Enhancing Rice Crop Resilience: Leveraging Image Processing Techniques in Deep Learning Models to Predict Salinity Stress of Rice during the Seedling Stage

One of the most significant staple crops in the world is rice. Rice seedlings are particularly susceptible to salt stress during the seedling stage, which can negatively affect crop quality and yield. Traditional approaches for assessing the susceptibility of rice crops to salt stress during the seedling stage are deemed inadequate and time consuming. The study emphasizes the necessity of employing a deep learning model instead of traditional methods to identify and classify salinity stress in rice seedlings using field images. To predict salinity stress in rice crops, this research examines the significance of image processing methods employed in deep learning models. To enhance the clarity and visual representation of salinity-induced stress symptoms, we explore several image enhancement techniques, such as noise reduction, contrast augmentation, and image normalization. To further capture and quantify the distinct visual features related to salinity stress, feature extraction techniques such as texture analysis, shape analysis, and color-based segmentation are used. We employ a deep learning model such as VGG16 and VGG19 models to use these extracted features as input to effectively classify the severity of salinity stress in rice seedlings as 1,3,5,7,9 scores. A comprehensive set of rice seedling images from field taken under various salinity stress conditions is used to assess the suggested method. The effectiveness of image processing techniques in improving the discriminatory power of deep learning models for salinity stress prediction is demonstrated by experimental results with 99.40%. The combination of image enhancement and feature extraction methods significantly improves the overall accuracy and reliability of the predictions, enabling farmers to make informed decisions regarding crop management and potential interventions to mitigate salinity stress.