Sun Xiaoxiong, Zhu Liangkuan, Liu Dayang
College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
College of Computer Science and Technology, Inner Mongolia Minzu University, Tongliao, China.
Front Plant Sci. 2024 Aug 19;15:1411485. doi: 10.3389/fpls.2024.1411485. eCollection 2024.
Mechanical damage significantly reduces the market value of fruits, making the early detection of such damage a critical aspect of agricultural management. This study focuses on the early detection of mechanical damage in blueberries (variety: Sapphire) through a non-destructive method.
The proposed method integrates hyperspectral image fusion with a multi-strategy improved support vector machine (SVM) model. Initially, spectral features and image features were extracted from the hyperspectral information using the successive projections algorithm (SPA) and Grey Level Co-occurrence Matrix (GLCM), respectively. Different models including SVM, RF (Random Forest), and PLS-DA (Partial Least Squares Discriminant Analysis) were developed based on the extracted features. To refine the SVM model, its hyperparameters were optimized using a multi-strategy improved Beluga Whale Optimization (BWO) algorithm.
The SVM model, upon optimization with the multi-strategy improved BWO algorithm, demonstrated superior performance, achieving the highest classification accuracy among the models tested. The optimized SVM model achieved a classification accuracy of 95.00% on the test set.
The integration of hyperspectral image information through feature fusion proved highly efficient for the early detection of bruising in blueberries. However, the effectiveness of this technology is contingent upon specific conditions in the detection environment, such as light intensity and temperature. The high accuracy of the optimized SVM model underscores its potential utility in post-harvest assessment of blueberries for early detection of bruising. Despite these promising results, further studies are needed to validate the model under varying environmental conditions and to explore its applicability to other fruit varieties.
机械损伤会显著降低水果的市场价值，因此早期检测此类损伤是农业管理的关键环节。本研究聚焦于通过无损方法对蓝莓（品种：蓝宝石）的机械损伤进行早期检测。
所提出的方法将高光谱图像融合与多策略改进的支持向量机（SVM）模型相结合。首先，分别使用连续投影算法（SPA）和灰度共生矩阵（GLCM）从高光谱信息中提取光谱特征和图像特征。基于提取的特征开发了包括支持向量机、随机森林（RF）和偏最小二乘判别分析（PLS - DA）在内的不同模型。为了优化支持向量机模型，使用多策略改进的白鲸优化（BWO）算法对其超参数进行了优化。
经多策略改进的白鲸优化算法优化后的支持向量机模型表现出卓越性能，在测试的模型中实现了最高的分类准确率。优化后的支持向量机模型在测试集上的分类准确率达到了95.00%。
通过特征融合整合高光谱图像信息在蓝莓瘀伤早期检测中证明是高效的。然而，该技术的有效性取决于检测环境中的特定条件，如光照强度和温度。优化后的支持向量机模型的高精度凸显了其在蓝莓采后评估中早期检测瘀伤的潜在效用。尽管取得了这些令人鼓舞的结果，但仍需要进一步研究以在不同环境条件下验证该模型，并探索其对其他水果品种的适用性。
