Customised Convolutional Neural Network With Transfer Learning for Multi-Nutrient Deficiency Identification With Pattern and Deep Features in Paddy Image

Abstract

Multi-nutrient deficiency in crops, involving a shortage of essential nutrients such as nitrogen, phosphorus and potassium, impacts plant growing and yield. Accurate recognition is vital for effective nutrient management and maximising productivity. Identification techniques include extractive methods that analyse symptoms and abstractive methods that generate insights from data, with hybrid approaches aiming to improve the accuracy. However, challenges remain in maintaining diagnostic consistency and so forth. Continuous improvements are necessary to better integrate and interpret complex data for more accurate nutrient deficiency identification. To tackle these challenges, this research proposes the customised convolutional neural network-transfer learning (CCNN-TL) model for identifying multi-nutrient deficiencies in paddy leaves. This model includes several key phases: image preprocessing, segmentation, feature extraction, data augmentation and identification. Initially, the paddy leaf images undergo preprocessing using the improved Wiener filtering (IWF) technique. Next, the modified U-Net model is proposed for segmenting the preprocessed images. In the feature extraction phase, relevant features are identified from the segmented images. These features are then augmented through the data augmentation process. Finally, the CCNN-TL model is used for multi-nutrient deficiency identification. The model's effectiveness is demonstrated through comprehensive simulations and experimental evaluations. These evaluations showcase its enhanced performance, with improved accuracy, precision and specificity compared to traditional methods. The CCNN-TL scheme attained the greatest accuracy of 0.982, precision of 0.975 and F-measure of 0.973. The Nutrient-Deficiency-Symptoms-in-Rice dataset was employed for simulations and analysis, ensuring a solid foundation for the evaluations.