Smart Agriculture

Application Analysis and Prospect of Nanosensor in the Quality and Safety of Agricultural Products | Open Access

WANG Peilong , TANG Zhiyong

2020, 2(2): 1-10. doi:10.12133/j.smartag.2020.2.2.202003-SA003

Asbtract ( 1406 )

HTML ( 1430)

PDF (1634KB) ( 1171 )

References | Related Articles | Metrics

Nano materials with special size effect and excellent photoelectric properties have been highly valued and widely used in sensing analysis for greatly improving the performance of sensor analysis technology. In recent years, with the rapid development of smart agriculture, the quality and safety of agricultural products as an important part of agricultural production have attracted more and more attentions. There are many harmful ingredients, including pesticides, veterinary drugs, mycotoxins, and environmental contaminants etc, can potentially affected the quality and safety of agricultural products. Therefore, high performance analytical methods and sensing technologies are essential. Thanks to the emerging of nano materials, they provide a novel approach to improve the analytical performances of the sensing technologies. Furthermore, the sensors based on nano materials have also been utilized into monitoring the harmful substances in agricultural products. This review briefly described the properties and characteristics of several commonly used nano materials, including carbon nano materials, noble metal based nano materials and metal-organic framework materials, follow discussed on the common sensing and analysis technologies and devices based on nano materials, such as chemical sensor, biosensor, electrochemical sensor and spectral sensor, as well as the application of nano sensing technology in the quality and safety monitoring of agricultural products. Especially, the function of nano materials in sensors and analytical performances of the developed sensors had been discussed in detailed. Chemical sensor devices had the characteristics of fast response speed and high sensitivity. They were widely used in environmental monitoring, food safety and medical diagnosis, such as monitoring hazardous substances, clenbuterol and melamine, metronidazole, dioxins, etc. Biosensors were widely used to monitor prohibited additives, mycotoxins, and so on. Electrochemical sensors were typically equipped with miniaturized analysis equipment, which detected trace targets, including small organic molecules, metal ions and biomolecules, by measuring changed in current and other electrochemical signals. This article introduced surface-enhanced Raman spectroscopy (SERS) , which was one of spectral sensor, and its applications. SERS technology had the advantages of good sensitivity, single molecule detection capability and rich spectral information. It had become a promising spectral technology in the rapid sensing analysis of target objects, and is developing rapidly in the fields of food safety, environmental monitoring and health. Finally, the existing problems of nano sensing and analysis technology, such as achievement of high-performance nano materials, fabrication of sensing devices and construction of high flux sensing arrays were summarized. The development trend and prospect of nanosensor were also discussed. It is believed that the review could provide a lot of useful information for the readers to understand the development of sensing technology for the quality and safety of agricultural products.

Characteristics Analysis and Challenges for Fault Diagnosis in Solar Insecticidal Lamps Internet of Things | Open Access

YANG Xing, SHU Lei, HUANG Kai, LI Kailiang, HUO Zhiqiang, WANG Yanfei, WANG Xinyi, LU Qiaoling, ZHANG Yacheng

2020, 2(2): 11-27. doi:10.12133/j.smartag.2020.2.2.202005-SA002

Asbtract ( 1840 )

HTML ( 2501)

PDF (3592KB) ( 990 )

