learning, Deep Learning

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Kiwifruit Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

Cited by 14 publications

References 15 publications

Red Deer Optimization with Deep Learning Enabled Agricultural Plant Disease Detection and Classification Model

Red Deer Optimization with Deep Learning Enabled Agricultural Plant Disease Detection and Classification Model

Research on a Flower Recognition Method Based on Masked Autoencoders

i-PomDiagnoser: A Real-Time Pomegranate Disease Management System

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