To explore the feasibility of using UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies, we employed UAV-LiDAR to scan sugarcane canopies during the tillering and elongation stages, acquiring canopy point cloud data. Subsequently, features such as average row height, projected row area, point cloud density at different canopy layers, and the ratios between these parameters were extracted. Three feature selection methods-partial least squares regression (PLSR), XGBoost feature importance (XGBoost-FI), and random forest-recursive feature elimination (RF-RFE)-were adopted to evaluate and identify the optimal input variables for modeling. With these selected variables, LAI inversion models were developed based on random forest (RF) and adaptive boosting (AdaBoost) algorithms, and their performance was assessed. Among the extracted features, the projected row area S_p and the total row point count C_total exhibited strong correlations with LAI, with correlation coefficients of 0.73 and 0.72, respectively. The AdaBoost-based LAI inversion model, using the projected row area S_p, average height H_avg, mid-layer point cloud density C_m, and total row point count C_total as input variables, achieved the best performance, with a coefficient of determination (R_v²) of 0.713 and a root mean square error (RMSE_v) of 0.25 on the validation set. This study provides an effective method for high-throughput acquisition of LAI in field crops, offering valuable scientific support for sugarcane field management and breeding efforts.
Plant Leaves/growth & development* ; Saccharum/growth & development* ; Algorithms ; Unmanned Aerial Devices ; Remote Sensing Technology/methods* ; Crops, Agricultural/growth & development*

Accurately obtaining the crop quantity and density is not only crucial for the demand-based input of water and fertilizer in the field but also vital for ensuring the yield and quality of crops. Aerial photography by unmanned aerial vehicles (UAVs) can quickly acquire the distribution image information of crops over a large area. However, the accurate recognition of a single type of dense targets is a huge challenge for most recognition algorithms. Taking banana seedlings as an example in this study, we captured the images of banana plantations by UAVs from high altitudes to explore an efficient recognition method for dense targets. We proposed a strategy of "cut-recognition-stitch" and constructed a counting method based on the improved Faster R-CNN algorithm. First, the images containing highly dense targets were cropped into a large number of image tiles according to different sizes (simulating different flight altitudes), and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was adopted to improve the image quality. A banana seedling dataset containing 36 000 image tiles was constructed. Then, the Faster R-CNN network with optimized parameters was used to train the banana seedling recognition model. Finally, the recognition results were reversely stitched together, and a boundary deduplication algorithm was designed to correct the final counting results to reduce the repeated recognition caused by image cropping. The results show that the recognition accuracy of the Faster R-CNN with optimized parameters for banana image datasets of different sizes can reach up to 0.99 at most. The deduplication algorithm can reduce the average counting error for the original aerial images from 1.60% to 0.60%, and the average counting accuracy of banana seedlings reaches 99.4%. The proposed method effectively addresses the challenge of recognizing dense small objects in high-resolution aerial images, providing an efficient and reliable technical solution for intelligent crop density monitoring in precision agriculture.
Musa/growth & development* ; Crops, Agricultural/growth & development* ; Algorithms ; Neural Networks, Computer ; Unmanned Aerial Devices ; Seedlings/growth & development* ; Image Processing, Computer-Assisted/methods* ; Photography ; Agriculture/methods*

OBJECTIVE: To establish a snail control approach for spraying chemicals with drones against Oncomelania hupensis in complex snail habitats in hilly regions, and to evaluate its molluscicidal effect. METHODS: The protocol for evaluating the activity of spraying chemical molluscicides with drones against O. hupensis snails was formulated based on expert consultation and literature review. In August 2022, a pretest was conducted in a hillside field environment (12 000 m²) north of Dafengji Village, Dacang Township, Weishan County, Yunnan Province, which was assigned into four groups, of no less than 3 000 m² in each group. In Group A, environmental cleaning was not conducted and 5% niclosamide ethanolamine salt granules were sprayed with drones at a dose of 40 g/m², and in Group B, environmental cleaning was performed, followed by 5% niclosamide ethanolamine salt granules sprayed with drones at a dose of 40 g/m², while in Group C, environmental cleaning was not conducted and 5% niclosamide ethanolamine salt granules were sprayed with knapsack sprayers at a dose of 40 g/m², and in Group D, environmental cleaning was performed, followed by 5% niclosamide ethanolamine salt granules sprayed with knapsack sprayers at a dose of 40 g/m². Then, each group was equally divided into six sections according to land area, with Section 1 for baseline surveys and sections 2 to 6 for snail surveys after chemical treatment. Snail surveys were conducted prior to chemical treatment and 1, 3, 5, 7 days post-treatment, and the mortality and corrected mortality of snails, density of living snails and costs of molluscicidal treatment were calculated in each group. RESULTS: The mortality and corrected mortality of snails were 69.49%, 69.09%, 53.57% and 83.48%, and 68.58%, 68.17%, 52.19% and 82.99% in groups A, B, C and D 14 days post-treatment, and the density of living snails reduced by 58.40%, 63.94%, 68.91% and 83.25% 14 days post-treatment relative to pre-treatment in four groups, respectively. The median concentrations of chemical molluscicides were 37.08, 35.42, 42.50 g/m² and 56.25 g/m² in groups A, B, C and D, and the gross costs of chemical treatment were 0.93, 1.50, 0.46 Yuan per m² and 1.03 Yuan per m² in groups A, B, C and D, respectively. CONCLUSIONS The molluscicidal effect of spraying 5% niclosamide ethanolamine salt granules with drones against O. hupensis snails is superior to manual chemical treatment without environmental cleaning, and chemical treatment with drones and manual chemical treatment show comparable molluscicidal effects following environmental cleaning in hilly regions. The cost of chemical treatment with drones is slightly higher than manual chemical treatment regardless of environmental cleaning. Spraying 5% niclosamide ethanolamine salt granules with drones is recommended in complex settings with difficulty in environmental cleaning to improve the molluscicidal activity and efficiency against O. hupensis snails.
Niclosamide/pharmacology* ; Ethanolamine/pharmacology* ; Unmanned Aerial Devices ; China ; Molluscacides/pharmacology* ; Ethanolamines