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| 1 | Management of CO_(2) in a tomato greenhouse using WSN and BPNN techniques显示文摘Rational management of CO_(2) can improve the net photosynthetic rate of plants,thereby improving crop yield and quality.In order to precisely manage CO_(2) in a greenhouse,a wireless sensor network(WSN)system was developed to monitor greenhouse environmental parameters in real time,including air temperature,humidity,CO_(2) concentration,soil temperature,soil moisture,and light intensity.The WSN system includes several sensor nodes,a gateway node,and remote management software.The sensor nodes can collect 0-5 V and 4-20 mA analog signals and universal asynchronous receiver/transmitter(UART)data.The gateway node can process and transmit the data and commands between sensor nodes and remote management software.The remote management software provides a friendly interface between user and machine.Users can inquire about real-time data,and set the parameters of the WSN.The photosynthetic rate of tomato plants were studied in the flowering stage.A LI-6400XT portable photosynthesis analyzer was used to measure the photosynthetic rates of the tomato plants,and the environmental parameters of leaves were controlled according to the presetting rule.The photosynthetic rate prediction model of a single leaf was established based on a back propagation neural network(BPNN).The environmental parameters were used as input neurons after being processed by principal component analysis(PCA),and the photosynthetic rate was taken as the output neuron.The performance of the prediction model was evaluated,and the results showed that the correlation coefficient between the simulated and observed data sets was 0.9899,and root-mean-square error(RMSE)was 1.4686.Furthermore,when different CO_(2) concentrations were selected as the input to predict the photosynthetic rate,the simulated and observed data showed the same trend.According to the above analysis,it was concluded that the model can be used for quantitative regulation of CO_(2) for tomato plants in greenhouses. | Li Ting Zhang Man Ji Yuhan Sha Sha Jiang Yiqiong Li Minzan | 2015 | International Journal of Agricultural and Biological Engineering2015，8，4： | 6 |
| 2 | Detection system of smart sprayers: Status, challenges, and perspectives显示文摘A smart sprayer comprises a detection system and a chemical spraying system.In this study,the development status and challenges of the detection systems of smart sprayers are discussed along with perspectives on these technologies.The detection system of a smart sprayer is used to collect information on target areas and make spraying decisions.The spraying system controls sprayer operation.Various sensing technologies,such as machine vision,spectral analysis,and remote sensing,are used in target detection.In image processing,morphological features are employed to segment characteristics such as shape,structure,color,and pattern.In spectral analysis,the characteristics of reflectance and multispectral images are applied in crop detection.For the remote sensing application,vegetation indices and hyperspectral images are used to provide information on crop management.Other sensors,such as thermography,ultrasonic,laser,and X-ray sensors,are also used in the detection system and mentioned in the review.On the basis of this review,challenges and perspectives are suggested.The findings of this study may aid the understanding of smart sprayer systems and provide feasible methods for improving efficiency in chemical applications. | Sun Hong Li Minzan Qin Zhang | 2012 | International Journal of Agricultural and Biological Engineering2012，5，3： | 6 |
| 3 | An improved method for prediction of tomato photosynthetic rate based on WSN in greenhouse显示文摘In order to improve the efficiency of CO2 fertilizer and promote high quality and yield,it is necessary to precisely control CO2 fertilizer by wireless sensor network based on a model of photosynthetic rate prediction in greenhouse.An experiment was carried out on tomato plants in greenhouse for photosynthetic rate prediction modeling combined rough set and BP neural network.In data acquiring phase,plants growth information and greenhouse environmental information that may have influences on photosynthetic rate,including plant height,stem diameter,the number of leaves and chlorophyll content of functional leaves,air temperature,air humidity,light intensity,CO2 concentration and soil moisture,which were measured.And LI-6400XT photosynthetic rate instrument was used for obtaining net photosynthetic rate of functional leaf.After preliminary processing,135 sets of data were obtained.And twelve of them were used for model test of neural network,while the others were used for modeling.All of the data were normalized before modeling.Two models were built to predict photosynthetic rate based on BP neural network.One had total nine input parameters.The other had six input parameters,chlorophyll content,air temperature,air humidity,light intensity,CO2 concentration,and soil moisture,which were reducted from original nine based on attributes reduction theory of rough set.Both two models have one output parameter,the net photosynthetic rate of single leaf.The genetic algorithm was adopted to reduct attributes.Since continuous data cannot be processed by rough set,the K-mean cluster