Development of Smart Agriculture to detect the Arabica Coffee Leaf Disease using IAFSA based MSAB with Channel and Spatial Attention Network

Abstract

Plant diseases provide challenges for the agriculture sector, notably to produce Arabica coffee. Recognising issues on Arabica coffee leaves is a first step in avoiding and curing illnesses to prevent crop loss. With the extraordinary advancements achieved in convolutional neural networks (CNN) in recent years, Arabica coffee leaf damage can now be identified without the aid of a specialist. However, the local characteristics that convolutional layers in CNNs record are typically redundant and unable to make efficient use of global data to support the prediction process. The proposed Hybrid Attention UNet, also known as CMSAMB-UNet due to its feature extraction and global modelling capabilities, integrates both the Channel and Spatial Attention Module (CSAM) as well as the Multi-head Self-Attention Block (MSAB). In this study, CMSAMB-UNet is built on Resnet50 to extract multi-level features from plant picture data. Two shallow layers of feature maps are used with CSAM according to local attention. used throughout the feature extraction process to enrich the features and adaptively disregard unwanted features. In order to recreate the spatial feature connection of the input pictures using high-resolution feature maps, two global attention maps produced by MSAB are combined.