Image-based Plant Diseases Detection using Deep Learning

Panchal, Adesh V.; Patel, Subhash Chandra; Bagyalakshmi, K.; Kumar, Pankaj; Khan, Ihtiram Raza; Soni, Mukesh

doi:10.1016/j.matpr.2021.07.281

Cited by 60 publications

(24 citation statements)

References 26 publications

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“…The paper [2] used AlexNet and GoogLeNet with and without transfer learning on the PlantVillage dataset to achieve 99.35% accuracy. They also visualize activations and test on scraped data from Bing and Google Search.…”

Section: B Plant Disease Detectionmentioning

confidence: 99%

“…In [2], the authors use VGG, ResNet, Inception-V3 on an augmented version of PlantVillage dataset with 87K images, and conclude that VGG is the best for their settings.…”

Section: B Plant Disease Detectionmentioning

confidence: 99%

“…In India, more than 70% population is dependent on agriculture [1]. More than 15% of the crops get wasted in India due to diseases and hence it has become one of the major concerns to be resolved [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions

Gupta¹,

Chopade²,

Jain³

et al. 2022

Preprint

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Farmers face several challenges when growing crops like uncertain irrigation, poor soil quality, etc. Especially in India, a major fraction of farmers do not have the knowledge to select appropriate crops and fertilizers. Moreover, crop failure due to disease causes a significant loss to the farmers, as well as the consumers. While there have been recent developments in the automated detection of these diseases using Machine Learning techniques, the utilization of Deep Learning has not been fully explored. Additionally, such models are not easy to use because of the high-quality data used in their training, lack of computational power, and poor generalizability of the models. To this end, we create an open-source easy-to-use web application to address some of these issues which may help improve crop production. In particular, we support crop recommendation, fertilizer recommendation, plant disease prediction, and an interactive news-feed. In addition, we also use interpretability techniques in an attempt to explain the prediction made by our disease detection model.

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Section: B Plant Disease Detectionmentioning

confidence: 99%

“…In [2], the authors use VGG, ResNet, Inception-V3 on an augmented version of PlantVillage dataset with 87K images, and conclude that VGG is the best for their settings.…”

Section: B Plant Disease Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions

Gupta¹,

Chopade²,

Jain³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, another considerable performance for the recognition of plant diseases by image-based naive networks (Relu rectifier linear unit), and transfer learning (VGGNet, ResNet50, and Inception-v3). VGG16 had the most relevant result with 93.5 classification accuracy [16] Rangarajan et al ( 2018) have examined the separability of pre-trained AlexNet and VGG16 net among six health classes of tomato crops (13,262 segmented images from PlantVillage dataset).…”

Section: Related Workmentioning

confidence: 99%

MajraDoc an Image based Disease Detection App for Agricultural Plants using Deep Learning Techniques

Alfozan¹,

Hassan²

2021

Computing Advances &Amp; Trends

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Infection of agricultural plants is a serious threat to food safety. It can severely damage plants' yielding capacity. Farmers are the primary victims of this threat. Due to the advancement of AI, image-based intelligent apps can play a vital role in mitigating this threat by quick and early detection of plants infections. In this paper, we present a mobile app in this regard. We have developed MajraDoc to detect some common diseases in local agricultural plants. We have created a dataset of 10886 images for ten classes of plants diseases to train the deep neural network. The VGG-19 network model was modified and trained using transfer learning techniques. The model achieved high accuracy, and the application performed well in predicting all ten classes of infections.

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“…For example, medical imaging is commonly employed in medicine to produce images such as CT scans and x-ray, which allowed the development of automated applications such as brain tumor detection from MRI images [12] and breast cancer detection using CNNs [13]. Furthermore, hyperspectral and multispectral imaging have become widely used in agriculture to create plant datasets in order to develop automated tools such as weed detection among crops [14] and plant disease detection [15]. Since CNNs are based on convolution, they are able to process images directly and extract features that can be used to issue a decision when fed to ML model such as MLP or SVM.…”

Section: Introductionmentioning

confidence: 99%

3D Convolutional Neural Networks for Solving Complex Digital Agriculture and Medical Imaging Problems