method was used to discretize the data of nine input parameters before attributes reduction.The prediction results of two models showed that the model with six input parameters had a mean absolute error of 0.6958,an average relative error of 7.28%,a root-mean-square error of 0.7428,and a correlation coefficient of 0.9964,while the other model respectively had 0.4026,4.53%,0.3245 and 0.9965,which proved that the model with minimum attributes had higher prediction accuracy.On the other hand,the number of iterations was used to represent the neural network train speed.The result showed that the model with six input parameters had an iteration of 544,while the other had 1038.Hence,the reduction model was applied to controlling CO2 concentration.The net photosynthetic rates at different CO2 concentrations were predicted at a certain condition.The results had the same curve trend with theory analysis,and a high prediction accuracy,which proved that the model was useful for CO2 concentration control. | Ji Yuhan Jiang Yiqiong Li Ting Zhang Man Sha Sha Li Minzan | 2016 | International Journal of Agricultural and Biological Engineering2016，9，1： | 6 |
| 4 | Apple detection from apple tree image based on BP neural network and Hough transform显示文摘Using machine vision to accurately identify apple number on the tree is becoming the key supporting technology for orchard precision production management.For adapting to the complexity of the field environment in various detection situations,such as illumination changes,color variation,fruit overlap,and branches and leaves shading,a robust algorithm for detecting and counting apples based on their color and shape modes was proposed.Firstly,BP(back propagation)neural network was used to train apple color identification model.Accordingly the irrelevant background was removed by using the trained neural network model and the image only containing the apple color pixels was acquired.Then apple edge detection was carried out after morphological operations on the obtained image.Finally,the image was processed by using circle Hough transform algorithm,and apples were located with the help of calculating the center coordinates of each apple edge circle.The validation experimental results showed that the correlation coefficient of R2 between the proposed approaches based counting and manually counting reached 0.985.It illustrated that the proposed algorithm could be used to detect and count apples from apple trees’images taken in field environment with a high precision and strong anti-jamming feature. | Xiao Changyi Zheng Lihua Li Minzan Chen Yuan Mai Chunyan | 2015 | International Journal of Agricultural and Biological Engineering2015，8，6： | 5 |
| 5 | Development and application of crop monitoring system for detecting chlorophyll content of tomato seedlings显示文摘A crop monitoring system was developed to nondestructively monitor the crop growth status in the field.With a two channel multispectral camera with one lens,controlling platform,wireless remote control module and control software,the system was able to synchronously acquire visible image(red(R),green(G),blue(B):400-700 nm)and near-infrared(NIR)image(760-1000 nm).The tomato seedlings multi-spectral images collection experiment in the greenhouse was conducted by using the developed system from the seeding stage to fruiting stage.More than 240 couples of tomato seedlings pictures were acquired with the Soil and Plant Analyzer Development(SPAD)value measured at the same time.The obtained images were available to process,and some vegetation indexes,such as normalized difference vegetation index(NDVI),ratio vegetation index(RVI)and normalized difference green index(NDGI),were calculated.Considering the SPAD value and the correlation coefficient between SPAD and other parameters in different fertilization treatments,the multiple linear regressions(MLR)model for estimating tomato seedlings chlorophyll content was built based on the average gray value in red,green,blue and NIR,vegetable indexes,NDVI,RVI and NDGI in the 33.3%(N1),66.6%(N2),and 100%(N3)nutrient levels during seeding stage and blossom and fruit stage.The R2 of the model is 0.88.The results revealed that the developed crop monitoring system provided a feasible tool to detect the growth status of tomato.More filed experiments and multi-spectral image analysis will be investigated to evaluate the crop growth status in the near future. | Wu Qian Sun Hong Li Minzan Yang Wei | 2014 | International Journal of Agricultural and Biological Engineering2014，7，2： | 5 |
| 6 | Supramolecular Nanohelix Fabricated by Pillararene-Based Host–Guest System for Chirality Amplification, Transfer, and Circularly Polarized Luminescence in Water显示文摘Amplified chirality and Förster resonance energy transfer(FRET)-assisted chirality transfer frommolecular to nanoscale level have been shown to play a vital role in co-assembled nanohelix for potential energy transfer in biological systems.Herein,we have constructed a chiral host–guest complex donor system for chiral amplification via induced chirality of pillar[5]arene host and loaded it with an achiral dye acceptor to demonstrate how chirality-assisted excitation energy transfer occurred in the supramolecular nanohelix system in an aqueous medium. | Krishnasamy Velmurugan Adil Murtaza Azhar Saeed Jianing Li Kaiya Wang Minzan Zuo Qian Liu Xiao-Yu Hu | 2022 | CCS Chemistry2022，4，10： | 3 |
