One of the most serious threats to global food security is plant diseases
compromising agricultural productivity and threatening the livelihoods of millions. These
diseases can decimate crops, disrupt food supply chains, and escalate the risk of food shortages,
underscoring the urgency of implementing robust strategies to safeguard the world’s food
sources. Deep learning methods have revolutionized the field of plant disease detection,
offering advanced and accurate solutions for early identification and management. However, a
recurring problem in deep learning models is their susceptibility to a lack of robustness and
generalization when facing novel crop and disease types that were not included in the training
dataset. In this paper, we address this issue by proposing a novel deep learning-based system
capable of recognizing diseased and healthy leaves across different crops, even if the system
was not trained on them. The key idea is to focus on recognizing the diseased small leaf regions
rather than the overall appearance of the diseased leaf, along with determining the disease’s
prevalence rate on the entire leaf. For efficient classification and to leverage the excellence of
the Inception model in disease recognition, we employ a small Inception model architecture,
which is suitable for processing small regions without compromising performance. To confirm
the effectiveness of our method, we trained and tested it using the widely acclaimed Plant
Village dataset, recognized as the most utilized dataset for its comprehensive and diverse
coverage. Our method achieved an accuracy rate of 94.04%. Furthermore, when tested on new
datasets, it achieved an accuracy rate of 97.13%.