Applying a Convolutional Neural Network in an IoT Robotic System for Plant Disease Diagnosis

Xenakis, Apostolos; Gerogiannis, Vassilis C.; Stamoulis, George

doi:10.1109/iisa50023.2020.9284356

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…Due to the conflict between the high computational power requirements of the models and the limited computational power of plant protection equipment, it is a challenging task to deploy plant disease detection models on mobile platforms ( Neupane and Baysal-Gurel, 2021 ). Currently, mobile devices are mostly used as a means of image acquisition, with disease images being transferred to more capable devices for identification ( Xenakis et al., 2020 ). Nevertheless, recent research highlights that image recognition can be achieved using shallow networks as well ( Kundu et al., 2021 ; Wieczorek et al, 2022 ), with model pruning being an effective model compression method whose core strategy is reducing the DNN’s complexity via discarding redundant and uninformative weights ( Han et al., 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of plant leaf diseases by deep learning based on channel attention and channel pruning

Chen

Huang

et al. 2022

Front. Plant Sci.

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Plant diseases cause significant economic losses and food security in agriculture each year, with the critical path to reducing losses being accurate identification and timely diagnosis of plant diseases. Currently, deep neural networks have been extensively applied in plant disease identification, but such approaches still suffer from low identification accuracy and numerous parameters. Hence, this paper proposes a model combining channel attention and channel pruning called CACPNET, suitable for disease identification of common species. The channel attention mechanism adopts a local cross-channel strategy without dimensionality reduction, which is inserted into a ResNet-18-based model that combines global average pooling with global max pooling to effectively improve the features’ extracting ability of plant leaf diseases. Based on the model’s optimum feature extraction condition, unimportant channels are removed to reduce the model’s parameters and complexity via the L1-norm channel weight and local compression ratio. The accuracy of CACPNET on the public dataset PlantVillage reaches 99.7% and achieves 97.7% on the local peanut leaf disease dataset. Compared with the base ResNet-18 model, the floating point operations (FLOPs) decreased by 30.35%, the parameters by 57.97%, the model size by 57.85%, and the GPU RAM requirements by 8.3%. Additionally, CACPNET outperforms current models considering inference time and throughput, reaching 22.8 ms/frame and 75.5 frames/s, respectively. The results outline that CACPNET is appealing for deployment on edge devices to improve the efficiency of precision agriculture in plant disease detection.

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Section: Introductionmentioning

confidence: 99%

Identification of plant leaf diseases by deep learning based on channel attention and channel pruning

Chen

Huang

et al. 2022

Front. Plant Sci.

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“…Inadequate fertilization affects crop growth and only affects crop growth and growth but also raises the possibility of problems such as resource waste, environmental pollution, and land deterioration. [ 7 ]…”

Section: Soil Analysismentioning

confidence: 99%

“…The DDSS uses a Convolution Neural Network learning method to diagnose and classify early plant diseases. [ 7 ] Many Artificial Intelligent based techniques have been used to improve the productivity and enhance the quality of production in farms that will be helpful in Precision Agriculture. Artificial intelligence detects problems and develops solutions to help innovators, people, and society.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of AI Based Model for Identification of Different Soil Types Using Tensorflow

Mavi,

Mittal,

Sarangi

2024

Macromolecular Symposia

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The agricultural industry is among the most important industry in the world and playing a critical part in human growth. Soil classification is becoming incredibly valuable, and recent study reveals that classification of images is proven as the preferred way of soil information for farmers. The testing of soil is the first step in construction planning to determine whether the plot of land is suitable for constructing any structure in order to prevent future disasters. In this work, the model has been proposed to address the visual classification of types of soil using the deep neural network (DNN) and tensor flow framework. Further, AI based sequential model has been created in python to classify the soil. This has been analyzed that with the use of proposed technique five types of soil viz. black, cinder, laterite, peat, and yellow soil has been classified while keeping the practical simplicity of the images. Far superior accuracy has been achieved using the CNN model on the above dataset. The model is useful for farmers to increase the crop production after identification of the appropriate soil type for respective crops.

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“…Additionally, it mentions other research studies on cotton categorization of leaf spot disease employing several techniques for image processing. Apostolos Xenakis et al [14] discusses using advanced technology like Convolutional Neural Networks (CNNs) in an IoT Robotic System to help farmers detect plant diseases early. By uploading leaf images to a mobile app, the system can identify diseases and send results back to the user.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Plant Diseases using Advanced Deep Learning Methods

Ameen,

Susmitha,

Vyshnavi

et al. 2024

eatp

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Plant diseases are a major global danger to crop productivity. The labor-intensive and time-consuming nature of traditional disease detection technologies causes delays in diagnosis and treatment. The progression of automated methods for identifying and diagnosis plant diseases has gained popularity with the growth of deep learning techniques, especially vision for computers and machine learning. This study examines the application Region based Convolutional Neural Networks (R-CNN) and Visual Geometry Group (VGG) to detect plant disease with highest accuracy of 0.9827 . With particular emphasis on the creation of a reliable and effective system for quick recognition and medical treatment. Used Village dataset and it is compared with existing models and achieved higher accuracy. The results of the experiments show the suggested model works to precisely detect different plant diseases Modern deep learning techniques have made a significant contribution to the diagnosis of plant diseases, providing prospective means of reducing crop losses, raising agricultural output, and securing the world's food supply.

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Applying a Convolutional Neural Network in an IoT Robotic System for Plant Disease Diagnosis

Cited by 16 publications

References 8 publications

Identification of plant leaf diseases by deep learning based on channel attention and channel pruning

Identification of plant leaf diseases by deep learning based on channel attention and channel pruning

Implementation of AI Based Model for Identification of Different Soil Types Using Tensorflow

Detection of Plant Diseases using Advanced Deep Learning Methods

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