Dynamic Analysis of Chlorophyll a Fluorescence in Response to Time-Variant Excitations during Strong Actinic Illumination and Application in Probing Plant Water Loss

Magnitude measurement of chlorophyll a fluorescence(ChIF)involves challenges,and dynamic responses to variable excitations may offer an alternative.In this research,ChIF was measured during strong actinic light by using a pseudo-random binary sequence as a time-variant multiple-frequency illumination excitation.The responses were observed in the time domain but were primarily analyzed in the frequency domain in terms of amplitude gain variations.The excitation amplitude was varied,and moisture loss was used to induce changes in the plant samples for further analysis.The results show that when nonphotochemical quenching(NPQ)activities start,the amplitude of ChIF responses vary,making the ChIF responses to illumination excitations nonlinear and nonstationary.NPQ influences the ChIF responses in low frequencies,most notably below 0.03 rad/s.The low-frequency gain is linearly correlated with NPQ and can thus be used as a reference to compensate for the variations in ChIF measurements.The high-frequency amplitude gain showed a stronger correlation with moisture loss after correction with the low-frequency gain.This work demonstrates the usefulness of dynamic characteristics in broadening the applications of ChIF measurements in plant analysis and offers a way to mitigate variabilities in ChIF measurements during strong actinic illumination.

Deep learning for smart agriculture:Concepts,tools,applications,and opportunities

In recent years,Deep Learning(DL),such as the algorithms of Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN)and Generative Adversarial Networks(GAN),has been widely studied and applied in various fields including agriculture.Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique.This article provides a concise summary of major DL algorithms,including concepts,limitations,implementation,training processes,and example codes,to help researchers in agriculture to gain a holistic picture of major DL techniques quickly.Research on DL applications in agriculture is summarized and analyzed,and future opportunities are discussed in this paper,which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly,and further to facilitate data analysis,enhance related research in agriculture,and thus promote DL applications effectively.

Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU)

Dissolved oxygen(DO),an important water quality indicator in aquaculture,affects the survival rate of aquatic creatures and the yield of aquatic production.Therefore,it is important to predict DO in fishery ponds for applying artificial aeration with low energy and cost.Recently,deep learning models,such as recurrent neural network(RNN),long short-term memory(LSTM),and gated recurrent unit(GRU),are often used to predict the trend of time series,but it is unclear which one of them is more suitable for prediction of DO in fishery ponds.In this work,the RNN model,LSTM model,and GRU model were used to build three DO predicting models.The performance of the three models were compared by mean absolute error(MAE),mean square error(MSE),mean absolute percentage error(MAPE),and the coefficient of determination(R2).The performance of RNN is worse result than LSTM and GRU.The four evaluation indicators of GRU are 0.450 mg/L,0.411,0.054,and 0.994,and the four indicators of LSTM are 0.407 mg/L,0.294,0.059,and 0.970,which shows that the performance of GRU is similar to LSTM,but the time cost and number of parameters used for GRU is much lower than LSTM.It is concluded that the GRU has overall better performance and can be applied to practical applications.