Abstract:
Tomato cultivation represents a critical component of nutrition, economic development, and
public health, yet it is increasingly compromised by foliar diseases that diminish yield and intensify
dependency on hazardous agrochemicals. Although deep learning models have demonstrated strong
capabilities for automated disease recognition, existing benchmark datasets exhibit limited real-world
utility, primarily due to the absence of in-field imagery and precise annotations of diseased regions. A
novel attention mechanism, Inverted Residual Convolutional Block Attention Module (IR-CBAM), is
proposed, combining inverted residual blocks with the CBAM Module, and is specifically tailored to
address challenges posed by in-field image variability, such as complex backgrounds and inconsistent
lighting. Furthermore, this study introduces SLIF-Tomato, the Sri Lankan In-Field Tomato leaf
disease dataset, which is the first complete in-field dataset comprising class labels and bounding box
annotations collected under diverse real-world conditions. The proposed approach achieved 99.66%
and 99.91% accuracy rates on two curated versions of the SLIF-Tomato dataset. Subsequently, the
YOLOv12-large model is employed to detect diseased regions, which obtained an average precision
score of 88.5%. These contributions advance the development of accurate, efficient and field-
adaptable diagnostic systems for tomato leaf disease management in precision agriculture.
