Edge - AI for Transforming Plant Disease Identification and Monitoring with Major Focus On Cereal Crops - An Overview

Edge AI Applications in Cereal Crop Disease Diagnostics

Abstract

Plant diseases represent a persistent threat to global agricultural productivity, with annual crop losses of 20–40% threatening food security and farmer livelihoods worldwide. Particularly in the early stages of infection, when intervention is most effective, traditional visual inspection techniques continue to be subjective, laborious, and unreliable. Although automated, image-based disease categorization has been made possible by recent developments in artificial intelligence (AI), machine learning (ML), and deep learning (DL), the majority of solutions rely on cloud infrastructure, which is a significant drawback in rural agricultural areas with poor connectivity. A viable substitute is Edge AI, which applies AI inference directly to nearby devices (such as smartphones, drones, and microcontrollers) and provides real-time analysis, lower latency, improved data privacy, and offline capabilities. Significant obstacles still exist, nevertheless, in the areas of computing effectiveness, model generalization, and real-world application across numerous agricultural contexts. The current level of Edge AI for plant disease detection is critically examined in this article, with a focus on cereal crops, which are essential to the world’s food security. In order to facilitate deployment on devices with limited resources, we thoroughly examine the Edge AI frameworks (TensorFlow Lite, OpenVINO, ONNX Runtime, Edge Impulse), hardware architectures (Google Edge TPU, NVIDIA Jetson, Intel Movidius, Raspberry Pi), and optimization strategies (quantization, pruning, knowledge distillation). We find important gaps through a thorough analysis of applications specific to cereals, including a lack of standardized evaluation procedures, dataset homogeneity that jeopardizes model robustness, limited field validation under variable environmental conditions, and inadequate integration of temporal and environmental data. Although Edge AI shows technological viability, lightweight systems can achieve >90% accuracy on controlled datasets, it is still difficult to translate to diverse field circumstances. To achieve the potential of Edge AI in precision agriculture, we conclude that: (1) domain-adaptive models trained on a variety of multi-location field datasets must be developed; (2) sensor systems and evaluation benchmarks must be standardized; (3) multimodal sensing (spectral, temporal, and environmental) must be integrated to improve diagnostic reliability; (4) few-shot learning and transfer learning must be explored to reduce data requirements; and (5) emerging technologies, such as large language models (LLMs) for farmer-accessible interfaces and federated learning for privacy-preserving model improvement, must be investigated. The detection of Fusarium head blight in wheat, blast surveillance in rice, and rust disease monitoring systems are priority topics for cereal crops in particular. These diseases have a significant economic effect and intricate symptomologies that require advanced edge-based solutions.