Crop and weed classification based on AutoML
This addresses the need for more efficient and accurate classification in agricultural farming, though it appears incremental as it builds on existing CNN methods.
The paper tackled the problem of laborious manual fine-tuning and misclassification in crop and weed classification by applying autonomous machine learning with a new objective function, achieving higher accuracy and a lower crop killing rate compared to state-of-the-art models like ResNet and VGG19.
CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable in most traditional practices and research. Moreover, the classic objective functions are not thoroughly compatible with agricultural farming tasks as the corresponding models suffer from misclassifying crop to weed, often more likely than in other deep learning application domains. In this paper, we applied autonomous machine learning with a new objective function for crop and weed classification, achieving higher accuracy and lower crop killing rate (rate of identifying a crop as a weed). The experimental results show that our method outperforms state-of-the-art applications, for example, ResNet and VGG19.