Grow-light smart monitoring system leveraging lightweight deep learning for plant disease classification  

作　　者：William Macdonald Yuksel Asli Sari Majid Pahlevani 

机构地区：[1]Department of Electrical&Computer Engineering,Queen's University,Kingston,ON K7L 3N6,Canada [2]The Robert M.Buchan Department of Mining,Queen's University,Kingston,ON K7L 3N6,Canada

出　　处：《Artificial Intelligence in Agriculture》2024年第2期44-56,共13页农业人工智能（英文）

摘　　要：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.

关 键 词：Plant disease classification Smart monitoring Deep learning Residual connections INCEPTION Dense residual connections 

分 类 号：S43[农业科学—农业昆虫与害虫防治] TP39[农业科学—植物保护]