References | Related Articles | Metrics

Solar insecticidal lamps Internet of Things (SIL-IoTs) is a novel physical agricultural pest control implement, which is an emerging paradigm that extends Internet of Things technology towards Solar Insecticidal Lamp (SIL). SIL-IoTs is composed of SIL nodes with functions of preventing and controlling of agricultural migratory pests with phototaxis feature, which can be deployed over a vast region for the purpose of ensuring pests outbreak area location, reducing pesticide dosage and monitoring agricultural environmental conditions. SIL-IoTs is widely used in agricultural production, and a number of studies have been conducted. However, in most current research projects, fault diagnosis has not been taken into consideration, despite the fact that SIL-IoTs faults have an adverse influence on the development and application of SIL-IoTs. Based on this background, this research aims to analyze the characteristics and challenges of fault diagnosis in SIL-IoTs, which naturally leads to a great number of open research issues outlined afterward. Firstly, an overview and state-of-art of SIL-IoTs were introduced, and the importance of fault diagnosis in SIL-IoTs was analyzed. Secondly, faults of SIL nodes were listed and classified into different types of Wireless Sensor Networks (WSNs) faults. Furthermore, WSNs faults were classified into behavior-based, time-based, component-based, and area affected-based faults. Different types of fault diagnosis algorithms (i.e., statistic method, probability method, hierarchical routing method, machine learning method, topology control method, and mobile sink method) in WSNs were discussed and summarized. Moreover, WSNs fault diagnosis strategies were classified into behavior-based strategies (i.e., active type and positive type), monitoring-based strategies (i.e., continuous type, periodic type, direct type, and indirect type) and facility-based strategies (i.e., centralized type, distributed type and hybrid type). Based on above algorithms and strategies, four kinds of fault phenomena: 1) abnormal background data, 2) abnormal communication of some nodes, 3) abnormal communication of the whole SIL-IoTs, and 4) normal performance with abnormal behavior actually were introduced, and fault diagnosis tools (i.e., Sympathy, Clairvoyant, SNIF and Dustminer) which were adapted to the mentioned fault phenomena were analyzed. Finally, four challenges of fault diagnosis in SIL-IoTs were highlighted, i.e., 1) the complex deployment environment of SIL nodes, leading to the fault diagnosis challenges of heterogeneous WSNs under the condition of unequal energy harvesting, 2) SIL nodes task conflict, resulting from the interference of high voltage discharge, 3) signal loss of continuous area nodes, resulting in the regional link fault, and 4) multiple failure situations of fault diagnosis. To sum up, fault diagnosis plays a vital role in ensuring the reliability, real-time data transmission, and insecticidal efficiency of SIL-IoTs. This work can also be extended for various types of smart agriculture applications and provide fault diagnosis references.

Cognitive Radio Sensor Networks Clustering Routing Algorithm for Crop Phenotypic Information Edge Computing Collection | Open Access

WANG Jinhong, HAN Yuxing

2020, 2(2): 28-47. doi:10.12133/j.smartag.2020.2.2.201909-SA005

Asbtract ( 958 )

HTML ( 1349)

PDF (2980KB) ( 982 )

References | Related Articles | Metrics

With the rapid growth of wireless nodes numbers and the increase in demanding for high-bandwidth transmission services such as multimedia images, the related fields of the agricultural Internet of Things(IoT) can foresee a trend of shortage of wireless spectrum resources. For the crop phenotypic information collection system based on the traditional IoT, there are many problems such as spectrum competition, data congestion during the data transmission process due to the dense deployment of nodes, and the reduction of the monitoring cycle due to uneven energy consumption in the fixed battery network. Based on previous studies, a crop phenotypic information collection model for cognitive radio sensor networks was established, and based on the model, an event-driven clustering routing algorithm that introduced dynamic spectrum and energy balance (DSEB) of edge computer system was proposed. The algorithm includes dynamic spectrum sensing clustering. The hierarchical clustering algorithm was used to combine the available channels, distances between nodes, residual energy, and neighbor node degrees obtained by spectrum sensing as similarities to cluster and cluster nodes in the monitored area and select cluster heads. The process of clustering and selecting cluster heads and constructing a clustering topology introduceed rewards and punishment factors to the equilibrium of the clustering sizes to improve the average spectrum utilization of each clustering network. The events triggered by edge computing trigger data routing, and based on the clustered topology structure, the events triggered by abnormal changes in farm conditions in the areas to be detected on the farm were forwarded to the convergent nodes by means of alternate cluster iterations and inter-cluster relays. Convergence includes direct transmission and intra-cluster relay, and inter-cluster relay includes two cases: ①primary gateway node and secondary gateway node-primary gateway node; ②adaptive re-clustering based on spectrum changes and communication quality of service (QoS)－changes in available channels caused by changes in the PU behavior of the primary user, or interference with poor quality of clustering effects on communication service quality, triggering cognitive radio sensor networks to perform adaptive re-clustering. In addition, a new energy balancing strategy was proposed to decentralize energy consumption (assuming sink is the center), that is, introducing a weight coefficient proportional to the distance from the node to the sink in the gateway or cluster head node selection calculation formula. The simulation results of the algorithm showed that, compared with the event-driven clustering ERP routing scheme using K-medoid clustering and energy sensing, under the premise that the number of CRSN nodes is a fixed value, the clustering routing algorithm based on DSEB in the network lifetime and there are certain improvements in utilization and energy efficiency; when the number of primary user nodes is a fixed value, the proposed algorithm has higher spectrum utilization than the other two algorithms.

