Tomato Leaf Disease Detection Using Deep Learning Techniques

Ashok, Surampalli; Kishore, G R; Rajesh, Velpula; Suchitra, S.; Sophia, S.; Pavithra, B.

doi:10.1109/icces48766.2020.9137986

Cited by 129 publications

(28 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [27] presented an application of CNN with hierarchical feature extraction for disease detection in tomato plant leaves. Before applying segmentation and feature extraction, it first used Gaussian filters to remove noise from input images.…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review

et al. 2022

View full text Add to dashboard Cite

Crops’ production and quality of yields are heavily affected by crop diseases which cause adverse impacts on food security as well as economic losses. In India, agriculture is a prime source of income in most rural areas. Hence, there is an intense need to employ novel and accurate computer vision-based techniques for automatic crop disease detection and their classification so that prophylactic actions can be recommended in a timely manner. In literature, numerous computer vision-based techniques by utilizing divergent combinations of machine learning, deep learning, CNN, and various image-processing techniques along with their associated merits and demerits have already been discussed. In this study, we systematically reviewed recent research studies undertaken by a variety of scholars and researchers of fungal and bacterial plant disease detection and classification and summarized them based on vital parameters like type of crop utilized, deep learning/machine learning architecture used, dataset utilized for experiments, performance matrices, types of disease detected and classified, and highest accuracy achieved by the model. As per the analysis carried out, in the category of machine learning-based approaches, 70% of studies utilized real-field plant leaf images and 30% utilized laboratory condition plant leaf images for disease classification while in the case of deep learning-based approaches, 55% studied employed laboratory-conditioned images from the PlantVillage dataset, 25% utilized real-field images, and 20% utilized open image datasets. The average accuracy attained with deep learning-based approaches is quite higher at 98.8% as compared to machine learning-based approaches at 92.2%. In the case of deep learning-based methods, we also analyzed the performances of pretrained and training from scratch models that have been utilized in various studies for plant leaf disease classification. Pretrained models perform better with 99.64% classification accuracy compared to training from scratch models which achieved 98.64% average accuracy. We also highlighted some major issues encountered in the computer vision-based disease detection and classification approach used in literature and provided recommendations that will help and guide researchers to explore new dimensions in crop disease recognition.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…These use highly accurate methods for identifying plant disease in tomato leaves. In addition, researchers have proposed many deep learning-based solutions in disease detection and classification, as discussed below in [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ].…”

Section: Related Workmentioning

confidence: 99%

“…A spectral library has been created using different disease samples [ 40 ]. An improved VGG16 has been used to identify apple leaf disease with an accuracy rate of 99.01% [ 41 ].…”

Section: Related Workmentioning

confidence: 99%

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

Trivedi

Gautam

Anand

et al. 2021

Sensors

251

View full text Add to dashboard Cite

Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

show abstract

“…A review was presented for plant disease prediction using machine learning techniques [1]. An automatic plant disease prediction using image processing approach was proposed [2,5]. The authors proposed CNN based methodology to predict plant disease [4].…”

Section: Related Workmentioning

confidence: 99%

“…The traditional machine techniques such as Support Vector Machine, Decision Tree classification, Random forest, Naïve Bayes are used for plant disease classification and prediction. Nowadays convolutional neural networks are used for plant disease classification and prediction [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Prediction using Convolutional Neural Network

2020

jxu

View full text Add to dashboard Cite

Every year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely feature extraction, downsizing image and classification. In CNN, the convolutional layer extracts the feature from plant image. The pooling layer downsizing the image. The disease classification was done in dense layer. The proposed model can recognize 38 differing types of plant diseases out of 14 different plants with the power to differentiate plant leaves from their surroundings. The performance of VGG16 and Resnet34 was compared. The accuracy, sensitivity and specificity was taken as performance Metrix. It helps to give personalized recommendations to the farmers based on soil features, temperature and humidity.

show abstract

Tomato Leaf Disease Detection Using Deep Learning Techniques

Cited by 129 publications

References 10 publications

A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review

A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

Plant Disease Prediction using Convolutional Neural Network

Contact Info

Product

Resources

About