ArticleFrontiers in plant science2023
A mobile-based system for maize plant leaf disease detection and classification using deep learning.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
- YOLOv11-MPD: A Multi-Part Maize Disease Detection Algorithm for Complex Field Environments.Sensors (Basel, Switzerland) · 2026Article
- A low-cost "plant-scanner" platform for automated detection of Ustilago maydis infection in maize using deep learning.Scientific reports · 2026Article
- Hybrid metaheuristic optimization of a DeepFusionNet for plant leaf disease diagnosis and recommendation.Plant methods · 2026Article
- DELP-YOLOv12: a lightweight deployable model for maize pest and disease detection.Plant methods · 2026Article
- A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.Journal of imaging · 2026Review
- Deep learning framework for timely detection and classification of chili leaf diseases and pests.Scientific reports · 2026Article
- Real-time detection of plant leaf diseases based on improved YOLOv13-LM in complex field environments.Frontiers in plant science · 2026Article
- LSL-YOLO11n: a YOLO11n-based model for maize leaf disease detection in complex field environments.Frontiers in plant science · 2026Article
- ZamYOLO-maize: a YOLOv8n-based deep learning framework for automated detection and classification of maize leaf diseases in field conditions in Zambia.Frontiers in artificial intelligence · 2026Article
- A Dynamic Kalman Filtering Method for Multi-Object Fruit Tracking and Counting in Complex Orchards.Sensors (Basel, Switzerland) · 2025Article
- Plant leaf disease detection using vision transformers for precision agriculture.Scientific reports · 2025Article
- Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN-ViT Model.Food science & nutrition · 2025Article
- Mobile based deep CNN model for maize leaf disease detection and classification.Plant methods · 2025Article
- Detection of kidney bean leaf spot disease based on a hybrid deep learning model.Scientific reports · 2025Article
- Detection of cotton crops diseases using customized deep learning model.Scientific reports · 2025Article
- Sugarcane leaf disease classification using deep neural network approach.BMC plant biology · 2025Article
- CDPNet: a deformable ProtoPNet for interpretable wheat leaf disease identification.Frontiers in plant science · 2025Article
- Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.Food science & nutrition · 2025Article
- Few-Shot Conditional Learning: Automatic and Reliable Device Classification for Medical Test Equipment.Journal of imaging · 2024Article
- Efficient and accurate identification of maize rust disease using deep learning model.Frontiers in plant science · 2024Article
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6 authors.
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Abstract
Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.
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