It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

Accurate and real-time monitoring of root soil water content (RSWC) is key in optimizing field irrigation decisions and enhancing crop water productivity. However, relying only on the vegetation index as the input to the inversion model may result in lower inversion accuracy due to the canopy spectral saturation effect. To break through the limitation of a single data source, this study constructed an integrated network model (ATT-LSTM) incorporating the attention mechanism based on the long and short-term memory network (LSTM) to enhance the inversion performance by integrating heterogeneous data from multiple sources. The experiment used canopy spectral data based on UAV remote sensing and weather station monitoring data as input features. A control group was set up for cross-validation to realize the accurate inversion of RSWC in kiwifruit plants. The results show that the coefficient of determination (R²) of the ATT-LSTM model on the test set reaches 0.868. This study confirms that the multi-source data fusion framework effectively overcomes vegetation index saturation, improves rhizosphere moisture monitoring accuracy, supports precision irrigation decisions in kiwifruit orchards, and provides a reference for smart agriculture water management optimization.