| 7 | Universality of an improved photosynthesis prediction model based on PSO-SVM at all growth stages of tomato显示文摘CO_(2)concentration is an environmental factor affecting photosynthesis and consequently the yield and quality of tomatoes.In this study,a photosynthesis prediction model for the entire growth stage of tomatoes was constructed to elevate CO_(2)level on the basis of crop requirements and to evaluate the effect of CO_(2)elevation on leaf photosynthesis.The effect of CO_(2)enrichment on tomato photosynthesis was investigated using two CO_(2)enrichment treatments at the entire growth stage.A wireless sensor network-based environmental monitoring system was used for the real-time monitoring of environmental factors,and the LI-6400XT portable photosynthesis system was used to measure the net photosynthetic rate of tomato leaf.As input variables for the model,environmental factors were uniformly preprocessed using independent component analysis.Moreover,the photosynthesis prediction model for the entire growth stage was established on the basis of the support vector machine(SVM)model.Improved particle swarm optimization(PSO)was also used to search for the best parameters c and g of SVM.Furthermore,the relationship between CO_(2)concentration and photosynthetic rate under varying light intensities was predicted using the established model,which can determine CO_(2)saturation points at the various growth stages.The determination coefficients between the simulated and observed data sets for the three growth stages were 0.96,0.96,and 0.94 with the improved PSO-SVM and 0.89,0.87,and 0.86 with the original PSO-SVM.The results indicate that the improved PSO-SVM exhibits a high prediction accuracy.The study provides a basis for the precise regulation of CO_(2)enrichment in greenhouses. | Li Ting Ji Yuhan Zhang Man Sha Sha Li Minzan | 2017 | International Journal of Agricultural and Biological Engineering2017，10，2： | 2 |
| 8 | Sensors for measuring plant phenotyping:A review显示文摘Food crisis is a matter of prime importance because it becomes more severe as the global population grows.Among the solutions to this crisis,breeding is deemed one of the most effective ways.However,traditional phenotyping in breeding is time consuming and laborious,and the database is insufficient to meet the requirements of plant breeders,which hinders the development of breeding.Accordingly,innovations in phenotyping are urgent to solve this bottleneck.The morphometric and physiological parameters of plant are particularly interested to breeders.Numerous sensors have been employed and novel algorithms have been proposed to collect data on such parameters.This paper presents a brief review on the parameter measurement for phenotyping to describe its development in recent years.Some parameters that have been measured in phenotyping are introduced and discussed,including plant height,leaf parameters,in-plant space,chlorophyll,water stress,and biomass.And the measurement methods of each parameter with different sensors were classified and compared.Some comprehensive measurement platforms were also summarized,which are able to measure several parameters simultaneously.Besides,some deficiencies of phenotyping should be addressed,and novel methods should be proposed to reduce cost,improve efficiency,and promote phenotyping in the future. | Ruicheng Qiu Shuang Wei Man Zhang Han Li Hong Sun Gang Liu Minzan Li | 2018 | International Journal of Agricultural and Biological Engineering2018，11，2： | 2 |
| 9 | Estimation of spectral responses and chlorophyll based on growth stage effects explored by machine learning methods显示文摘Estimation of leaf chlorophyll content(LCC) by proximal sensing is an important tool for photosynthesis evaluation in high-throughput phenotyping. The temporal variability of crop biochemical properties and canopy structure across different growth stages has great impacts on wheat LCC estimation, known as growth stage effects. It will result in the heterogeneity of crop canopy at different growth stages, which would mask subtle spectral response of biochemistry variations. This study aims to explore spectral responses on the growth stage effects and establish LCC models suited for different growth stages. A total number of 864 pairwise samples of wheat canopy spectra and LCC values with 216 observations of each stage were sampled at the tillering, jointing, booting and heading stages in 2021. Firstly, statistical analysis of LCC and spectral response presented different distribution traits and typical spectral variations peak at 470, 520 and 680 nm. Correlation analysis between LCC and reflectance showed typical red edge shifts. Secondly, the testing model of partial least square(PLS) established by the entire datasets to validate the predictive performance at each stage yielded poor LCC estimation accuracy. The spectral wavelengths of red edge(RE) and blue edge(BE) shifts and the poor estimation capability motivated us to further explore the growth stage effects by establishing LCC models at respective growth periods.Finally, competitive adaptive reweighted sampling PLS(CARS-PLS), decision tree(DT) and random forest(RF) were used to select sensitive bands and establish LCC models at specific stages. Bayes optimisation was used to tune the hyperparameters of DT and RF