# SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n
## 1 Introduction  
Rapeseed (_Brassica napus_ L.), a globally vital oilseed crop, serves as a primary source of edible vegetable oil and plant protein, holding significant agricultural importance. China, the world's leading rapeseed producer, contributes approximately 30% of the global cultivation area and total output [1]. However, escalating demand has driven external dependence rates to nearly 70%, posing substantial risks to national food security and exacerbating the supply-demand imbalance for vegetable oils [2]. Cultivating elite rapeseedvarieties is critical for enhancing yield and addressing these challenges, underscoring the urgency for advanced breeding strategies to ensure sustainable agricultural productivity. Field-based phenotypic analysis plays a critical role in evaluating plant performance during crop breeding [3; 4]. High-throughput phenotyping enables timely and accurate acquisition of growth and developmental data, facilitating optimized field management decisions for rapeseed. Rapeseed yield is dominated by three factors: seed number per pod, pod number and grain weight. Pod retention rate at maturity is one of the key influencing factors [5], and pod retention rate is mainly restricted by flowering time and inflorescence number [6]. Therefore, the flowering time of rapeseed had a significant effect on yield [7; 8]. Although some studies have determined the correlation between the duration of flowering stage [9], the density of full flowering stage [10], and the canopy coverage rate with seed yield and quality [11], there is a lack of methods to accurately quantify rapeseed inflorescusters. Traditional field counting relies on manual observation, which is inefficient and subjective. In the field of breeding and yield prediction, accurate counting of rapeseed inflorescusters is very critical, especially in the density of full flowering stage, the number of inflorescusters and flowering time directly affect the pod retention rate and then affect the yield. Therefore, in order to improve the accuracy of breeding and provide a reliable basis for yield prediction models, it is urgent to develop automated and non-destructive techniques for inflorescence quantification. The application of satellite remote sensing technology in agriculture has become a trend [12]. It can acquire multi-spectral crop information over large areas at different times and spaces without damaging crop structures, and is widely used in precision agriculture, yield prediction, and other fields [13; 14]. It has also been successfully applied to rapeseed monitoring. For example, [PERSON] [10] et al. combined optical and radar images captured by satellites to estimate flowering time, while [PERSON] [9] et al. used Landsat-8 and Sentinel-1/2 to monitor flowering characteristics. [PERSON] [15] et al. extracted traits from multi-spectral and LiDAR data to predict wheat yield and grain protein content. However, the low spatial resolution and temporal frequency of satellite images limit their development in precision agriculture, potentially leading to missed flowering times and difficulties in counting small objects such as rapeseed inflorescences from low-resolution images. Although multi-spectral data can provide extensive plant information and aid in phenotypic analysis, its acquisition is heavily influenced by weather conditions. The inconsistent flowering times of different rapeseed varieties and the rapid changes in rapeseed flower conditions in the field further limit the feasibility of multi-spectral data collection. Therefore, a convenient and high-throughput method for rapeseed inflorescence counting is crucial for rapeseed breeding. Unmanned aerial vehicle (UAV) RGB imaging technology has become a research and application hotspot due to its flexibility, real-time capability, and high resolution [16; 17]. Counting methods based on density estimation are widely used in these studies. They count objects by estimating their density in images.