A Fluorescence Based Dissolved Oxygen Sensor | Open Access

GU Hao, WANG Zhiqiang, WU Hao, JIANG Yongnian, GUO Ya

2020, 2(2): 48-58. doi:10.12133/j.smartag.2020.2.2.202005-SA004

Asbtract ( 1290 )

HTML ( 295)

PDF (1763KB) ( 1220 )

References | Related Articles | Metrics

The measurement of dissolved oxygen content in water is of great significance to aquaculture. However, the dissolved oxygen sensors on the market in China are expensive, and are difficult to maintain continuous online measurement and update parts, so they cannot be widely applied in real production and play expected role in the aquaculture Internet of things(IoT). Based on the principle of fluorescence quenching, a low cost and easy maintenance of dissolved oxygen sensor was developed in this work based on the relationship between the concentration of dissolved oxygen in water and the phase difference of fluorescence signal. The self-made oxygen-sensitive membrane was used to generate red fluorescence which being excited by blue light, and the fluorescence life was regulated by the concentration of dissolved oxygen. Photoelectric conversion circuit with optical signal sensing device was designed to sense optical signal. The STM32F103 microprocessor was used as the main control chip, and the lower computer program was programmed to generate the excitation light pulse. The phase-sensitive detection principle and fast Fourier transform (FFT) were used to calculate the phase difference between the excitation light and the reference light, which was converted into the concentration of dissolved oxygen and realized the measurement of dissolved oxygen. The fluorescence detection part and the main control part of the system were designed as detachable independent modules, and shield lines were used to plug and pull directly, so as to facilitate replacement and maintenance and realize online remote measurement. The testing results showed that, the measurement range of the sensor was 0-20 mg/L, system time delay was less than 2 s, and the life time of the oxygen sensitive membrane would be about 1 year. The dissolved oxygen sensor has the characteristics of convenient measurement, stable result output, low cost and small volume, which will lay a good foundation for the development and marketization of low-cost dissolved oxygen sensors in aquaculture industry of China.

Near-Field Telemetry Detection of Soil Nutrient Based on Modulated Near-Infrared Reflectance Spectrum | Open Access

JIAO Leizi, DONG Daming, ZHAO Xiande, TIAN Hongwu

2020, 2(2): 59-66. doi:10.12133/j.smartag.2020.2.2.202005-SA003

Asbtract ( 718 )

HTML ( 886)

PDF (1790KB) ( 748 )

References | Related Articles | Metrics

Proper soil nutrients content plays an important role in agricultural production—undernutrition would reduce crop yield and quality and overnutrition would cause environmental pollution. Though the traditional approaches based on sampling and chemical analysis can comprehensively and accurately measure soil nutrients, but the soil sampling and pretreatment process are cumbersome, complicated, time-consuming, and costly. Therefore, rapid and accurate measurement of soil nutrients is of great significance for precise fertilizer application, which can increase yield, improve crop quality, and alleviate environmental pollution. Toward this objective, a rapid soil nutrients detection method based on modulated near infrared spectroscopy for active near-field telemetry was proposed, which could effectively minimize effect of sunlight during the measuring process. Eight channels narrow-band laser diodes with wavelengths of 1260, 1310, 1350, 1410, 1450, 1510, 1550 and 1610 nm were selected as active lighting sources for measuring the reflectance of soil samples. Eight channels narrow-band laser diodes were symmetrically placed on a concentric circle. A photodetector with a circular photosensitive area of 5 mm in diameter was placed at the center of the concentric circle to maximize the reception of laser beam reflected by soil. A focusing lens was placed in front of the photodetector to collect the laser beam reflected from the soil sample to increase the sensitivity. The sensing area of the photodetector was located at the focus of the lens. seventy four groups of soil samples with known N content were divided into training set (54 groups) and prediction set (20 groups) for data analysis. The spectral reflectance significantly correlated with soil N content was screened by analyzing the training set based on a general linear model and a quantitative measurement model with R² of 0.97 between the screened spectral reflectance and soil N content was achieve. The predicted soil N content obtained from prediction set based on the established model and the referenced soil N content of the prediction set had a R² of 0.9, indicating that this method has an ability to quickly, as well as accurately detect soil nutrients.

