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p-Index From 2021 - 2026
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All Journal Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC)
Raymond Wafula Ongus
Mount Kenya University
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

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Multimodal Machine Learning for Maize Disease Detection: A Systematic Review of Architectures and Deployment Challenges Mercy Chepkoech Tonui; John Wachira Kamau; Raymond Wafula Ongus
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 2 (2026): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i2.3942

Abstract

Maize diseases continue to threaten agricultural productivity and food security, particularly in developing regions where early diagnosis remains constrained by limited expert access. While deep learning has enabled automated disease detection systems, most existing approaches rely on unimodal image datasets and cloud-dependent architectures, limiting robustness and deployment feasibility in resource-constrained environments. This study presents a structured systematic review of 38 peer-reviewed studies published between 2020 and 2025, focusing on multimodal machine learning approaches integrating visual and environmental data for maize disease detection. Quantitative synthesis reveals that 58% of reviewed studies employ image-only deep learning models, 26% implement multimodal frameworks, and only 29% conduct validation under real or semi-real field conditions. Furthermore, 32% explicitly address deployment considerations, including edge computing and mobile inference. The findings demonstrate that multimodal architectures improve robustness and contextual modeling compared to unimodal systems by integrating phenotypic and environmental drivers of disease expression. However, increased computational complexity, synchronization challenges, and limited edge optimization remain barriers to scalable deployment. This review advances scientific knowledge by providing a computing-centered synthesis of multimodal architectures, fusion strategies, deployment constraints, and explainability gaps, identifying key research priorities in edge efficiency, real-world validation, and interpretable intelligent systems.
Multimodal CNN–LSTM Framework for Real-Time Maize Disease Detection Mercy Chepkoech Tonui; John Kamau; Raymond Wafula Ongus
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 2 (2026): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i2.3970

Abstract

Maize diseases present a major challenge to agricultural productivity and food security, particularly in low-resource settings in sub-Saharan Africa. Timely detection plays an important role in reducing yield losses and enabling effective farm management. This research introduces and validates a multimodal machine learning–based system for real-time maize disease detection in Bomet County, Kenya. The system integrates maize leaf image data, environmental sensor data, and farmer-reported observations to develop a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to automatically identify and categorize maize diseases. A mixed-methods research design was adopted, combining machine learning experiments with surveys and interviews involving farmers and agricultural officers. The findings revealed that Maize Lethal Necrosis (MLN) was the most prevalent disease (41%), followed by Gray Leaf Spot (33%) and Northern Leaf Blight (26%). Environmental variables such as humidity and temperature demonstrated strong associations with disease occurrence. The proposed multimodal CNN–LSTM framework integrates maize leaf images, environmental sensor data, and farmer observations, achieving an accuracy of 94.2%, which outperforms conventional image-only CNN models (87.5%) and environmental-data-based LSTM models (81.3%). Additionally, 78% of farmers reported faster disease diagnosis using the developed system. The findings demonstrate that the proposed system supports real-time maize disease detection through an edge-enabled architecture, enabling deployment on mobile devices and facilitating practical intelligent system integration in agricultural environments.
Co-Authors John Kamau John Wachira Kamau Mercy Chepkoech Tonui