regression. The modelling results indicated that CARS-PLS and DT did not extract specific wavelengths that could decrease the influences of growth stage effects. From the RF out-of-bag(OOB) evaluation, the sensitive wavelengths displayed consistent spectral shifts from BE to GP and from RE to RV from tillering to heading stages. Compared with CARS-PLS and DT,results of RF modelling yielded an estimation accuracy with deviation to performance(RPD) of 2.11, 2.02,3.21 and 3.02, which can accommodate the growth stage effects. Thus, this study explores spectral response on growth stage effects and provides models for chlorophyll content estimation to satisfy the requirement of high-throughput phenotyping. | Dehua Gao Lang Qiao Lulu An Ruomei Zhao Hong Sun Minzan Li Weijie Tang Nan Wang | 2022 | The Crop Journal2022，10，5： | 2 |
| 10 | Temporal and spatial variability of soil moisture based on WSN显示文摘 | Man Zhang Minzan Li Weizhen Wang Chunhong Liu Hongju Gao | 2013 | Mathematical and Computer Modelling . 2013 (3-4)2013，，3： | 1 |
| 11 | Estimation of chlorophyll content in maize canopy using wavelet denoising and SVR method显示文摘In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction(MSC)to reduce the noise influence.Firstly,the signal to noise ratio(SNR)and curve smoothness(CS)were used to evaluate the denoising effect of different wavelet functions and decomposition levels.As a result,the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original signal.The MSC method was used to eliminate the scattering effect after denoising.Then three spectral ranges were extracted by interval partial least squares(IPLS)including the 525-549 nm,675-749 nm and 850-874 nm.Finally,the chlorophyll content estimation model was developed by using support vector regression(SVR)method.The calibration Rc2 of the SVR model was 0.831,the RMSEC was 1.3852 mg/L;the validation Rv2 was 0.809,the RMSEP was 0.8664 mg/L.The results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field. | Haojie Liu Minzan Li Junyi Zhang Dehua Gao Hong Sun Liwei Yang | 2018 | International Journal of Agricultural and Biological Engineering2018，11，6： | 1 |
| 12 | Development of a smart mobile farming service system 显示文摘 | Lihua Zheng Minzan Li Caicong Wu Haijian Ye Ronghua Ji Xiaolei Deng Yanshuang Che Cheng Fu Wei Guo | 2010 | Mathematical and Computer Modelling2010，543，： | 1 |
| 13 | Soil parameters estimation with NIR spectroscopy 显示文摘 | Li Minzan Sasao A Shibusawa S | 2000 | Journal of the Japanese Society of Agricultural Machinery2000，62，3： | 1 |
| 14 | Development of a Field Wireless Sensors Network Based on ZigBee Technology 显示文摘 | Deng Xiaolei Zheng Lihua Li Minzan | 2010 | IEEE2010，，： | 1 |
| 15 | Soil parameters estimation with NIR spectroscopy显示文摘 | Li Minzan Sasao A Shibusawa S | 2000 | J Jap Soc Agri Mach (in Japanese)2000，62，3： | 1 |
| 16 | Spectral difference a- nalysis and airborne imaging classification for citrus greening infected trees 显示文摘 | Li Xiuhua Lee W S Li Minzan | 2012 | Computers and Electronics in Agriculture2012，83，： | 1 |
| 17 | Adaptive spraying decision system for plant protection unmanned aerial vehicle based on reinforcement learning显示文摘To solve the problem of lacking scientific guidance in aerial pesticide application,this study introduced an adaptive spraying decision system(ASDS)for Unmanned Aerial Vehicle(UAV)spraying to guide the operators of plant protection UAVs to set reasonable spraying parameters under complicated environment.The minimum applied volume rate,proper spraying velocity,spraying height,and initial droplet size were recommended by the ASDS.The key factor of the decision system is the decision model of reinforcement learning based on the actor-critic neural network.In specific,the field experimental data were used to train the critic and actor networks,which made the model adaptive to optimize the output of spraying parameters.Compared with the conventional spraying parameters,the spraying parameters recommended by the ASDS had a positive impact on wheat parcels.The decision results of the ASDS showed that the spraying volume rate was lower in the blocks with a small leaf area index.In addition,the spraying volume rate for the whole parcel was reduced by 14%.After UAV spraying,the uniformity of the droplet deposition in the ASDS parcel was better than that in the conventional parcel.Moreover,the penetrability of the droplets and the control efficacy for the brown wheat mite Petrobia latens(Muller)were similar in the two parcels.The ASDS can recommend the optimal spraying parameters to minimize pesticide application. | Ziyuan Hao Xinze Li Chao Meng Wei Yang Minzan Li | 2022 | International Journal of Agricultural and Biological Engineering2022，15，4： | 1 |
| 18 | Local variability of soil nutrient parameters in Japanese small size field 显示文摘 | LI Minzan Akira Sasao Sakae Shibusawa | 1999 | Journal of the Japanese Society of AgriculturaI Maehinery1999，61，1： | 1 |
| 19 | Feasibility study on Huanglongbing (citrus greening) detection based on WorldView-2 satellite imagery 显示文摘 | Li Xiuhua Leed W S Li Minzan | 2015 | Biosystems Engineering2015，132，： | 1 |
| 20 | Temporal and spatial variability of soil moisture based on WSN显示文摘 | Man Zhang Minzan Li Weizhen Wang Chunhong Liu Hongju Gao | 2013 | Mathematical and Computer Modelling (-)2013，，3： | 1 |
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