Leaf Disease Diagnosis using Online and Batch Backpropagation neural network

Sapkal, Ashwini; Kulkarni, Uday

doi:10.26438/ijcse/v6i6.362366

Cited by 2 publications

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“…Images of infected leaves that are input into the system are processed using image processing algorithms to extract features from the pictures. According to Sapkal and Kulkarni (2018), there are two primary types of feature extraction methods employed. The first method involves utilizing image processing techniques to extract features from the input of infected leaf images.…”

Section: Feature Extraction For Disease Identificationmentioning

confidence: 99%

Crop Disease Detection Using Deep Learning Techniques on Images

Deputy,

Passi,

Jain

2023

Journal of Computer Science

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Agriculture plays a crucial role in the economic development of many countries and sustains the global population despite facing various challenges like climate change, pollinator decline, and plant diseases. These threats to food security highlight the need for innovative solutions to prevent crop loss. Leveraging smartphone technology for automated image recognition-based disease diagnosis has emerged as a promising approach, thanks to their computing power and highresolution cameras. To address this issue, we have focused on deep learning-based image detection techniques to identify plant diseases using the "PlantVillage" dataset. Several deep learning architectures, including AlexNet, GoogleNet, ResNet50, and InceptionV3, were employed and trained using two approaches: 'Training from scratch' and 'transfer learning'. The results of the analysis reveal GoogLeNet architecture achieved the highest accuracy of 0.999 for color images and 0.996 for segmented images, whereas InceptionV3 trained from scratch gave the highest accuracy of 0.994 for grayscale images with a train-test ratio of 90:10. All the models trained from scratch achieved the maximum F1-score of 1.0 for color and segmented images whereas for grayscale images, GoogleNet and InceptionV3 achieved the highest F1-score of 0.999 with train-test ratio 90:10. These findings indicate the potential of deep learning methods in detecting and diagnosing plant diseases, which can significantly enhance the efficiency and accuracy of disease diagnosis processes in agriculture. Further research and improvements in image recognition techniques can lead to more robust and effective solutions for securing global food production.

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Section: Feature Extraction For Disease Identificationmentioning

confidence: 99%

Crop Disease Detection Using Deep Learning Techniques on Images

Deputy,

Passi,

Jain

2023

Journal of Computer Science

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“…The characteristics are then used as sources for classifiers after the features have been reduced, and probabilistic neural network (PNN) classifiers are used to categorize image samples. Sapkal et al [45] when illnesses and insects in rice seedlings are accurately and sensibly diagnosed, farmers can provide urgent care for the plants and considerably reduce economic harm. For the detection and diagnosis of rice illnesses and parasites, large-scale designs have also been used.…”

Section: Literature Reviewmentioning

confidence: 99%

Advanced Hybrid Model for Multi Paddy diseases detection using Deep Learning

Dixit,

Verma

2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Rapid developments in deep learning (DL) techniques have made it possible to find and recognize objects in pictures. To create a network that is significantly more successful than a single CNN, GAN, RNN, etc., we can mix various neural network models (CNN, GAN, RNN).this combination is known as hybrid model. Hybrid model of deep leaning is give more accurately result for detection and identification of paddy diseases. OBJECTIVES: I have studies outcome of hybrid model 1(DCNN+SVM) and Hybrid model 2 (DCNN + Transfer Learning) to increase accuracy of Rice plant disease detection and classification. The Researched model detects multiple rice plant diseases and it is giving same result in multiple data sets. METHODS: The Proposed System have used Deep Learning Image Processing algorithm and neural Network Like DCNN ,SVM and Transfer Learning .The brand new model is DST where D stands for DCNN, S stands for SVM and T stands for transfer learning. RESULTS: The Researched DST model achieved 95% Training accuracy and 85% validation Accuracy. The Researched model detect multiple rice plant diseases and it is giving same result in multiple data set. CONCLUSION: The proposed model combined 2 existing model and developed hybrid model that a detect various rice plant diseases with better accuracy from available existing model.

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Leaf Disease Diagnosis using Online and Batch Backpropagation neural network

Cited by 2 publications

References 0 publications

Crop Disease Detection Using Deep Learning Techniques on Images

Crop Disease Detection Using Deep Learning Techniques on Images

Advanced Hybrid Model for Multi Paddy diseases detection using Deep Learning

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