A Deep Learning Approach for Enhancing Crop Disease Detection and Pesticide Management

Abstract

The rapid growth of the global population and the intensification of agricultural practices have significantly increased the vulnerability of crops to diseases and pest infestations, leading to substantial yield losses and economic instability in the agri-food sector. Traditional crop disease identification and pesticide application methods are largely manual, time-consuming, and prone to human error, often resulting in delayed intervention and excessive or inefficient pesticide usage. In recent years, deep learning has emerged as a powerful paradigm for automated crop disease detection due to its superior capability in learning complex visual patterns from largescale image data. This research presents a deep learning–based approach for enhancing crop disease detection and pesticide management by leveraging advanced convolutional neural networks and intelligent decision-support mechanisms. The proposed approach aims to achieve accurate and early-stage disease identification while facilitating targeted and optimized pesticide recommendations, thereby minimizing chemical overuse and environmental impact. By integrating image-based disease recognition with intelligent inference models, the system supports precision agriculture objectives, including improved crop health monitoring, sustainable pest control, and increased agricultural productivity. The study synthesizes recent advances in deep learning architectures, dataset augmentation strategies, and evaluation metrics relevant to real-world agricultural deployment. The findings underscore the potential of deep learning–driven systems to transform crop protection practices by enabling scalable, real-time, and cost-effective disease detection and pesticide optimization.

Code and data availability

The article describes a multi-crop leaf image dataset and a deep learning framework, but contains no public dataset deposit, code repository, model checkpoint, or supplement with author-provided URLs. No availability or contact-for-data language appears in the supplied blocks.

No evidence-backed public reproduction asset is currently recorded.