When it comes to growing lettuce, specific nutrients play vital roles in its growth and development. These essential nutrients include full nutrients (FN), nitrogen (N), phosphorus (P), and potassium (K). Insufficient or excess levels of these nutrients can have negative effects on lettuce plants, resulting in various deficiencies that can be observed in the leaves. To better understand and identify these deficiencies, a deep learning approach is employed to improve these tasks. For this study, YOLOv8 Nano, a lightweight deep network, is chosen to classify the observed deficiencies in lettuce leaves. Several enhancements to the baseline algorithm are made, the backbone is replaced with VGG16 to improve the classification accuracy, and depthwise convolution is incorporated into it to enrich the features while keeping the head unchanged. The proposed network, incorporating these modifications, achieved superior classification results with a top-1 accuracy of 99%. This method outperformed other state-of-the-art classification methods, demonstrating the effectiveness of the approach in identifying lettuce deficiencies. The objective of this research was to improve the baseline algorithm to complete the classification task with a top-1 accuracy above 85%, a FLOP inferior to 10G, and classification latency below 170 ms per image.

Plant and fruit diseases significantly impact agricultural economies by diminishing crop quality and yield. Developing precise, automated detection techniques is crucial to minimize losses and drive economic growth. We introduce YOLO-AppleScab, integrating Content-Aware ReAssembly of FEature ($\mathbb{C}\mathbb{A}\mathbb{R}\mathbb{A}\mathbb{F}\mathbb{E}$) architecture into YOLOv7 for enhanced apple fruit detection and disease classification. The model achieves impressive metrics: F1, recall, and precision of 89.75%, 85.20%, and 94.80%, and a mean average precision of 89.30% at $IoU$ 0.5. With 64% efficiency, this model’s integration with YOLOv7 improves detection, promising economic benefits by accurately detecting apple scab disease and reducing agricultural damage.