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

Abstract

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.

Plant phenotyping relevance

トマト葉の病害状態を画像から分類するCNN手法が研究の中心であり、植物の病徴を直接推定する画像ベース表現型解析に該当する。

Code and data availability

The paper's tomato leaf disease classification experiments were performed on a publicly available Kaggle dataset (PlantVillage), explicitly cited by the authors with a public URL. No author analysis code, trained models, or supplementary data deposits are reported; the Data Availability Statement says 'Not applicable.'