Design of an Improved Model for Nutrient Deficiency Detection in Plants Using CNNs, Autoencoders, and Precision-Aware Learning

This paper presents a new machine learning-based approach for nutrient deficiency detection through literature shortcomings in advanced feature extractions and classification models. In the proposed framework, Convolutional Neural Networks are used for high-dimensional feature extraction from images and samples of plants. Only in that case will such architectures of CNNs, through their convolutional operations, pooling layers, and feature maps, be able to capture relevant indicators of color anomalies, texture, shape, or structure. Pre-trained models have been fine-tuned for VGG16, ResNet, and Inception in plant-specific data for better accuracy in feature identification. This work is based on auto-encoders, a type of unsupervised learning for feature extraction from images of plants; through encoder-decoder architecture with a bottleneck layer, only important features are maintained. Principal Component Analysis further reduces the dimensionality of the feature space by isolating key components that contribute to the data variance through eigenvectors and eigenvalues in real-time use case scenarios. Classifications are done using Support Vector Machines against high accuracy and robustness using Kernel functions like Radial Basis Function in the transformation of the feature space. Random Forests add to the feature importance and avoid overfitting by combining many self-same decision trees. It uses GBMs because of their sequential error-correcting nature, and the quality of the predictions is improved due to boosting iterations across different scenarios. Cost-sensitive learning with focal loss and other precision-aware methods are implemented to handle class imbalance and increase model sensitivity to critical misclassifications. This is an integral methodology for raising the correctness and efficiency of nutrient deficiency detection and providing a scalable and robust solution for modern agricultural practices.