CNN based Leaf Disease Identification and Remedy Recommendation System

Suma, V.; Shetty, Roshan; Tated, Rishab F; Rohan, Sunku; Pujar, Triveni S

doi:10.1109/iceca.2019.8821872

Cited by 32 publications

(9 citation statements)

References 5 publications

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“…Classification accuracy of 99.32% is reported in [13]. In [14], Prashar et al report that cotton farmers in India lose about 10%-20% annual profit due to diseases that affect the crop.…”

Section: Review Of Related Workmentioning

confidence: 99%

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Peter¹,

Damuut²,

Abdulkadir³

2020

AJNNA

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In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of pasture with resultant conflicts with local farm communities in the region. The inability to find a lasting solution to these problems has led to persistent cases of deteriorating relationships amongst crop farmers and herders which has continued to precipitate hostile consequences including the loss of lives, interruption and annihilation of the family units and in some cases, whole communities are destroyed. This research attempts to address the problem of inadequate grazing resources by the use of advances in Computational Intelligence Techniques in the determination of the optimum maturity of maize, so as to complement for the grazing of livestock in the region. Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. This paper leverages on the use of Artificial Neural Networks (ANN) interfaced with image processing and Convolutional Neural Networks (pre-trained ResNet50 Network) in determining the optimum ripeness of the maize crop grown in Sub-Saharan Africa. Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. Therefore, this research posits that farmers could be sensitized on the possibility of utilizing image processing and neural networks technique in the determination of the maturity of maize in the nearest future when made operational.

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“…Classification accuracy of 99.32% is reported in [13]. In [14], Prashar et al report that cotton farmers in India lose about 10%-20% annual profit due to diseases that affect the crop.…”

Section: Review Of Related Workmentioning

confidence: 99%

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Peter¹,

Damuut²,

Abdulkadir³

2020

AJNNA

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“…Tabel Gambar 3 menunjukkan proses terjadinya konvolusi, image yang digambarkan dengan warna hijau akan dikonvolusi dengan menggunakan kernel yang digambarkan dengan warna kuning. Kernel akan bergerak dari kiri atas ke kanan bawah untuk menghasilkan convolved feature [11].…”

Section: Pendahuluanunclassified

Aplikasi Pendeteksi Penyakit Pada Daun Tanaman Apel Dengan Metode Convolutional Neural Network

Wicaksono¹,

Andryana²,

Benrahman³

2020

JOINTECS

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According to 2017 statistical fruit and vegetable crops published by BPS, total apple production in 2017 amounted to 319004 tons. There are many diseases that can attack apple plants, therefore early detection and identification of plant diseases are the main factors to prevent and reduce the spread of apple plant diseases. CNN method is used in this study with LeNet-5 architecture which can process 3151 imagery data with a mini-mum accuracy level of 75%. This study uses a dataset derived from PlantVillage created by SP Mohanty CEO & Co-founder of CrowdAI with a total of 3151 leaf images that have been classified according to their respec-tive classes. CNN stages include Convolution Layer, Rectified Linear Unit (ReLU), Subsampling, Flattening, Fully Connected Layer. The test results are evaluated using image testing data. The evaluation process is done using a confusion matrix. Based on the results of testing applications that are designed with 99,4% model ac-curacy and 97,8% validation accuracy, the application is useful for detecting apple disease using apple leaf images.

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“…The models are trained to identify ten commonly occurring rice diseases. Using the ten-fold cross-validation approach,the suggested CNN-based model achieved significantly higher accuracy.Suma, Shetty, Tated, Rohan, Pujar [6] utilized an open image dataset and made use of various semi-supervised techniques and the convolution system to perform characterization of crop species and detect the magnitude of damage. Liang, Zhang, Cao [7] implemented a CNN-based rice blast recognition approach.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is done by considering each weight's squared average and then dividing it by the square root of the mean square.The equation is given in (5) wherein θ means parameter, η is the learning rate, signifies decay term and(5) iii.Adaptive Moment Estimation(Adam) It[22]is a combination of RMSprop and Stochastic Gradient Descent along with momentum. It exploits the squared gradients to calibrate the learning rate and uses momentum by using shifting the average of the gradient like SGD with momentum.The equation is given in (6) wherein θ means parameter, η is the learning rate,∇ signifies the gradient, andJ is the loss function (6).…”

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confidence: 99%

Rice Disease Classification using Deep Convolutional Neural Network

Shrivastava¹,

Pillai²,

Baranidharan³

2020

IJITEE

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For an Agro-based country like India where agriculture acts as a main source of livelihood for more than 50% of the population, crop diseases are a major threat to food security. Hence, digital image processing along with proper machine learning algorithms can be utilized for the classification of diseases from the images of a plant. In this paper, a comparative study on the effects of different machine learning models on crop disease prediction has been done. Since Convolutional Neural Network (CNN) proved to be the best for image classification techniques, models based on CNN alone were considered in this study. We compared the performance of smallCNN with three pre-trained CNN models namely, AlexNet, ResNet-50, and VGG-16. SmallCNN is the CNN model built by us with fewer parameters and suitable for small datasets. The crop tested in this research is Oryza Sativa (Asian Rice) commonly referred to as paddy which is cultivated in abundance in India. The input dataset was fed into the model after performing appropriate pre-processing techniques followed by segmentation. The best accuracy of 66.67% was achieved in the case of ResNet-50 with Adam as the optimizer at a learning rate of 0.0001.

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CNN based Leaf Disease Identification and Remedy Recommendation System

Cited by 32 publications

References 5 publications

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Aplikasi Pendeteksi Penyakit Pada Daun Tanaman Apel Dengan Metode Convolutional Neural Network

Rice Disease Classification using Deep Convolutional Neural Network

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