Gopalakrishnan K, Sivaraj R, Vijayakumar M
Department of Computer Science and Business Systems, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India, 641048.
Department of Computer Science and Engineering, Nandha Engineering College, Perundurai, Erode, Tamil Nadu, India, 638052.
Sci Rep. 2025 Aug 10;15(1):29279. doi: 10.1038/s41598-025-15275-3.
Weeds and crops contribute to a endless resistance for similar assets, which leads to potential declines in crop production and enlarged agricultural expenses. Conventional models of weed control like extensive pesticide use, appear with the hassle of environmental pollution and advancing weed battle. As the need for organic agricultural and pollutant-free products increases, there is a crucial need for revolutionary solutions. The rise of smart agricultural tools, containing satellite technology, unmanned aerial vehicles (UAV), and intelligent robots certifies to be paramount in dealing with weed-related challenges. Deep learning (DL) based object detection model has been carried out in numerous applications. As a result, need for instance-level analyses of the weed dataset places constraints on the significance of influential DL methods. Artificial intelligence (AI) led image analysis for weed recognition and mainly, machine learning (ML) and deep learning (DL) utilizing images from cultivated lands have commonly been employed in the literature for identifying numerous kinds of weeds that are cultivated beside crops. This method develops an Automated Weed Recognition and Classification using a Deep Learning Model with Lemrus Optimization (AWRC-DLMLO). The main purpose of the AWRC-DLMLO method is to effectively detect and classify weeds and crop. In the proposed AWRC-DLMLO technique, the main phase of Gaussian filtering (GF) utilizing image pre-processing is implemented to eliminate unwanted noise. The plant segmentation was also developed utilizing the Residual Attention U-Net (RA-UNet) for generating segments. The ShuffleNetV2 approach is exploited in the AWRC-DLMLO method to ascertain feature vector. Next, the lemurs optimization algorithm (LOA) is applied to increase the hyperparameter and fine-tune the DL technique, further enhancing its performance. Eventually, the cascading Q-network (CQN)model is employed for the classification process. To emphasize the improved weed detection performance of the projected AWRC-DLMLO method, a wide range of simulations were done. The extensive outcome highlighted the improvements of the developed AWRC-DLMLO technique with other existing models.
杂草和农作物对类似资源造成了无休止的抗性，这导致作物产量可能下降，农业成本增加。传统的杂草控制模式，如大量使用农药，伴随着环境污染和杂草抗性增强的问题。随着对有机农产品和无污染物产品需求的增加，迫切需要革命性的解决方案。智能农业工具的兴起，包括卫星技术、无人机（UAV）和智能机器人，被证明在应对与杂草相关的挑战方面至关重要。基于深度学习（DL）的目标检测模型已在众多应用中得到应用。因此，对杂草数据集进行实例级分析的需求限制了有影响力的DL方法的重要性。人工智能（AI）主导的用于杂草识别的图像分析，主要是利用耕地图像的机器学习（ML）和深度学习（DL），在文献中通常被用于识别与作物共生的多种杂草。该方法开发了一种使用带有狐猴优化的深度学习模型的自动杂草识别与分类（AWRC-DLMLO）。AWRC-DLMLO方法的主要目的是有效地检测和分类杂草与作物。在所提出的AWRC-DLMLO技术中，利用图像预处理的高斯滤波（GF）主要阶段被用于消除不需要的噪声。还利用残差注意力U-Net（RA-UNet）进行植物分割以生成片段。在AWRC-DLMLO方法中利用ShuffleNetV2方法来确定特征向量。接下来，应用狐猴优化算法（LOA）来增加超参数并微调DL技术，进一步提高其性能。最终，级联Q网络（CQN）模型用于分类过程。为了强调所提出的AWRC-DLMLO方法在杂草检测性能上的提升，进行了广泛的模拟。广泛的结果突出了所开发的AWRC-DLMLO技术相对于其他现有模型的改进。
