Deep Learning Applications in Maize Disease Detection: A Systematic Review of Trends, Gaps, and Future Research

Abstract

Maize, a staple crop globally, faces significant threats from various diseases that can drastically reduce yield and quality, impacting food security and economic stability, particularly in regions heavily reliant on agriculture. The search for automated and effective diagnostic tools is driven by the labor-intensive, time-consuming, and error-prone nature of traditional illness detection methods, which frequently rely on visual inspection and expert knowledge. In recent years, deep learning methodologies have emerged as a transformative force in plant disease detection, exhibiting remarkable capabilities in image recognition and classification, surpassing the limitations of conventional machine learning techniques that necessitate manual feature extraction. Deep learning models, highlight key trends, including the increasing use of convolutional neural networks, transfer learning techniques, data augmentation methods, and real- time disease detection using mobile applications. The paper also identifies several gaps in the current research, such as limited diversity in maize disease datasets, insufficient focus on early-stage detection, lack of standardized evaluation metrics, and inadequate consideration of environmental factors. These models have demonstrated proficiency in learning intricate features directly from raw image data, enabling accurate and rapid identification of diseases in maize crops. The paper further outlines potential future directions for research, including the development of more comprehensive datasets, exploration of multi-modal deep learning approaches, investigation of explainable AI techniques, integration with IoT devices, adaptation of models for different maize varieties and growing conditions, incorporation of temporal data, and development of hybrid models. To address the identified gaps, the paper suggests collaborating with agricultural experts, developing models for multiple disease detection, and investigating unsupervised and semi- supervised learning approaches.