Effective Classification of Plant Diseases Using Blend Unity Resqueeze With ResNet Model

Abstract

Agricultural is the primary source of essential provisions almost all nation and remains as a vital survival tool for human race for past, present and future. The agriculture which remains as the bedrock of the civilizations is frequently being affected by devastating plant diseases leading to the loss of economy and food shortage. The manual testing requires high expertise, time and often leads to human error. Recent advancements in computer vision and AI models have highlighted the potential for building automatic plant disease detection models based on visible signs using image classification tasks. However, the task becomes complex and erroneous due to the complex nature of data. Consequently Deep Learning (DL) models tend to outperform others traditional methods through utilizing network topology with convolution layers in core. Projected system uses high quality Cotton Disease dataset sourced by Kaggle and provide a suitable solution to the aforementioned problem using advanced DL neural network namely Blend Unity Resqueeze Resnet approach which yields high accuracy with modification including Resqueeze layer and blend unity weights applied to do the tedious job of diagnosing plant diseases on image based classification. The proposed research outperforms the conventional methods achieving better accuracy of 92% with better precision and reliability. The outcome of the respective research is analyzed with suitable metrics and compared with the recent developed conventional algorithms in which the proposed model proves to be a better suited for the efficient plant disease classification. Hence, the proposed method is intended to contribute in the plant disease classification and assist the agriculturists significantly to prevent the losses due to crop diseases.

Plant phenotyping relevance

植物画像から病害状態を分類する深層学習手法の開発・比較が中心であり、植物表現型（病害状態）の取得・推定に該当する。

Code and data availability

The paper's plant-phenotyping measurements are based on the public Cotton Disease Dataset from Kaggle, explicitly cited with URL and CC BY 4.0 license. No author analysis code or trained model is shared.