RiceSeedNet: Rice seed variety identification using deep neural network

Abstract

Rice is one of the most important food crops in the South India. Many varieties of rice are cultivated in different regions of the India to meet the dietary needs of the ever-growing population. In spite of huge investment in terms of land, labour, raw materials and machinery, the farmers continuously face irrecoverable loss due to various reasons like climatic changes, drought situation and seed quality. In the current practice, the quality of the seeds is certified by the Seed Testing Laboratories (STL) and purity analysis is done manually by trained technicians. However, seed classification is not uniform across different labs, due to several factors like fatigue, eye-strain and personal circumstances of the technicians. Hence, automated rice seed variety identification becomes a crucial task for ensuring the quality and germination potential of rice crops. This research is focused on the application of Deep Neural Network (RiceSeedNet) combined with traditional image processing techniques to classify local rice seed varieties of southern Tamilnadu, India. The RiceSeed Image corpus is created for this purpose considering 13 local varieties. The captured RGB images of rice seed data consists of 13,000 images of local rice seed varieties, having 1000 images for each variety. To automate the rice seed varietal identification, vision transformer-based architecture RiceSeedNet is developed. The proposed RiceSeedNet is 97% accurate in classifying the 13 local varieties of rice seeds. The RiceSeedNet was also evaluated on a publicly available rice grain data set to study the performance of the proposed model across the different rice grain varieties. On this cross-data validation, RiceSeedNet is able to achieve 99% accuracy in classifying 8 varieties of rice grains on the public dataset.

Keywords

• Deep neural network
• Vision transformer
• Rice seed classification
• Precision agriculture
• Image processing
• Machine vision