This work focuses on a novel lightweight machine learning approach to the task of plant disease classification,posing as a core component of a larger grow-light smart monitoring system.To the extent of our knowledge,t...This work focuses on a novel lightweight machine learning approach to the task of plant disease classification,posing as a core component of a larger grow-light smart monitoring system.To the extent of our knowledge,this work is the first to implement lightweight convolutional neural network architectures leveraging down-scaled versions of inception blocks,residual connections,and dense residual connections applied without pre-training to the PlantVillage dataset.The novel contributions of this work include the proposal of a smart monitor-ing framework outline;responsible for detection and classification of ailments via the devised lightweight net-works as well as interfacing with LED grow-light fixtures to optimize environmental parameters and lighting control for the growth of plants in a greenhouse system.Lightweight adaptation of dense residual connections achieved the best balance of minimizing model parameters and maximizing performance metrics with accuracy,precision,recall,and F1-scores of 96.75%,97.62%,97.59%,and 97.58%respectively,while consisting of only 228,479 model parameters.These results are further compared against various full-scale state-of-the-art model architectures trained on the PlantVillage dataset,of which the proposed down-scaled lightweight models were capable of performing equally to,if not better than many large-scale counterparts with drastically less com-putational requirements.展开更多
文摘This work focuses on a novel lightweight machine learning approach to the task of plant disease classification,posing as a core component of a larger grow-light smart monitoring system.To the extent of our knowledge,this work is the first to implement lightweight convolutional neural network architectures leveraging down-scaled versions of inception blocks,residual connections,and dense residual connections applied without pre-training to the PlantVillage dataset.The novel contributions of this work include the proposal of a smart monitor-ing framework outline;responsible for detection and classification of ailments via the devised lightweight net-works as well as interfacing with LED grow-light fixtures to optimize environmental parameters and lighting control for the growth of plants in a greenhouse system.Lightweight adaptation of dense residual connections achieved the best balance of minimizing model parameters and maximizing performance metrics with accuracy,precision,recall,and F1-scores of 96.75%,97.62%,97.59%,and 97.58%respectively,while consisting of only 228,479 model parameters.These results are further compared against various full-scale state-of-the-art model architectures trained on the PlantVillage dataset,of which the proposed down-scaled lightweight models were capable of performing equally to,if not better than many large-scale counterparts with drastically less com-putational requirements.