Development and Application of an Intelligent Remote Management Platform for Agricultural Machinery | Open Access

ZHU Dengsheng, FANG Hui, HU Shaoming, WANG Wenquan, ZHOU Yansuo, WANG Hongyan, LIU Fei, HE Yong

2020, 2(2): 67-81. doi:10.12133/j.smartag.2020.2.2.202004-SA006

Asbtract ( 1532 )

HTML ( 1204)

PDF (2701KB) ( 1930 )

References | Related Articles | Metrics

In order to solve problems such as the lack of real-time data in agricultural machinery management, the difficulty in real-time machine operation supervision and the asymmetry of machine service information, an intelligent remote management platform was developed in this research. Firstly, five design principles of a specialized remote agricultural machinery management system: specialization, standardization, cloud platform, modularity and openness were proposed. Based on these principles, a customizable general-purpose intelligent remote management system for agricultural machinery based on intelligent sensing technology, Internet of Things technology, positioning technology, remote sensing technology and geographic information system was designed. Practical modules, including agricultural machinery information-based and location-based services using WebGIS, real-time monitoring and management of machinery operation, basic information management of farmland, basic information management of crops in the field, dispatching management of machinery, subsidy management of machinery, order management of machinery operation were designed and implemented in the platform for users of government agencies, agricultural machinery corporations, machine operators, and farmers. Besides, some key technologies of the platform under the current technical background, including the calculation method of the working area with low-precision GNSS positioning receivers, the analysis of anomality data during the processing of GNSS positioning data, the machine scheduling algorithm development, the integration of sensors were focused， analyzed and implementd. The idea of building the machinery management platform with each individual field as the building block was developed. It can be predicted that the agricultural machinery operation management platform would gradually change from simple operation management to field-level comprehensive management. The research and development of this platform can not only solve current machinery management problems, but also provide basic functions for development of similar machinery management platforms.

Development and Testing of Intelligent Sensing and Precision Proportioning System of Water and Fertilizer Concentration | Open Access

JIN Zhou, ZHANG Junqing, GUO Hongyan, HU Yimin, CHEN Xiangyu, HUANG He, WANG Hongyan

2020, 2(2): 82-93. doi:10.12133/j.smartag.2020.2.2.202003-SA012

Asbtract ( 1153 )

HTML ( 1426)

PDF (2031KB) ( 1300 )

