# Transfer Learning in Multimodal Sunflower Drought Stress Detection
## 4 Conclusions  
The early detection of drought stress in sunflowers is important because it can increase crop yield, which leads to higher food production while preserving water resources. Furthermore, it allows systematical identification of inbred lines with lower drought tolerance under controlled conditions, which can provide detailed insights into the physiological and morphological responses of these lines to drought stress. Therefore, it is important to develop technologies that could automate drought detection, and AI has significant potential to impact the field. In this study, we applied five different CNN TL models to detect drought stress in sunflower images collected from two different sources. MobileNet with targeted data augmentation on grayscale images provided the most promising results, with an accuracy and F1 score of 0.95 for the test data, along with precision and recall values of 1.00 and 0.90, respectively.  
The exploration of TL CNN models within our novel pipeline for sunflower drought stress detection has shown promising results. While our study concentrated on using learning models on a small dataset, we believe that a larger and more varied dataset will eventually allow the development of models with even greater accuracy. In addition, it would be interesting to use Generative Adversarial Networks (GANs) for synthetic data generation to enrich and enlarge the dataset. The use of GANs can lead to increased and balanced datasets, further improving the generalizations of the model. Moreover, in future developments, our model's interpretability can be improved using a technique called Gradient-weighted Class Activation Mapping (Grad-CAM), which shows the areas in sunflower images that influence the model's predictions. Using this method, agricultural specialists might identify precisely which sunflower parts, such as certain segments of the root or shoot, are important indicators of drought stress. Future advancements may also include remote sensing technology, including field cameras that monitor plant growth and send data to our model in real time, allowing for rapid cultivation responses and immediate decision making related to the water needs of the crops in the field. The use of cutting-edge computer techniques, like DL, will surely be crucial to future studies and applications as agriculture continues to evolve.  
Conceptualization, O.L. and S.C.; methodology, O.L.; software, O.L.; validation, O.L., D.M. and S.C.; formal analysis, O.L.; investigation, O.L. and S.C.; resources, O.L., B.D., S.C., S.J. and D.M.; data curation, O.L. and B.D.; writing--original draft preparation, O.L.; writing--review and editing, O.L., S.C., S.J., B.D. and A.K.; visualization, O.L.; supervision, O.L. and S.C.; project administration, O.L. and S.C.; funding acquisition, O.L., S.C., S.J., D.M., B.D. and A.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Fund of the Republic of Serbia through the IDEAS project \"Creating climate smart sunflower for future challenges\" (SMARTSUN), grant number 7732457. This research was supported by the Ministry of Science, Technological Development, and Innovation (Contract No. 451-03-65/2024-03/200156) and the Faculty of Technical Sciences, University of Novi Sad, by the \"Scientific and Artistic Research Work of Researchers in Teaching and Associate Positions at the Faculty of Technical Sciences, University of Novi Sad\" project (No. 01-3394/1). S.C., B.D., S.J., and D.M. were also supported by the Centre of Excellence for Innovations in Breeding of Climate-Resilient Crops--Climate Crops of the Institute of Field and Vegetable Crops.  
Not applicable.  
The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the corresponding author.  
The authors declare no conflicts of interest.