A Computer Vision System for Guava Disease Detection and Recommend Curative Solution Using Deep Learning Approach

Haque, A S M Farhan Al; Hafiz, Rubaiya; Hakim, Md. Azizul; Islam, Gazi

doi:10.1109/iccit48885.2019.9038598

Cited by 27 publications

(13 citation statements)

References 11 publications

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“…Both diseased and healthy leaf were including images for the purpose of training and testing the proposed model. Haque et al [ 46 ] collected ten thousand images using a Nikon D7200 DSLR camera, under various conditions, for four different types of guavas: fruit canker, anthracnose, fruit rot (disease-impacted), and healthy guava. Sahithya et al [ 47 ] acquired ladies’ finger leaf images using a 1584 × 3456 resolution digital camera.…”

Section: Related Workmentioning

confidence: 99%

“…Haque et al [ 46 ] applied several augmentation methods, including flipping (horizontal flip), zooming, shifting (height and breadth), rotating, nearest fill, and shearing, to lessen the overfitting of the guava images in the dataset. Coulibaly et al [ 19 ] utilized four different operations by which images were augmented, namely, rescale, flipping, shift, and zoom.…”

Section: Related Workmentioning

confidence: 99%

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Plant Disease Detection and Classification: A Systematic Literature Review

Ramanjot

Mittal

Wadhawan

et al. 2023

Sensors

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A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Plant Disease Detection and Classification: A Systematic Literature Review

Ramanjot

Mittal

Wadhawan

et al. 2023

Sensors

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“…By achieving a better accuracy of flower classification than other traditional methods, they have showed a remarkable enhancement. S. M. Farhan Al Haque et al [13] proposed a system for the detection of guava disease using deep learning models. Their final model achieved 95.61% of accuracy.…”

Section: Related Workmentioning

confidence: 99%

Local Rose Breeds Detection System Using Transfer Learning Techniques

Farha

Hakim

Khatun

2022

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Flower breed detection and giving details of that breed with the suggestion of cultivation processes and the way of taking care is important for flower cultivation, breed invention, and the flower business. Among all the local flowers in Bangladesh, the rose is one of the most popular and demanded flowers. Roses are the most desirable flower not only in Bangladesh but also throughout the world. Roses can be used for many other purposes apart from decoration. As roses have a great demand in the flower business so rose breed detection will be very essential. However, there is no remarkable work for breed detection of a particular flower unlike the classification of different flowers. In this research, we have proposed a model to detect rose breeds from images using transfer learning techniques. For such work in flowers, resources are not enough in image processing and classification, so we needed a large dataset of the massive number of images to train our model. we have used 1939 raw images of five different breeds and we have generated 9306 images for the training dataset and 388 images for the testing dataset to validate the model using augmentation. We have applied four transfer learning models in this research, which are Inception V3, ResNet50, Xception, and VGG16. Among these four models, VGG16 achieved the highest accuracy of 99%, which is an excellent outcome. Breed detection of a rose by using transfer learning methods is the first work on breed detection of a particular flower that is publicly available according to the study.

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“…Advances in smartphones technology [22] was introduced by classifying the diseases based on the severity levels. In [23] SVM and ANN classifier are used for the automatic citrus fruit disease detection and obtained an accuracy of 93.12 and 88.16 respectively. However, the challenges like class imbalance and decision support systems have to be addressed e-ISSN : 0976-5166 p-ISSN : 2231-3850 further.…”

Section: Literature Reviewmentioning

confidence: 99%

An LSTM Based CNN Model for Pomegranate Fruit Classification With Weight Optimization Using Dragonfly Technique

Vasumathi¹,

Kamarasan²

2021

INDJCSE

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Pomegranate is a widely grown plant in India. This highly beneficial fruit is infected by multiple pests and diseases which cause great economical losses. Different forms of pathogen diseases on leaf, stem and the fruits are present. Some of the diseases that affect pomegranate fruits are anthracnose, cercospora, heart rot and bacterial blight. There is a need for disease control strategies to incorporate timely action on the developed diseases. Thus, there is a need for intelligent and self-learning recognition systems to detect these diseases on time. This study is aiming to classify pomegranate fruits into two classes normal and abnormal using CNN LSTM technique. This research work uses a hybrid CNN-LSTM technique to detect four types of diseases present in the pomegranate fruits and classify them into four classes. The results obtained using CNN LSTM are then optimized using dragonfly algorithm. The features like colour, texture and shape of the fruits are collected and fed into the hybrid CNN-LSTM. The dataset for the classifier is given as an excel file which is initially pre-processed using map reduce technique and dimensionality reduction carried using Principal Component Analysis and Discriminant analysis. The CNN LSTM classifier identifies the 4 types of diseases and normal fruit. The classification is further optimized using dragonfly algorithm. The optimized weight and cost function has further explored to support the multi-class disease detection process. Experimental results have shown an accuracy of 92% in classification using CNN-LSTM technique and optimization using dragonfly techniques shows an improved classification accuracy of 97.1%.

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A Computer Vision System for Guava Disease Detection and Recommend Curative Solution Using Deep Learning Approach

Cited by 27 publications

References 11 publications

Plant Disease Detection and Classification: A Systematic Literature Review

Plant Disease Detection and Classification: A Systematic Literature Review

Local Rose Breeds Detection System Using Transfer Learning Techniques

An LSTM Based CNN Model for Pomegranate Fruit Classification With Weight Optimization Using Dragonfly Technique

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