References | Related Articles | Metrics

Water and fertilizer integration technology can effectively improve nutrient utilization efficiency. However, the existing water and fertilizer machines have some shortcomings, such as huge cost, single fertilizer injection, need for cleaning water and so on, which hinder the development of water and fertilizer integration technology. Aiming at the problems of precise and low-cost compounding of compound fertilizer at the local farm, the water and fertilizer integrated intelligent irrigation and fertilization system were taken as the research object. In this research, new concept of an intelligent sensing system was proposed， and accurate proportioning system of water and fertilizer concentration was constructed and implemented. Firstly, a fast on-line method of intelligent sensing model of water and fertilizer was established based on a series of concentration gradient compound fertilizer solutions. The conductivity values of these formulated solutions were tested by contactless conductivity detection electrodes. Subsequently, the data analysis algorithms were discussed and compared to fit regression model. Based on the intelligent sensing model of water and fertilizer , the framework structure of in-situ intelligent sensing and accurate proportioning system of water and fertilizer concentration was designed, and the working principle of the system was also explained. The system proposed includs a first-level water and fertilizer concentration intelligent perception model building subsystem and a second-level water and fertilizer accurate proportioning subsystem. The first-level subsystem was designed as a portable device, which mainly included a precise pump for quantitative dosing, a large-range online conductivity sensor, a plastic bucket and supporting control and model building software. The second-level subsystem was designed as a dynamic and precise fertilizer distribution device. The effectiveness of the system was verified by three types of water intelligent fertilizer application so as to guide the in-situ water and fertilizer concentration ratio. The testing results showed that the second-order polynomial fitting curve under regularization conditions was the best model to express the relationship between the conductivity and the concentration of water and fertilizer, and the correlation coefficients R² was higher than 0.999. Combined with the proportion of each index of compound fertilizer, the concentration of each index of compound fertilizer that the user cares about can be obtained according to this model. The results of three types of water intelligent fertilizer application showed that the conductivity of natural water had an effect on the water and fertilizer system, and the relative deviation was more than 0.1. The online water and fertilizer perception and ratio system proposed in this research realized the elimination of the interference of the local water conductivity on the accuracy of the ratio of water and fertilizer, and the accurate calculation of compound fertilizer was achieved through model calculation. This system has a simple structure and accurate ratio, low cost， and can be easily combined with the existing water and fertilizer integrated machine or artificial fertilizer system. The system could be widely used in facility agriculture, orchard cultivation and field cash crop cultivation, et al.

Stereoscopic Light Environment Intelligent Control System Based on Characteristic Differences of Facility Cucumber Plants Light Requirements | Open Access

ZHANG Zhongxiong, LI Bin, FENG Pan, ZHANG Pan, LAI Haibin, HU Jin, ZHANG Haihui

2020, 2(2): 94-104. doi:10.12133/j.smartag.2020.2.2.202005-SA007

Asbtract ( 774 )

HTML ( 779)

PDF (2566KB) ( 644 )

References | Related Articles | Metrics

Light is the main energy source for plants to carry out photosynthesis, and the quality of light directly affects the yield and quality of crops. In view of the fact that most of the existing plant light supplement systems are based on the photosynthetic capacity of functional leaves, problems such as photoinhibition of new leaves in the canopy and lack of supplementary light in the functional leaf position between plants, and the position of light supplement can’t be adjusted dynamically to adapt to crop growth exist, taking facility cucumber as the research object, an stereo light environment intelligent control system based on the characteristic differences of plant light requirements was designed in this research. The system is composed of intelligent control subsystem, canopy-plant environment monitoring subsystem, canopy-plant LED light-compensating lamp subsystem, and light-compensating lamp lifting subsystem. Wireless communication between subsystems was realized by using ZigBee technology. The canopy-interplant environmental monitoring subsystem obtains the canopy and interplant environmental information respectively and sends them to the intelligent control subsystem. According to the real-time environmental information, the intelligent control subsystem invokes the canopy regulation model and the appropriate interplant leaf position regulation model to obtain the corresponding regulation target values, and sends them to the canopy-interplant light-compensating lamp to realize the dynamic real-time regulation of the canopy and interplant light-compensating lamp. In November 2018, the stereoscopic light-compensating equipment and the traditional canopy light-compensating equipment were tested and verified with the natural control in the vegetable base of the vegetable industry comprehensive service area of Jingyang County, Shaanxi province. The results showed that, compared with the traditional canopy light-compensating area, the cucumber plant height and stem diameter in the stereoscopic light-compensating area increased significantly, and the average plant height and stem diameter increased by 8.03% and 7.24%, respectively. Compared with the natural treatment area, the average plant height and stem diameter increased by 26.51% and 36.03%, respectively. And during the one-month picking period, compared with the traditional canopy light-compensating area, the yield of the stereoscopic light-compensating area increased by 0.28 kg/m², the economic benefit increased by 2.82 CNY/m², the yield of the stereoscopic light-compensating area increased by 1.39 kg/m², and the economic benefit increased by 4.88 CNY/m²; compared with the natural treatment area indicating that the stereoscopic light environment control system can improve economic benefits and has good application and promotion values.

