Distance-Entropy: An Effective Indicator for Selecting Informative Data.

Smart agriculture is inseparable from data gathering, analysis, and utilization. A high-quality data improves the efficiency of intelligent algorithms and helps reduce the costs of data collection and transmission. However, the current image quality assessment research focuses on visual quality, while ignoring the crucial information aspect.

Toward Sustainability: Trade-Off Between Data Quality and Quantity in Crop Pest Recognition.

The crop pest recognition based on the convolutional neural networks is meaningful and important for the development of intelligent plant protection. However, the current main implementation method is deep learning, which relies heavily on large amounts of data. As known, current big data-driven deep learning is a non-sustainable learning mode with the high cost of data collection, high cost of high-end hardware, and high consumption of power resources.

Semi-supervised few-shot learning approach for plant diseases recognition.

Background: Learning from a few samples to automatically recognize the plant leaf diseases is an attractive and promising study to protect the agricultural yield and quality. The existing few-shot classification studies in agriculture are mainly based on supervised learning schemes, ignoring unlabeled data's helpful information.

Methods: In this paper, we proposed a semi-supervised few-shot learning approach to solve the plant leaf diseases recognition.