Deep learning for broadleaf weed seedlings classification incorporating data variability and model flexibility across two contrasting environments  

作　　者：Lorenzo León Cristóbal Campos Juan Hirzel 

机构地区：[1]Instituto de Investigaciones Agropecuarias(INIA),INIA Quilamapu,Avda.Vicente Méndez 515,Chillán,Chile [2]Fundacion ObservatorioÑuble,Arauco 340,Chillan,Chile

出　　处：《Artificial Intelligence in Agriculture》2024年第2期29-43,共15页农业人工智能（英文）

基　　金：part of the“New elements of integrated weed management in the south-central zone of Chile”,project 502602-70,financed by the Ministry of Agriculture of Chile.

摘　　要：The increasing deployment of deep learning models for distinguishing weeds and crops has witnessed notable strides in agricultural scenarios.However,a conspicuous gap endures in the literature concerning the training and testing of models across disparate environmental conditions.Predominant methodologies either delineate a single dataset distribution into training,validation,and testing subsets or merge datasets from diverse condi-tions or distributions before their division into the subsets.Our study aims to ameliorate this gap by extending to several broadleaf weed categories across varied distributions,evaluating the impact of training convolutional neural networks on datasets specific to particular conditions or distributions,and assessing their performance in entirely distinct settings through three experiments.By evaluating diverse network architectures and training approaches(finetuning versus feature extraction),testing various architectures,employing different training strategies,and amalgamating data,we devised straightforward guidelines to ensure the model's deployability in contrasting environments with sustained precision and accuracy.In Experiment 1,conducted in a uniform environment,accuracy ranged from 80%to 100%across all models and training strategies,with finetune mode achieving a superior performance of 94%to 99.9%compared to the feature extraction mode at 80%to 92.96%.Experiment 2 underscored a significant performance decline,with accuracy fig-ures between 25%and 60%,primarily at 40%,when the origin of the test data deviated from the train and valida-tion sets.Experiment 3,spotlighting dataset and distribution amalgamation,yielded promising accuracy metrics,notably a peak of 99.6%for ResNet in finetuning mode to a low of 69.9%for InceptionV3 in feature extraction mode.These pivotal findings emphasize that merging data from diverse distributions,coupled with finetuned training on advanced architectures like ResNet and MobileNet,markedly enhances performance,contrasting with the rel-atively lo

关 键 词：Artificial neural networks Deep learning Transfer learning Precision farming Feature extraction Finetuning GENERALIZATION Out-of-domain distribution Domain adaptation Multi-domain learning 

分 类 号：S3[农业科学—农艺学] TP39[自动化与计算机技术—计算机应用技术]