Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review

Tuğrul, Bülent; Elfatimi, Elhoucine; Eryiğit, Resul

doi:10.3390/agriculture12081192

Cited by 93 publications

(36 citation statements)

References 120 publications

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“…Neural networks with two or more layers are the conceptual basis to generate DL models, whose progress has been spectacular in recent years in all disciplines, even in precision crop protection ( Ferentinos, 2018 ; Kamilaris and Prenafeta-Boldú, 2018 ; Xia et al., 2018 ; Farooq et al., 2019 ; Hasan et al., 2021 ; Rakhmatulin et al., 2021 ; Allmendinger et al., 2022 ; Tugrul et al., 2022 ; Rai et al., 2023 ). DL algorithms transform data to construct complex concepts in a hierarchical structure with several levels of abstraction, so that the higher levels are composed of the characteristics of the lower levels ( LeCun et al., 2015 ).…”

Section: Taxonomy Based On the Tasks To Be Solvedmentioning

confidence: 99%

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

et al. 2023

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Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.

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Section: Taxonomy Based On the Tasks To Be Solvedmentioning

confidence: 99%

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

et al. 2023

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“…The models are developed with deep, complex, and high parameters to achieve high accuracy with a limitation of complex computation. VGGNet, InceptionV3, AlexNet, GoogLeNet, and XceptionNet are widely used DL models [144]. Some models are developed to achieve computation efficiency with a restriction of adequate accuracy.…”

Section: Analysis Of Various Classification Approachesmentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection.

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“…Using a mix of a Deep Learning classification model (CNN) and a features selection method genetic algorithm (GA), a model is presented for the diagnosis and recognition of tomato plant disease using the leaf image data ( Tugrul et al., 2022 ). The proposed given framework was trained on 500 images belonging to 4 types of diseases.…”

Section: Related Workmentioning

confidence: 99%

“…According to the Ministry of Food Processing Industries, agricultural losses in 2016 totaled thirteen billion US dollars ( GoI, 2019 ). Image processing and neural networks can be used to perform one of the beneficial steps in plant disease diagnosis techniques ( Tugrul et al., 2022 ). Recent research has demonstrated that neural networks and deep learning perform categorization tasks effectively.…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Shoaib

Shah²,

Hussain³

et al. 2022

Front. Plant Sci.

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Plants are the primary source of food for world’s population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant’s survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield.

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Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review

Cited by 93 publications

References 120 publications

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

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