Tomato Leaf Disease Detection Using Convolutional Neural Networks
By Prajwala Tm, Alla Pranathi, Kandiraju SaiAshritha, Nagaratna B. Chittaragi, Shashidhar G. Koolagudi. International Conference on Contemporary Computing, 2018
294 citations recorded by the source corpus.
The tomato crop is an important staple in the Indian market with high commercial value and is produced in large quantities. Diseases are detrimental to the plant's health which in turn affects its growth. To ensure minimal losses to the cultivated crop, it is crucial to supervise its growth. There are numerous types of tomato diseases that target the crop's leaf at an alarming rate. This paper adopts a slight variation of the convolutional neural network model called LeNet to detect and identify diseases in tomato leaves. The main aim of the proposed work is to find a solution to the problem of tomato leaf disease detection using the simplest approach while making use of minimal computing resources to achieve results comparable to state of the art techniques. Neural network models employ automatic feature extraction to aid in the classification of the input image into respective disease classes. This proposed system has achieved an average accuracy of 94–95 % indicating the feasibility of the neural network approach even under unfavourable conditions.
Subfields: Analytical Chemistry, Plant Science
View Paper: https://www.semanticscholar.org/paper/f2d03978f41755b8407cafc229b7c10335732e6d
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Citations in the corpus
- Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network (2021, methodology): “The image processing technique uses deep learning methods to identify and classify tomato plant diseases [36].”
- Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, unknown): “Studies that investigate only one type of crop in particular highlight those that address the phytopathology of Tomato crop with 18 investigations (S3, S9, S27, S44, S48, S55, S58, S61, S67, S70, S75, S78, S86, S93, S99, S102, S105, S108).”