KMeans Kernel-Learning Based AI-IoT Framework for Plant Leaf Disease Detection

Development of IoT based solutions in agriculture is changing the sector with Smart Agriculture. Plant Leaf Disease Detection (PLDD) using ICT is one of the most active and challenging research areas because of its potential in the food security topic. Some of current solutions based on AI/Machine learning techniques (E.g. KNN, CNN) are very efficient. However, deploying them in the context of Africa will be challenging knowing that computation resources, connectivity to data centers, and electrical power supply won’t be guaranteed. In this paper we propose an AI-IoT Framework based on KMeans Kernel Learning to build Artificial Intelligence services on Core Network and deploy it to Edge AI-IoT Network. AI-Service Segment selects leaves images that have representative characteristics of diseased leaves (Kernel-Images), uses KMeans machine learning algorithm to build clusters of Kernel-Images so that diseased regions are contained cluster. We call the resulting models KMeans Kernel Models. Main outcome of our proposal is designing a low-computation and economic Edge AI-AoT Framework as efficient as sophisticated methods. We have evaluated that our system is efficient and provides a very good result with a rate of 96% accuracy with a low number of training images. Our proposed framework reduces the need for large training datasets to be efficient (in comparison to KNN/SVM and CNN) and learned models are embeddable in IoT devices near the plants.