# Transfer Learning in Multimodal Sunflower Drought Stress Detection
###### Abstract  
Efficient water supply and timely detection of drought stress in crops to increase yields is an important task considering that agriculture is the primary consumer of water globally. This is particularly significant for plants such as sunflowers, which are an important source of quality edible oils, essential for human nutrition. Traditional detection methods are labor-intensive, time-consuming, and rely on advanced sensor technologies. We introduce an innovative approach based on neural networks and transfer learning for drought stress detection using a novel dataset including 209 non-invasive rhizotron images and 385 images of manually cleaned sections of sunflowers, subjected to normal watering or water stress. We used five neural network models: VGG16, VGG19, InceptionV3, DenseNet, and MobileNet, pre-trained on the ImageNet dataset, whose performance was compared to select the most efficient architecture. Accordingly, the most efficient model, MobileNet, was further refined using different data augmentation mechanisms. The introduction of targeted data augmentation and the use of grayscale images proved to be effective, demonstrating improved results, with an F1 score and an accuracy of 0.95. This approach encourages advances in water stress detection, highlighting the value of artificial intelligence in improving crop health monitoring and management for more resilient agricultural practices. artificial intelligence; convolutional neural networks; data augmentation; drought stress detection; image classification; sunflower; transfer learning +
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In addition, the integration of ML with image processing techniques represents a leap forward from standard practices, enabling rapid and non-invasive assessment of water stress [14]. ML methods proved to be reliable for the classification of drought stress in plants using leaf reflectance spectra, which highlights the potential of spectral analysis combined with advanced computational techniques [15]. Moreover, deep learning (DL), through the use of convolutional neural networks (CNNs), has revolutionized image classification in agriculture. Also, DL, in combination with transfer learning (TL), has emerged as a promising tool for automating the process of early water stress detection in various crops [16]. For example, the identification and classification of drought stress in maize were performed using deep CNNs [17]. The power of TL in agriculture is reflected in the adaptability of pre-trained networks to new tasks [18]. These studies not only showed high accuracy in predictions but also proved to be non-invasive, economical, and scalable solutions compared to traditional stress detection methods. Although DL can have a great positive impact on agricultural problems, such as timely drought detection, various challenges with agricultural datasets can compromise the effectiveness of the results. DL models need very large datasets to achieve good results [19; 20]. Since the size of datasets in real-world scenarios is usually limited, it is common practice to use lightweight CNN architectures for image classification. These models have demonstrated a promising balance between high classification accuracy and computational efficiency in many fields [21]. Furthermore, using TL can improve model accuracy without requiring more data [18]. However, agricultural images are domain-specific, and the pre-trained models used in TL are not trained on such images but on general image datasets for computer vision. Another problem can be the class imbalance, which can also degrade the performance of DL models, including in agricultural applications. This can be seen in the paper, which represents an application of transfer learning through the ResNet50 architecture to detect two common apple diseases [22]. The obtained accuracy was 97%, with an accuracy of only 51% for mixed diseases, which was caused by class imbalance [22].