Study on a new nitrogen nutrition detection sensor based on stem puncture

Yong Liu; Weiqai Qin; Yanshan Yang; Yafei Wang; Bin Wang; Qiang Shi; Mengdie Xu; Hanping Mao

doi:10.15586/qas.v17i3.1564

Yong Liu Facility Agriculture Intelligent Equipment Engineering Technology Research and Development Centre, Suzhou Polytechnic Institute of Agriculture, Suzhou, Jiangsu Province, China; School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China
Weiqai Qin Facility Agriculture Intelligent Equipment Engineering Technology Research and Development Centre, Suzhou Polytechnic Institute of Agriculture, Suzhou, Jiangsu Province, China
Yanshan Yang Facility Agriculture Intelligent Equipment Engineering Technology Research and Development Centre, Suzhou Polytechnic Institute of Agriculture, Suzhou, Jiangsu Province, China
Yafei Wang School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China
Bin Wang School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China
Qiang Shi School of Science and Technology, Shanghai Open University, Shanghai, China; School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China
Mengdie Xu Institute of science and technology information, Jiangsu University, Zhenjiang, Jiangsu Province, China
Hanping Mao School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China

Keywords

stem; nutrient stress; greenhouse cucumber; puncture; wearable detection

Abstract

This report presents a wearable nitrogen (N) detection sensor based on a stem puncture methodology. Analysis of variance (ANOVA) was employed for statistical evaluation of the experimental data, and the relationship between stem mechanical characteristics and different N treatments was analyzed. The results showed that determination coefficients (R²) of the N detection models based on stem penetration and epidermal break distance were significantly lower compared to the model based on stem epidermal penetration. Consequently, the latter model was selected for further development. This study developed a wearable detection sensor for N nutrition by combining the detection model, a 3D-printed fixed skeleton, and a micro motor. The efficacy of the sensor was high in N detection, with R² and RMSE values of 0.9046 and 12.3 g, respectively, based on a 1:1 straight line. These results were comparable to established N content estimation methods employing spectral technology (R² range: 0.882– 0.980). Furthermore, experimental observations indicated that the wearable sensor had minimal adverse effects on Cucumis sativus growth. Therefore, the proposed wearable N detection sensor was demonstrated to be simple, sensitive, and biocompatible, offering a viable approach for rapid detection of stem mechanical characteristics for crop nutrition.

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