Sustainable Edge AI for Precision Agriculture: A Lightweight CNN Model for Aloe Vera Leaf Disease Diagnosis

ABSTRACT With increasing focus on sustainable agriculture and AI‐enabled solutions, this work proposes AloeVeraNet, a compact deep learning model designed for the efficient and real‐time detection of aloe vera leaf diseases on edge devices. The model employs depthwise and pointwise convolutions to achieve a significantly reduced parameter count (289 K) and model size (1.10 MB), enabling deployment in low‐resource environments. With 96.09% accuracy, AloeVeraNet sets a new benchmark in classifying aloe vera leaf conditions: healthy, rust‐infected and spot‐affected, outperforming MobileNetV2, EfficientNetV2‐S and VGG16. This sustainable, artificial intelligence (AI)‐based solution supports precision agriculture through optimised computation, energy efficiency and local disease monitoring, all without relying on cloud infrastructure, thereby contributing to environmentally responsible farming practices. This study demonstrates the value of integrating AI with sustainable edge computing in creating resilient and inclusive solutions for the agricultural sector.