An intelligent VegeCareAI tool for next generation plant growth management

Abstract

In the field of agriculture, there are many individual micro businesses with low investment capacity and awareness of IT utilization, and it is difficult to obtain a return on investment. In recent years, edge computing and Artificial Intelligence (AI) technologies have attracted a lot of attention in the agricultural industry to cover the labor shortage. Also, safer vegetables are required by peoples due to COVID-19 epidemic and radioactive pollution. In this paper, we propose an agricultural support system called VegeCareAI for agricultural workers. The proposed system supports vegetable classification, plant disease classification and insect pest classification to improve the crops’ productively. The support system can show the growth condition of vegetables. When there are some problems, the VegeCareAI presents information on how to deal with diseases and insect pests. From the results, we found that our proposed VegeCareAI tool has advantage for supporting several crops. For vegetable classification, our training data for 300 epochs predicted six kinds of vegetables correctly. For plant disease classification, for 400 epochs the accuracy is more than 96% accuracy for both potato and corn leaves. For insect pest classification, the accuracy of corn insect pests is more than 73%, but the results of different life cycles showed low classification accuracy, which present a future challenge.

Introduction

Recently, Artificial Intelligence (AI) based agriculture systems have attracted attention because Japan’s farming population has steadily decreased with agricultural worker’s average age has risen. Thus reducing the cultivated area where crops are harvested on a regular cycle. On the other hand, people are more concerned for safer and cost efficient food due to the impact of the COVID-19 epidemic and radioactive pollution [1], [2]. They have massively impacted every part of life, both economic and non-economic, including the agriculture sector. Likewise, most of the food produced for the food industry came from agricultural commodity products. A key role for solving the problem in fostering agricultural innovation is a collaboration between non-agriculture and agricultural technologies, such as edge computing, cloud computing and AI [3], [4], [5], [6], [7]. Cloud computing services can be used for big data analysis and massive amounts of batch jobs. However, edge computing is better for agriculture compared with cloud computing considering unfriendly conditions and remote locations of farms that may present network connectivity and bandwidth concerns. The purpose of edge computing is to provide a suitable, cost-effective alternative. In this way, edge-based agriculture systems are expected to improve agricultural products support to obtain high quality and safely to solve the shortage of agricultural workers [8], [9].

In order to improve the crops’ productivity, we proposed a classification tool considering tomato diseases [10]. We analyzed the classification performance in 6 classes of tomato diseases. The proposed tool predicted tomato mosaic virus correctly, but the testing results of septoria leaf spot is not high because the leaf color and leaf shape are similar.

In this paper, we propose a VegeCareAI tool for plant growth management considering vegetable classification, disease classification and insect pest classification. We use three kinds of classification functions to provide many guidelines for next generation agricultural worker. Our agricultural support system is focusing on edge AI, where daily learning is done in the cloud server and real-time prediction is done at the edge device.

The structure of the paper is as follows. In Sect. 2, we describe the related work. In Sect. 3, we present the proposed agricultural support system. In Sect. 4, we show the evaluation setting. In Sect. 5, we present the experimental results. Finally, conclusions and future work are given in Sect. 6.