Tomato Leaf Disease Detection Using Convolutional Neural Networks

Tm, Prajwala; Pranathi, Alla; SaiAshritha, Kandiraju; Chittaragi, Nagaratna B.; Koolagudi, Shashidhar G.

doi:10.1109/ic3.2018.8530532

Cited by 210 publications

(78 citation statements)

References 8 publications

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“…Convolution neural networks (CNN) can be used to create a computational model that uses unstructured inputs and finds outputs with corresponding labels, thereby correlating image inputs with output results [24].…”

Section: Proposed Cnn Modelmentioning

confidence: 99%

“…Several studies have been carried out using deep learning applied to image recognition of pathologies in plants; the most promising ones use the architecture of convolutional neural networks (CNN). To identify diseases on leaves, some authors have recently proposed the use of CNN to perform the identification of diseases through digital images extracted from leaves [16,[19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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Nutritional Evaluation of Brachiaria brizantha cv. marandu using Convolutional Neural Networks

et al. 2020

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The identification of plant nutritional stress based on visual symptoms is predominantly done manually and is performed by trained specialists to identify such anomalies. In addition, this process tends to be very time consuming, has a variability between crop areas and is often required for analysis at various points of the property. This work proposes an image recognition system that analyzes the nutritional status of the plant to help solve these problems. The methodology uses deep learning that automates the process of identifying and classifying nutritional stress of Brachiaria brizantha cv. marandu. An image recognition system was built and analyzes the nutritional status of the plant using the digital images of its leaves. The system identifies and classifies Nitrogen and Potassium deficiencies. Upon receiving the image of the pasture leaf, after a classification performed by a convolutional neural network (CNN), the system presents the result of the diagnosed nutritional status. Tests performed to identify the nutritional status of the leaves presented an accuracy of 96%. We are working to expand the data of the image database to obtain an increase in the accuracy levels, aiming at the training with a larger amount of information presented to CNN and, thus, obtaining results that are more expressive.

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Section: Proposed Cnn Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nutritional Evaluation of Brachiaria brizantha cv. marandu using Convolutional Neural Networks

et al. 2020

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“…Tomato leaf disease detection using CNN (2) employed 3 convolutional layers. The hyperparameters of this model are 10000 epochs each with a batch size of 64, a learning rate of 0.001 and a dropout rate of 0.5.…”

Section: Introductionmentioning

confidence: 99%

An Effective Pomegranate Fruit Classification Based On CNN-LSTM Deep Learning Models

Vasumathi¹,

Kamarasan²

2021

IJST

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Objectives: To employ a deep learning technique that would sort the fruits into normal and abnormal based on the features such as fruit colour, number of fruit spots, and shape of the fruit. Methods: A combined CNN LSTM deep learning model is applied to classify a set of 6519 fruits into two classes namely normal and abnormal. The dataset is an excel file with each record consisting of 9 features. Convolutional Neural Networks (CNN) are applied for deep feature extraction and Long-Short Term Memory (LSTM) is used to detect the class based on extracted features. Findings: The proposed system achieved an accuracy of 98.17%, specificity of 98.65%, sensitivity of 97.77%, and an F1-score of 98.39%. Novelty: The sensitivity of disease detection was less with lesser availability of enhanced detection methods for detecting disease in earlier stages. The issue with these various existing algorithms is that the accuracy was reduced since some sources of errors were not eliminated. Deep Learning delivers methodologies, approaches, and functionalities that can help to resolve analytic and predictive analysis accurately.

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“…ANNs allow us to develop models based on the intrinsic relations among the variables, without prior knowledge of their functional relationships [9]. Soft computing for ANN techniques has been widely used to develop models to predict different crop indicators, such as growth, yield, and other biophysical processes, and also because of the commercial importance of tomato [10][11][12][13][14][15][16][17][18][19][20][21][22][23] and other vegetables, such as lettuce [24][25][26][27][28][29][30], pepper [31][32][33][34], cucumber [35][36][37][38], wheat [39][40][41][42][43][44][45], rice [46][47][48], oat [49], maize [50,51], corn [52][53][54], corn and soybean [55], soybean…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

et al. 2020

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Non-linear systems, such as biological systems, can be simulated by artificial neural network (ANN) techniques. This research aims to use ANN to simulate the accumulated aerial dry matter (leaf, stem, and fruit) and fresh fruit yield of a tomato crop. Two feed-forward backpropagation ANNs, with three hidden layers, were trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted. The input layer consisted of the leaf area, plant height, fruit number, dry matter of leaves, stems and fruits, and the growth degree-days at 136 days after transplanting (DAT); these were obtained from a tomato crop, a hybrid, EL CID F1, with indeterminate growth habits, grown with a mixture of peat moss and perlite 1:1 (v/v) (substrate) and calcareous soil (soil). Based on the experimentation of the ANNs with one, two and three hidden layers, with MSE values less than 1.55, 0.94 and 0.49, respectively, the ANN with three hidden layers was chosen. The 7-10-7-5-2 and 7-10-8-5-2 topologies showed the best performance for the substrate (R = 0.97, MSE = 0.107, error = 12.06%) and soil (R = 0.94, MSE = 0.049, error = 13.65%), respectively. These topologies correctly simulated the aerial dry matter and the fresh fruit yield of the studied tomato crop.

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Tomato Leaf Disease Detection Using Convolutional Neural Networks

Cited by 210 publications

References 8 publications

Nutritional Evaluation of Brachiaria brizantha cv. marandu using Convolutional Neural Networks

Nutritional Evaluation of Brachiaria brizantha cv. marandu using Convolutional Neural Networks

An Effective Pomegranate Fruit Classification Based On CNN-LSTM Deep Learning Models

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

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