Onion Crop Monitoring with Multispectral Imagery using Deep Neural Network

Abstract

The world’s growing population leads the government of Pakistan to increase the supply of food for the coming years in a well-organized manner. Feasible agriculture plays a vital role for sustain food production and preserves the environment from any unnecessary chemicals by the use of technology for good management. This research presents the design and development of a multi-spectral imaging system for precision agriculture tasks. This imaging system includes an RGB camera and Pi NoIR camera controlled by a raspberry pi in a drone. The images are captured by Unmanned Aerial Vehicle (UAV) and then send images to the Java application. Images are processed to sharp, resize by application. The Normalized Difference Vegetation Index (NDVI) is calculated to determine the crop health status based on real-time data. The Deep Learning (DL) technique is used to recognize the onion crop growth stage using the captured dataset. We express how to implement a progressive model for the deep neural network to recognize the onion crop growth stage. The performance accuracy of the system for batch size 16 is 96.10% and for batch size 32 is 93.80%.

Plant phenotyping relevance

UAVマルチスペクトル撮像システムと深層学習によるタマネギの生育段階・健全性推定を開発しており、植物表現型の取得・抽出が研究の中心である。

Code and data availability

The paper describes a custom UAV-collected onion crop dataset (4000 RGB/NIR images) and a VGG16-based DL4J application, but provides no public dataset deposit, code repository, model checkpoint, or any availability statement with an authors' URL. No qualifying paper-specific public asset is present in the supplied text

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