# Artificial intelligence–Driven detection and decision support system for precision management of maize downy mildew

## TL;DR

This paper presents an AI system that detects maize downy mildew with high accuracy and provides farm advice to improve crop yield and profitability.

## Contribution

A web-based AI system for detecting maize downy mildew and offering precision management solutions is developed and validated in field trials.

## Key findings

- VGG16 achieved 97% accuracy in classifying maize leaves as healthy or infected.
- Field trials showed a significant reduction in disease severity and increased grain yield with AI-guided fungicide use.
- The web-based application improved economic returns with a B:C ratio of 3.36–3.57.

## Abstract

Artificial intelligence (AI) enables rapid and precise plant disease detection, offering transformative potential for crop protection. Maize downy mildew (MDM), a destructive disease, causes substantial yield losses, making early detection critical. In this study, we evaluated the performance of thirteen machine-learning (ML) and deep-learning (DL) algorithms for classifying healthy and infected maize leaves using a curated field dataset. Model performance was assessed using multiple metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. Among the tested models, VGG16 achieved the highest performance, with 97% accuracy, 0.98 precision, 0.95 recall, 0.97 F1-score, and an AUC-ROC of 0.99. Training and validation curves indicated minimal overfitting, demonstrating robust generalization. Feature visualization using t-SNE revealed clear separability between healthy and diseased samples, while Grad-CAM analysis confirmed that VGG16 focused on biologically relevant symptomatic regions, such as chlorotic streaks and leaf discoloration. Confusion matrix analysis further validated near-perfect classification, with very few misclassifications. Furthermore, we developed a web-based application (https://maize-mdm.streamlit.app/) that not only classifies MDM but also provides farm-level advisory measures. Two-year field trials of DSS-guided fungicide applications effectively suppressed MDM, reducing disease severity (PDI 3.20–5.20; PROC 93−96%), increasing grain yield (75.6–80.2 q/ha; PIOC 195−289%), and improving economic returns (B:C ratio 3.36–3.57) compared to untreated controls. Overall, this study demonstrates that AI-driven models, integrated with web-based decision support, provide accurate, interpretable, and actionable solutions for precision management of maize diseases, contributing to improved yield, profitability, and sustainable agricultural practices.