ANN-Based Continual Classification in Agriculture

Yang Li,Xuewei Chao

doi:10.3390/agriculture10050178

Yang Li, Xuewei Chao

Open Access

https://doi.org/10.3390/agriculture10050178

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Journal: Agriculture	Publication Date: May 18, 2020
Citations: 63	License type: CC BY 4.0

Affiliation: Shihezi University, Tianjin University

Abstract

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.

Highlights

In the field of intelligent agriculture, for instance, plant protection and precision farming, there are incremental progresses in agricultural image processing, e.g., classification of crop pests, and harvest yield forecast
When first task the task data will metric be organized as pairs and fed the metric learning In order tothe testify thecomes, performance of the learning model ontosimilarity matching for a model (SiameseExperiment network).with
In order to testify the performance of the metric learning model on similarity matching for a single task, we carried out experiments on a crop pest dataset and plant leaf dataset, respectively

Summary

Introduction

In the field of intelligent agriculture, for instance, plant protection and precision farming, there are incremental progresses in agricultural image processing, e.g., classification of crop pests, and harvest yield forecast. Step advances are catalyzed by the developed various computerized models, which have covered a wide range of technologies, such as machine learning, deep learning, transfer learning, few-shot learning, and so on. Several machine learning methods were adopted in crop pest classification [1,2]. The deep learning neural networks showed a powerful and excellent performance on several agricultural applications, such as plant identification [5], crop classification [6,7], fruit classification [8], weed classification [9], animal classification [10], quality evaluation [11], and field pest classification [12,13]. There were some related agricultural research surveys [17,18,19], providing more comprehensive views

Methods

Results

Conclusion