Deep convolutional neural network models for weed detection in polyhouse grown bell peppers
作者机构：ICAR-Central Institute of Agricultural Engineering(CIAE)BhopalMadhya PradeshIndia
出 版 物：《Artificial Intelligence in Agriculture》 (农业人工智能（英文）)
年 卷 期：2022年第6卷第1期
页 面：47-54页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Bell pepper Computer vision Convolutional neural networks Deep learning Weed identification
摘 要：Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural *** identification and classification of weeds can play a vital role in weed management contributing to better crop *** and smart spot-spraying system s efficiency relies on the accuracy of the computer vision based detectors for autonomous weed *** the present study,feasibility of deep learning based techniques(Alexnet,GoogLeNet,InceptionV3,Xception)were evaluated in weed identification from RGB images of bell pepper *** models were trained with different values of epochs(10,20,30),batch sizes(16,32),and hyperparameters were tuned to get optimal *** overall accuracy of the selected models varied from 94.5 to 97.7%.Among the models,InceptionV3 exhibited superior performance at 30-epoch and 16-batch size with a 97.7%accuracy,98.5%precision,and 97.8%*** this Inception3 model,the type 1 error was obtained as 1.4%and type II error was 0.9%.The effectiveness of the deep learning model presents a clear path towards integrating them with image-based herbicide applicators for precise weed management.
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