Oumaima¹

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3D signals have become widely popular in view of the advantage they provide via 3D representations of data by employing a third spatial or temporal dimension to extend 2D signals. Predominantly, 3D signals contain details inexistent in their 2D counterparts such as the depth of an image, which is inherent to point clouds (PC), or the temporal evolution of an image, which is inherent to time series data such as videos. Despite this advantage, 3D models are still underexploited in machine learning (ML) compared to 2D signals, mainly due to data scarcity. In this thesis, we exploit and determine the efficiency and influence of using both multispectral PCs and time-series data with 3D convolutional neural networks (CNNs). We evaluate the performance and utility of these networks and data in the context of two applications from the areas of digital agriculture and medical imaging. In particular, multispectral PCs are investigated for the problem of fusarium-head-blight (FHB) detection and total number of spikelets estimation, while time-series echocardiography are investigated for the problem of myocardial infarction (MI) detection. In the context of the digital agriculture application, two state-of-the-art datasets were created, namely the UW-MRDC WHEAT-PLANT PC dataset, consisting of 216 multispectral PC of wheat plants, and the UW-MRDC WHEAT-HEAD PC dataset, consisting of 80 multispectral PC of wheat heads. Both dataset samples were acquired using a multispectral 3D scanner. Moreover, a real-time parallel GPU-enabled preprocessing method, that runs 1065 times faster than its CPU counterpart, was proposed to convert multispectral PCs into multispectral 3D images compatible with CNNs. Also, the UW-MRDC WHEAT-PLANT PC dataset was used to develop novel and efficient 3D CNNs for disease detection to automatically identify wheat infected with FHB from multispectral 3D images of wheat plants. In addition, the influence of the multispectral information on the detection performance was evaluated, and our results showed the dominance of the red, green, and blue (RGB) colour channels over both the near-infra-red (NIR) channel and RGB and NIR channels combined. Our best model for FHB detection in wheat plants achieved 100% accuracy. Furthermore, the UW-MRDC WHEAT-HEAD PC dataset was used to develop unique and efficient 3D CNNs for total number of spikelets estimation in multispectral 3D images of wheat heads, in addition to adapting three benchmark 2D CNN architectures to 3D images to achieve the same purpose. Our best model for total number of spikelets estimation in wheat head achieved 1.13 mean absolute error, meaning that, on average, the difference between the estimated number of spikelets and the actual value is equal to 1.13. Our 3D CNN for FHB detection in wheat achieved the highest accuracy amongst existing FHB detection models, and our 3D CNN for total number of spikelets estimation in wheat is a unique and pioneer application. These results suggest that replacing arduous tasks that require the input of field experts and significant temporal resources with automated ML models in the context of digital agriculture is feasible and promising. In the context of the medical imaging application, an innovative, real-time, and fully automated pipeline based on 2D and 3D CNNs was proposed for early detection of MI, which is a deadly cardiac disorder, from a patient’s echocardiography. The developed pipeline consists of a 2D CNN that performs data preprocessing by segmenting the left ventricle (LV) chamber from the apical 4-chamber (A4C) view from an echocardiography, followed by a 3D CNN that performs MI detection in real-time. The pipeline was trained and tested on the HMC-QU dataset consisting of 162 echocardiography. The 2D CNN achieved 97.18% accuracy on data segmentation, and the 3D CNN achieved 90.9% accuracy, 100% precision, 95% recall, and 97.2% F1 score. Our detection results outperformed existing state-of-the-art models that were tested on the HMC-QU dataset for MI detection. Moreover, our results demonstrate that developing a fully automated system for LV segmentation and MI detection is efficient and propitious and could enable the creation of a tool that reliably suggests the presence of MI in a given echocardiography on the fly. All the empirical results achieved in our thesis indicate the efficiency and reliability of 3D signals, that are multispectral PCs and videos, in developing detection and regression 3D CNN models that can achieve accurate and reliable results.

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Image-based Plant Diseases Detection using Deep Learning

Cited by 60 publications

References 26 publications

Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions

Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions

MajraDoc an Image based Disease Detection App for Agricultural Plants using Deep Learning Techniques

3D Convolutional Neural Networks for Solving Complex Digital Agriculture and Medical Imaging Problems

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