Perception of Groundnuts Leaf Disease by Neural Network with Progressive Re-Sizing

Abstract

India is the world's second-largest groundnut producer after Brazil. An major crop of oilseeds is groundnuts. Because of this, the crop's quality and yield have declined, which has had a detrimental effect on the agricultural economy. This is partly because the crop is more susceptible to various diseases. It is required to create more precise and reliable automated approaches to address this problem and improve the identification of groundnut leaf diseases. This article proposes a deep learning-driven approach based on a progressive scaling technique for the accurate classification and identification of groundnut leaf diseases. The five main groundnut leaf diseases that are the subject of this study are leaf spot, armyworm effect, wilts, yellow leaf, and healthy leaf. The proposed model is trained using both progressive resizing and conventional techniques, and its performance is assessed using cross-entropy loss. A fresh dataset is meticulously curated in Gujarat state, India's Saurashtra region, for training and validation. Due to the dataset's uneven sample distribution across disease categories, an extended focus loss function was used to correct this class imbalance. In order to evaluate the performance of the suggested model, a number of performance metrics are utilized, including accuracy, sensitivity, F1-score, precision, and sensitivity. Notably, the suggested model has a 96.12% success rate, which signifies a considerable increase in the disease identification accuracy. It's important to note that the model incorporating progressive resizing beats the basic neural network-based model based on cross-entropy loss, highlighting the potency of the recommended approach.

Plant phenotyping relevance

落花生葉の病害状態を画像から分類する深層学習手法を開発・評価しており、植物病害表現型の取得・推定が中心です。

Code and data availability

The paper describes a curated groundnut leaf disease image dataset and a Densenet-169 classification model, but provides no public deposit, repository, or download URL for the dataset, images, code, or trained model. The only stated intent is aspirational ('we believe it's essential to create and make available a data[

No evidence-backed public reproduction asset is currently recorded.