Leveraging EfficientNet-B4 for Accurate Classification of Nitrogen Deficiency in Rice Crops

One of the main issues lowering rice crop output and, if unchecked, can cause significant yield losses is a lack of nitrogen. The pillars of conventional methods for spotting nitrogen shortfall are manual observation and laboratory testing, labour-intensive and prone to human error. To solve these issues, this work presents a fresh approach for consistently diagnosing nitrogen shortfall in rice crops by using the EfficientNet-B4 deep learning model. Using EfficientNet-B4, a model noted for its innovative performance in image classification tasks, images of rice fields with varying nitrogen shortage were examined. The dataset utilised to train and assess the system consisted in thousands of annotated images, guaranteeing consistent performance over a spectrum of growth phases and environmental conditions. Using measures including accuracy, F1-score, and confusion matrix to evaluate the classification performance helped the model to be more generalisable by means of data augmentation strategies. The current work shows the possibilities of deep learning models, including EfficientNet-B4, to change crop health monitoring and provides the foundation for further research on the application of innovative machine learning approaches in agriculture. By giving farmers and agronomists a scalable and reasonably priced choice, the recommended strategy promotes data-driven decision-making and enhances food security.