Talk · IEEE International Conference on Smart Information Systems and Technologies · May 13, 2026

Domain Adaptation Techniques for Plant Disease Classification: Bridging the Gap between Laboratory and Field Conditions

Nurshapagat Shapay, Nurzhamal Kashkimbayeva, Dinara Kaibassova

Abstract

When deep learning models based on carefully selected laboratory datasets are applied to real-world field images, their accuracy tends to decrease. In our case, the model, which reached 98.9% for PlantVillage, dropped to just 41.9% for field-collected PlantDoc images, a gap that severely limits any practical use of such systems in agriculture. To solve this problem, we studied and compared three strategies for adapting to the subject area: self-study with pseudo-labels, a progressive multi-stage curriculum, and a collaborative learning scheme with a balanced sample by class. Progressive learning, despite its intuitive appeal, suffered greatly from catastrophic forgetting and peaked at 66.5%. On the other hand, due to joint training, the accuracy of measurements in the field increased to 79.1%, which is 37.2 percentage points higher than with the unadapted baseline. Looking at individual diseases, we found that visually different diseases such as powdery mildew of squash (95.9%) and late blight of tomatoes (83.2%) brought the greatest benefits, while it turned out to be more difficult to adapt to more inconspicuous diseases. Adding an increase in testing time gave an additional 0.6% increase. Our findings indicate that training on all classes at once, rather than in stages, sidesteps the forgetting problem and makes field deployment of plant disease classifiers considerably more feasible.

Smart Agriculture and AIDomain Adaptation and Few-Shot LearningAdvanced Neural Network Applicationsdomain adaptationplant disease classificationfield photographs

Domain Adaptation Techniques for Plant Disease Classification: Bridging the Gap between Laboratory and Field Conditions

Abstract

More by Nurzhamal Kashkimbayeva