Design and Experimental Research of Long-Term Monitoring System for Bee Colony Multiple Features | Open Access

HONG Wei, XU Baohua, LIU Shengping

2020, 2(2): 105-114. doi:10.12133/j.smartag.2020.2.2.202005-SA001

Asbtract ( 913 )

HTML ( 561)

PDF (1997KB) ( 920 )

References | Related Articles | Metrics

The pollination during bees’ foraging is vital to continue species on the earth. However, bee colonies in some areas of America and Europe frequently appeared colony collapse disorder in the past decade due to many possible factors such as climate change and pesticide usage, which has not received enough attention and positive response from human beings. In this research, bee colony’s activities were investigated with seven detectable features (i.e., weight, temperature, humidity, gas concentration, vibration, sound and entrance counts), and the applicability of the features was evaluated by considering four factors (i.e. the relevance to bee colony’s activities, the richness of information, the cheapness of cost and the simplicity of engineering). Based on the investigation and evaluation, an Internet of Things(IoT) based system was presented for long-time monitoring of bee colonies, which could hourly detect the temperature and humidity inside of hive, bee combs’ weight, bee colony’s sounds and bees’ counts of passing through hive entrance. In this system, each hive has an individual detection device for the monitoring of bee colony, and the colony information could be automatically collected and transferred to a remote cloud server which took responsible for the information storing. Finally, the users could freely visit the server to browse the history data and manage their bee colonies. Moreover, a 235 days continuous monitoring for Apis mellifera ligustica was performed from August, 2019 to April, 2020 to demonstrate the system performance, and long-time and one-day monitoring results were both analyzed. The monitoring results indicated that the system could continuously operate without human intervention, and the data could reveal bee colony’s activity and growth, e.g., the temperature and humidity could reflect the micro climate of the bee hive, the weight could show the forging and stock of food, the sounds contained lots of information about bees’ behavior and the entrance count was strongly related to the activeness and scale of bee colony. Compared with the reported monitoring system, this system is superior in the diversity of detected features, the capability of power self-support and the wireless of data transmission that can benefit to the system’s deployment in the field and long-term operation without maintenance. In the visible future, this system will effectively promote the study related to the biology of bee’s behavior, the reason of colony collapse disorder and the development of precision beekeeping.

Beehive Key Parameters Online Monitoring System and Performance Test | Open Access

YANG XuanJiang, LI Hualong, LI Miao, HU Zelin, LIAO Jianjun, LIU Xianwang, GUO Panpan, YUE Xudong

2020, 2(2): 115-125. doi:10.12133/j.smartag.2020.2.2.202004-SA001

Asbtract ( 912 )

HTML ( 476)

PDF (2059KB) ( 979 )

References | Related Articles | Metrics

With the development of information technology, using big data analysis, monitoring of Internet of Things, sensor perception, wireless communication and other technologies to build a real-time online monitoring system for beehive is a feasible solution for reducing the stress response of bee colony caused by check the beehive artificially. Focusing on situation that real-time monitoring in the closed environment of the beehive is difficult, the STM32F103VBT6 32-bit microcontroller, integrated with the temperature and humidity sensor, microphone, and laser beam sensor were used in this study to develop a low-power, continuous working online monitoring system for the multi-parameter information acquisition and monitoring of beehive key parameters. The system mainly includes core processing module, data acquisition module, data sending module and database server. The data collection module includes a temperature and humidity collection unit inside the beehive, a bee colony sound collection unit, a bee in and out nest number counting unit, etc., and transfers data by accessing the mobile communication network. The performance test results of system on-site deployment showed that the developed system could monitor the temperature and humidity in the beehive in real time, effectively distinguish the bees of entering and leaving the beehive, record the numbers of bees of entering and leaving the nest door, and the bee colony sounds that the automatically obtained were consistent with the standard sound distribution of bee colony. The results indicate that this system meets the design requirements, can accurately and reliably collect the beehive parameters data, and can be used as a data collection method for related research of bee colony.

