Next Article in Journal
Advanced Data Analysis as a Tool for Net Blotch Density Estimation in Spring Barley
Next Article in Special Issue
Modeling the Dynamic Response of Plant Growth to Root Zone Temperature in Hydroponic Chili Pepper Plant Using Neural Networks
Previous Article in Journal
Prediction of Soil Oxalate Phosphorus using Visible and Near-Infrared Spectroscopy in Natural and Cultivated System Soils of Madagascar
Previous Article in Special Issue
Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain
Open AccessArticle

ANN-Based Continual Classification in Agriculture

by Yang Li 1,2,* and Xuewei Chao 1
1
College of Mechanical and Electrical Engineering, Shihezi University, Xinjiang 832003, China
2
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
*
Author to whom correspondence should be addressed.
Agriculture 2020, 10(5), 178; https://doi.org/10.3390/agriculture10050178
Received: 13 April 2020 / Revised: 15 May 2020 / Accepted: 15 May 2020 / Published: 18 May 2020
(This article belongs to the Special Issue Artificial Neural Networks in Agriculture)
In the area of plant protection and precision farming, timely detection and classification of plant diseases and crop pests play crucial roles in the management and decision-making. Recently, there have been many artificial neural network (ANN) methods used in agricultural classification tasks, which are task specific and require big datasets. These two characteristics are quite different from how humans learn intelligently. Undoubtedly, it would be exciting if the models can accumulate knowledge to handle continual tasks. Towards this goal, we propose an ANN-based continual classification method via memory storage and retrieval, with two clear advantages: Few data and high flexibility. This proposed ANN-based model combines a convolutional neural network (CNN) and generative adversarial network (GAN). Through learning of the similarity between input paired data, the CNN part only requires few raw data to achieve a good performance, suitable for a classification task. The GAN part is used to extract important information from old tasks and generate abstracted images as memory for the future task. Experimental results show that the regular CNN model performs poorly on the continual tasks (pest and plant classification), due to the forgetting problem. However, our proposed method can distinguish all the categories from new and old tasks with good performance, owing to its ability of accumulating knowledge and alleviating forgetting. There are so many possible applications of this proposed approach in the agricultural field, for instance, the intelligent fruit picking robots, which can recognize and pick different kinds of fruits; the plant protection is achieved by automatic identification of diseases and pests, which can continuously improve the detection range. Thus, this work also provides a reference for other studies towards more intelligent and flexible applications in agriculture. View Full-Text
Keywords: similarity; metric; memory; deep learning similarity; metric; memory; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Li, Y.; Chao, X. ANN-Based Continual Classification in Agriculture. Agriculture 2020, 10, 178.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop