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To optimize rice yield and enhance quality through targeted field management at each growth stage, rapid and accurate identification of rice growth stages is crucial. This study presents the Mobilenetv3-YOLOv8 rice growth-stage recognition model, designed for high efficiency and accuracy using Unmanned Aerial Vehicle (UAV) imagery. A UAV captured images of rice fields across five distinct growth stages from two altitudes (3 m and 20 m) across two independent field experiments. These images were processed to create training, validation, and test datasets for model development. Mobilenetv3 was introduced to replace the standard YOLOv8 backbone, providing robust small-scale feature extraction through multi-scale feature fusion. Additionally, the Coordinate Attention (CA) mechanism was integrated into YOLOv8’s backbone, outperforming the Convolutional Block Attention Module (CBAM) by enhancing position-sensitive information capture and focusing on crucial pixel areas. Compared to the original YOLOv8, the enhanced Mobilenetv3-YOLOv8 model improved rice growth-stage identification accuracy and reduced the computational load. With an input image size of 400 × 400 pixels and the CA implemented in the second and third backbone layers, the model achieved its best performance, reaching 84.00% mAP and 84.08% recall. The optimized model achieved parameters and Giga Floating Point Operations (GFLOPs) of 6.60M and 0.9, respectively, with precision values for tillering, jointing, booting, heading, and filling stages of 94.88%, 93.36%, 67.85%, 78.31%, and 85.46%, respectively. The experimental results revealed that the optimal Mobilenetv3-YOLOv8 shows excellent performance and has potential for deployment in edge computing devices and practical applications for in-field rice growth-stage recognition in the future.

The rice spike, a crucial part of rice plants, plays a vital role in yield estimation, pest detection, and growth stage management in rice cultivation. When using drones to capture photos of rice fields, the high shooting angle and wide coverage area can cause rice spikes to appear small in the captured images and can cause angular distortion of objects at the edges of images, resulting in significant occlusions and dense arrangements of rice spikes. These factors are unique challenges during drone image acquisition that may affect the accuracy of rice spike detection. This study proposes a rice spike detection method that combines deep learning algorithms with drone perspectives. Initially, based on an enhanced version of YOLOv5, the EMA (efficient multiscale attention) attention mechanism is introduced, a novel neck network structure is designed, and SIoU (SCYLLA intersection over union) is integrated. Experimental results demonstrate that RICE-YOLO achieves a mAP@0.5 of 94.8% and a recall of 87.6% on the rice spike dataset. During different growth stages, it attains an AP@0.5 of 96.1% and a recall rate of 93.1% during the heading stage, and a AP@0.5 of 86.2% with a recall rate of 82.6% during the filling stage. Overall, the results indicate that the proposed method enables real-time, efficient, and accurate detection and counting of rice spikes in field environments, offering a theoretical foundation and technical support for real-time and efficient spike detection in the management of rice growth processes.

Leaf chlorophyll content is crucial for monitoring plant growth and photosynthetic capacity. The Soil and Plant Analysis Development (SPAD) values are widely utilized as a relative chlorophyll content index in ecological agricultural surveys and vegetation remote sensing applications. Multi-spectral cameras are a cost-effective alternative to hyperspectral cameras for agricultural monitoring. However, the limited spectral bands of multi-spectral cameras restrict the number of vegetation indices (VIs) that can be synthesized, necessitating the exploration of other options for SPAD estimation. This study evaluated the impact of using texture indices (TIs) and VIs, alone or in combination, for estimating rice SPAD values during different growth stages. A multi-spectral camera was attached to an unmanned aerial vehicle (UAV) to collect remote sensing images of the rice canopy, with manual SPAD measurements taken immediately after each flight. Random forest (RF) was employed as the regression method, and evaluation metrics included coefficient of determination (R2) and root mean squared error (RMSE). The study found that textural information extracted from multi-spectral images could effectively assess the SPAD values of rice. Constructing TIs by combining two textural feature values (TFVs) further improved the correlation of textural information with SPAD. Utilizing both VIs and TIs demonstrated superior performance throughout all growth stages. The model works well in estimating the rice SPAD in an independent experiment in 2022, proving that the model has good generalization ability. The results suggest that incorporating both spectral and textural data can enhance the precision of rice SPAD estimation throughout all growth stages, compared to using spectral data alone. These findings are of significant importance in the fields of precision agriculture and environmental protection.