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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Sagar, Sanjeela
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Extracting features of tomato viral leaf diseases using image processing techniques Sagar, Sanjeela; Singh, Jaswinder
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp925-932

Abstract

Agriculture is the main livelihood of Indians. More than 50% of Indian population Is dependent on it and it contributes about 18% of Indian gross domestic product (GDP). According to Inc42, the agricultural sector of India is predicted to increase to US$ 24 billion by 2025. With the increase in population, the demand for food also increases, but more than 30% of crops get affected due to crop diseases. Overall, India lost approximately five million hectares of crop area to flash floods, cyclonic storms, floods, cloudbursts, and landslides till 2021. In that case, there is a need to prevent crops from diseases to fulfil demand supply ratio. This paper presents the feature extraction of tomato viral leaf diseases using various image processing techniques. Most of the research uses Convolutional Neural networks to extract the features of these diseases, but these neural networks are not performing much accurately in real scenarios, so there is a need to extract the features using image processing methods. During the study, it is found that these diseases have different colours, shapes and textures and these features can be used with convolution neural networks to bring more accurate results in real scenarios.
An experimental study of tomato viral leaf diseases detection using machine learning classification techniques Sagar, Sanjeela; Singh, Jaswinder
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4385

Abstract

Agriculture is the backbone of India and more than 50% of the population is dependent on it. With the increasing demand for food with the increase in population, it is the need of time that crops should be prevented against diseases. More than 1K acres of land with tomato diseases got affected in Pune only during this pandemic (2021). It could have been prevented by correct identification of the disease and then by corrective measures. This paper presents the experimental and comparative study of tomato leaf disease classification using various traditional machine learning algorithms like random forest (RF), support vector machines (SVM), naïve bayes (NB), and deep learning convolutional neural network (CNN) algorithm. In this study, it is perceived that CNN with a pre-trained Inception v3 model was able to detect and classify better than traditional methods with more than 95% accuracy.
Co-Authors Singh, Jaswinder