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The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R2) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R2, RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables. [ABSTRACT FROM AUTHOR]

Detecting litchis in a complex natural environment is important for yield estimation and provides reliable support to litchi-picking robots. This paper proposes an improved litchi detection model named YOLOv5-litchi for litchi detection in complex natural environments. First, we add a convolutional block attention module to each C3 module in the backbone of the network to enhance the ability of the network to extract important feature information. Second, we add a small-object detection layer to enable the model to locate smaller targets and enhance the detection performance of small targets. Third, the Mosaic-9 data augmentation in the network increases the diversity of datasets. Then, we accelerate the regression convergence process of the prediction box by replacing the target detection regression loss function with CIoU. Finally, we add weighted-boxes fusion to bring the prediction boxes closer to the target and reduce the missed detection. An experiment is carried out to verify the effectiveness of the improvement. The results of the study show that the mAP and recall of the YOLOv5-litchi model were improved by 12.9% and 15%, respectively, in comparison with those of the unimproved YOLOv5 network. The inference speed of the YOLOv5-litchi model to detect each picture is 25 ms, which is much better than that of Faster-RCNN and YOLOv4. Compared with the unimproved YOLOv5 network, the mAP of the YOLOv5-litchi model increased by 17.4% in the large visual scenes. The performance of the YOLOv5-litchi model for litchi detection is the best in five models. Therefore, YOLOv5-litchi achieved a good balance between speed, model size, and accuracy, which can meet the needs of litchi detection in agriculture and provides technical support for the yield estimation and litchi-picking robots. [ABSTRACT FROM AUTHOR]