Crop disease classification using deep learning approach: an overview and a case study

Aravind, Krishnaswamy Rangarajan; Maheswari, Prabhakar; Raja, P.; Szczepański, Cezary

doi:10.1016/b978-0-12-819764-6.00010-7

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…They have also used pre-trained DL models (VGG16 and Inception v3 respectively), but these are not suitable for online deployment in edge devices given the performance requirements of the RPi. Aravind et al (2020) have used smartphone images from four crops and succeeded with a validation accuracy of 0.9 on the online validation, using the VGG16 model [76].…”

Section: Discussionmentioning

confidence: 99%

An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevines

Morellos,

Dolaptsis,

Tziotzios

et al. 2024

Applied Sciences

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Grapevine is a valuable and profitable crop that is susceptible to various diseases, making effective disease detection crucial for crop monitoring. This work explores the use of deep learning-based plant disease detection as an alternative to traditional methods, employing an Internet of Things approach. An edge device, a Raspberry Pi 4 equipped with an RGB camera, is utilized to detect diseases in grapevine plants. Two lightweight deep learning models, MobileNet V2 and EfficientNet B0, were trained using a transfer learning technique on commercially available online dataset, then deployed and validated on field-site in an organic winery. The models’ performance was further enhanced using semantic segmentation with the Mobile-UNet algorithm. Results were reported through a web service using FastAPI. Both models achieved high training accuracies exceeding 95%, with MobileNet V2 slightly outperforming EfficientNet B0. During validation, MobileNet V2 achieved an accuracy of 94%, compared to 92% for EfficientNet B0. In terms of IoT deployment, MobileNet V2 exhibits faster inference time (330 ms) compared to EfficientNet B0 (390 ms), making it the preferred model for online deployment.

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Section: Discussionmentioning

confidence: 99%

An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevines

Morellos,

Dolaptsis,

Tziotzios

et al. 2024

Applied Sciences

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“…The deep-learning network has been used extensively as part of the automated decision-making tool by extracting hierarchical features from input data for various agricultural tasks. As a result, this wide adoption of DL has opened new possibilities for interpreting massive amounts of data accurately for agriculture analytic systems, such as: Crop surveillance systems through remote sensing to map the land cover and crop discrimination [ 68 , 69 , 70 ]; Plant-stress-monitoring systems by implementing classification and segmentation networks to better understand the interactions between pathogens, insects, and plants, as well as to determine the causes of plant stress [ 71 , 72 , 73 , 74 , 75 ]; Disease and pest identification and quantification systems that will assist in monitoring the health condition of plants, including the nutritional status, development phase, and yield prediction [ 72 , 76 , 77 , 78 , 79 ]. …”

Section: Application Of Multiscale Deep Learningmentioning

confidence: 99%

“…Disease and pest identification and quantification systems that will assist in monitoring the health condition of plants, including the nutritional status, development phase, and yield prediction [ 72 , 76 , 77 , 78 , 79 ].…”

Section: Application Of Multiscale Deep Learningmentioning

confidence: 99%

A Review on Multiscale-Deep-Learning Applications

Elizar

Zulkifley

Muharar

et al. 2022

Sensors

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In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information and the exclusion of semantic information throughout the pooling operations. In the early layers of a CNN, the network encodes simple semantic representations, such as edges and corners, while, in the latter part of the CNN, the network encodes more complex semantic features, such as complex geometric shapes. Theoretically, it is better for a CNN to extract features from different levels of semantic representation because tasks such as classification and segmentation work better when both simple and complex feature maps are utilized. Hence, it is also crucial to embed multiscale capability throughout the network so that the various scales of the features can be optimally captured to represent the intended task. Multiscale representation enables the network to fuse low-level and high-level features from a restricted receptive field to enhance the deep-model performance. The main novelty of this review is the comprehensive novel taxonomy of multiscale-deep-learning methods, which includes details of several architectures and their strengths that have been implemented in the existing works. Predominantly, multiscale approaches in deep-learning networks can be classed into two categories: multiscale feature learning and multiscale feature fusion. Multiscale feature learning refers to the method of deriving feature maps by examining kernels over several sizes to collect a larger range of relevant features and predict the input images’ spatial mapping. Multiscale feature fusion uses features with different resolutions to find patterns over short and long distances, without a deep network. Additionally, several examples of the techniques are also discussed according to their applications in satellite imagery, medical imaging, agriculture, and industrial and manufacturing systems.

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“…Ref. [7] presented an overview of ML techniques for crop disease classification. In addition, it presented to a case study where a deep learning algorithm was successfully used.…”

Section: Crop Protectionmentioning

confidence: 99%

Data analytics for crop management: a big data view

Chergui

Kechadi

2022

J Big Data

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Recent advances in Information and Communication Technologies have a significant impact on all sectors of the economy worldwide. Digital Agriculture appeared as a consequence of the democratisation of digital devices and advances in artificial intelligence and data science. Digital agriculture created new processes for making farming more productive and efficient while respecting the environment. Recent and sophisticated digital devices and data science allowed the collection and analysis of vast amounts of agricultural datasets to help farmers, agronomists, and professionals understand better farming tasks and make better decisions. In this paper, we present a systematic review of the application of data mining techniques to digital agriculture. We introduce the crop yield management process and its components while limiting this study to crop yield and monitoring. After identifying the main categories of data mining techniques for crop yield monitoring, we discuss a panoply of existing works on the use of data analytics. This is followed by a general analysis and discussion on the impact of big data on agriculture.

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Crop disease classification using deep learning approach: an overview and a case study

Cited by 7 publications

References 25 publications

An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevines

An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevines

A Review on Multiscale-Deep-Learning Applications

Data analytics for crop management: a big data view

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