MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases

Ksibi, Amel; Ayadi, Manel; Soufiene, Ben Othman; Jamjoom, Mona; Ullah, Zahid

doi:10.3390/app122010278

Cited by 37 publications

(10 citation statements)

References 31 publications

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“…This remarkable performance places it in a favorable position alongside existing research efforts. In [4] and [13], they reported an accuracy of 96%, closely followed by [12], which achieved a commendable accuracy of 95.6%. While [10] attained an accuracy of 95%, the significant lead of the proposed model with an accuracy of 98.3% shows its potential for higher precision in classification tasks.…”

Section: Comperive With Other Studiesmentioning

confidence: 93%

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Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Alsaeedi,

Al-juboori,

Al-Mahmood

et al. 2023

Sustainability

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Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results.

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Section: Comperive With Other Studiesmentioning

confidence: 93%

“…Ksibi et al [12] proposed a hybrid deep learning model for olive leaf disease detection and classification. The proposed model is composed of neural networks from the ResNet50 and Mo-bileNet models.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Alsaeedi,

Al-juboori,

Al-Mahmood

et al. 2023

Sustainability

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“…The proposed method used a transfer learning technique and achieved higher accuracy than previous studies. Also, Ksibi et al [26] proposed a hybrid deep learning model called mobile residual neural network (MobiRes-Net) for detecting and classifying olive leaf diseases. Their model combined the advantages of MobileNet and ResNet architectures and achieved high accuracy in detecting multiple types of diseases.…”

Section: Related Work 31 Techniques For Plant Diseases Classification...mentioning

confidence: 99%

Optimizing olive disease classification through transfer learning with unmanned aerial vehicle imagery

Raouhi,

Lachgar,

Hrimech

et al. 2024

IJECE

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Early detection of diseases in growing olive trees is essential for reducing costs and increasing productivity in this crucial economic activity. The quality and quantity of olive oil depend on the health of the fruit, making accurate and timely information on olive tree diseases critical to monitor growth and anticipate fruit output. The use of unmanned aerial vehicles (UAVs) and deep learning (DL) has made it possible to quickly monitor olive diseases over a large area indeed of limited sampling methods. Moreover, the limited number of research studies on olive disease detection has motivated us to enrich the literature with this work by introducing new disease classes and classification methods for this tree. In this study, we present a UAV system using convolutional neuronal network (CNN) and transfer learning (TL). We constructed an olive disease dataset of 14K images, processed and trained it with various CNN in addition to the proposed MobileNet-TL for improved classification and generalization. The simulation results confirm that this model allows for efficient diseases classification, with a precision accuracy achieving 99% in validation. In summary, TL has a positive impact on MobileNet architecture by improving its performance and reducing the training time for new tasks.

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“…For example, promising results of early detection of Xylella fastidiosa using UV multispectral imaging was recently generated in a study conducted in Apulia ( Castrignanò et al., 2020 ). Although it may be challenging to distinguish pathogen-triggered symptoms from abiotic stress in some cases, if combined with advanced deep learning algorithms UAV-produced imaging data does have the potential to aide detection and classification of olive foliar diseases ( Ksibi et al., 2022 ). Concomitant application of climate models to disease and pest forecasts will likely also be an important aspect in the olive orchard of the future.…”

Section: Sustainable Integrated Pathogen and Pest Management In Futur...mentioning

confidence: 99%

Innovations towards sustainable olive crop management: a new dawn by precision agriculture including endo-therapy

Grandi,

Oehl,

Lombardi

et al. 2023

Front. Plant Sci.

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Olive trees (Olea europaea L.) are deeply interwoven with the past, present and future of Mediterranean civilizations, both as essential economical as well as cultural valuables. Olive horticulture constitutes one of the primary agroecosystems in the Mediterranean regions of today. Being inhabitant of ecological niches, Olea europaea is prone to a peculiar vulnerability towards climatic and socioeconomical transformations that are briskly reshaping regional and global agroecosystems. Because of climatic changes and the biosafety risks of global agricultural trades, olive plants are highly susceptible to newly emerging diseases and pests, and the traditional olive horticultural crop protection practices are under scrutiny towards reducing their ecological impact. Hence there is an eminent demand for a more nature-positive olive tree crop management. Recent innovations in precision agriculture are raising the prospect for innovative crop protection methods that may provide olive farmers the required agility to respond to present and future agricultural challenges. For instance, endo-therapy, which is the systemic delivery of active ingredients via trunk injection, is a technology that holds promise of a true step-change in sustainable olive crop management. Endo-therapy allows reaching vascular diseases inaccessible to foliar treatments and delivers active ingredients in a precise manner with no risks of off-target drifts. Similarly, progresses in biological precision delivery using controlled release of active ingredients based on innovative formulation technologies are showing an exciting potential for more targeted and sustainable crop protection solutions. This review summarizes the latest innovations on both physical and biological precision deliveries in the realm of olive arboriculture in the Mediterranean regions and gives an outlook how these technologies may help orchestrating innovative olive culture practices soon.

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MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases

Cited by 37 publications

References 31 publications

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Optimizing olive disease classification through transfer learning with unmanned aerial vehicle imagery

Innovations towards sustainable olive crop management: a new dawn by precision agriculture including endo-therapy

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