This paper reviews the development of all-solid-state ion-selective electrodes (ASSISEs) for agricultural crop detection. Both nutrient ions and heavy metal ions inside and outside the plant have a significant influence on crop growth. This review begins with the detection principle of ASSISEs. The second section introduces the key characteristics of ASSISE and demonstrates its feasibility in crop detection based on previous research. The third section considers the development of ASSISEs in the detection of corps internally and externally (e.g., crop nutrition, heavy metal pollution, soil salinization, N enrichment, and sensor miniaturization, etc.) and discusses the interference of the test environment. The suggestions and conclusions discussed in this paper may provide the foundation for additional research into ion detection for crops.

Calcium, potassium, nitrogen, magnesium, and phosphorus, the main elements of the nutrient solution, are absorbed by plants and play an important role in plants. By measuring Ca²⁺, K⁺, Mg²⁺, NH₄ ⁺, NO₃ ⁻, HPO₄ ²⁻, the artificial neural networks (ANNs) were used in this study to accurately calculate the concentrations of these elements. Firstly, the error sources of the calculating element concentration were analyzed based on the data of six-ion measurement experiments. Subsequently, various optimization algorithms were compared to optimize back propagation and radial basis function ANNs. Finally, the results of mean relative errors (MREs) and recovery values show that ANNs can effectively reduce the measurement error of ion sensors. From the perspective of recovery values, the prediction error of all elements can be controlled within 15%. From the perspective of MRE, except for magnesium and phosphorus elements, the improved model prediction errors of other elements were also less than 10%.

Traditional detection methods of crop information are often destructive and low efficiency. In this study, a new evaluation method was developed based on photosynthesis and chlorophyll fluorescence. Via analysis of the changes under NaCl stress during the seedling stage of two varieties, the salt resistance mechanism of soybeans was explored and a non-destructive stress-recognition method was developed. In this experiment, two soybean varieties were treated with one of four levels of NaCl stress: CK (0 mmol/L), LS (50 mmol/L), MS (100 mmol/L), and HS (150 mmol/L), for 15 d. The normal functions of the photosynthetic system of soybeans were enhanced under LS NaCl stress, but were inhibited under HS NaCl stress. Biomass, net leaf photosynthetic rate (Pn), stomatal conductance (gs), intercellular carbon dioxide concentration (Ci), transpiration rate (Tr), chlorophyll fluorescence parameters Y(II) and PSII decreased. However, in contrast to the findings of other studies on the influence of severe drought stress on soybean for long periods in which non-photochemical quenching coefficient (qN) decreased, this parameter increased under salt stress in soybean. The results demonstrate that the method developed is a promising tool for rapid and non-destructive detection of soybean photosynthetic responses under salt stress in the field. Keywords: biomass, chlorophyll fluorescence parameters, NaCl stress, photosynthetic parameters, soybean DOI: 10.25165/j.ijabe.20211403.5941 Citation: Luo B, Wang C, Wang X D, Zhang H, Zhou Y N, Wang W S, et al. Changes in photosynthesis and chlorophyll fluorescence in two soybean (Glycine max) varieties under NaCl stress. Int J Agric & Biol Eng, 2021; 14(3): 76–82.

This review systematically studied and discussed the application of ion-selective electrode arrays combined with classification algorithms in agriculture.