Multi-Band Image Fusion Method for Visually Identifying Tomato Plant’s Organs With Similar Color | Open Access

FENG Qingchun, CHEN Jian, CHENG Wei, WANG Xiu

2020, 2(2): 126-134. doi:10.12133/j.smartag.2020.2.2.202002-SA001

Asbtract ( 766 )

HTML ( 686)

PDF (1659KB) ( 566 )

References | Related Articles | Metrics

Considering at the robotic management for tomato plants in the greenhouse, it is necessary to identify the stem, leaf and fruit with the similar color from the broad-band visible image. In order to highlight the difference between the target and background, and improve the identification efficiency, the multiple narrow-band image fusion method for identifying the tomato’s three similar-colored organs, including stem, leaf, and green fruit, was proposed, based on the spectral features of these organs. According to the 300-1000 nm spectral data of three organs, the regularized logistic regression model with Lasso for distinguishing their spectral characteristic was built. Based on the sparse solution of the model’s weight coefficients, the wavelengths 450, 600 and 900 nm with the maximum coefficients were determined as the optimal imaging band. The multi-spectral image capturing system was designed, which could output three images of optimal bands from the same view-field. The relationship between the organs’ image gray and their spectral feature was analyzed, and the optimal images could accurately show the organs’ reflection character at the various band. In order to obtain more significant distinctions, the weighted-fusion method based NSGA-II was proposed, which was supposed to combine the organ’s difference in the optimal band image. The algorithm’s objective function was defined to maximize the target-background difference and minimize the background-background difference. The coefficients obtained were adopted as the linear fusion factors for the optimal band images.Finally, the fusion method was evaluated based on intuitional and quantitative indexes, respectively considering the one among stem, leaf and green fruit as target, and the other two as the backgrounds. As the result showed, compared with the single optimal band image, the fused image greatly intensified the difference between the similar-colored target and background, and restrained the difference among the background. Specifically, the sum of absolute difference (SAD) was used to describe the grey value difference between the various organs, and the fusion result images’ SAD between the target and the background raised to 2.02, 8.63 and 7.89 times than the single band images. The Otsu automatic segmentation algorithm could respectively obtain the recognition accuracy of 71.14%, 60.32% and 98.32% for identifying the stem, leaf and fruit on the fusion result image. The research was supposed as a reference for the identification on similar-colored plant organs under agricultural condition.

Estimation Model of Cucumber Leaf Wetness Duration Considering the Spatial Heterogeneity of Solar Greenhouse | Open Access

LIU Jian, REN Aixin, LIU Ran, JI Tao, LIU Huiying, LI Ming

2020, 2(2): 135-144. doi:10.12133/j.smartag.2020.2.2.202001-SA003

Asbtract ( 827 )

HTML ( 309)

PDF (1629KB) ( 640 )

References | Related Articles | Metrics

Leaf wetness duration (LWD) is one of the important input variables of plant disease model, which is related to the infection of many leaf pathogens and affects the pathogen infection and developmental rate. In order to accurately predict the occurrence time and location of cucumber diseases in solar greenhouse, nine sampling points were set up in two different greenhouses located in Beijing in March and September 2019, according to the chessboard method to deploy temperature, humidity and light sensors. The fixed-point visual inspection method was used to collect the data every 1 h. From the leaf wetting to the leaf drying is the leaf wetness duration of a day. The relative humidity model (RHM) and back propagation neural network model (BPNN) were used to quantitatively estimate and analyze the LWD, the input layer of BPNN was temperature, humidity, radiation and location, the hidden layer was 10, and the output layer was location and whether the leaf surface was wet. The results showed that BPNN obtained similar accuracy ACC = 0.90 and 0.92 under the experimental conditions of two greenhouses, which was higher than RHM ACC = 0.82 and 0.84 in estimating of LWD, the mean absolute errors MAE were 1.81 h and 1.61 h, root mean squared error RMSE were 2.10 and 1.87, and coefficient of determination R² were 0.87 and 0.85. In sunny and cloudy conditions, the spatial distribution of LWD was generally in the South > the Middle > the North. In the South, the average LWD was the longest, 12.17 h/d; from the east to the west, the spatial distribution of LWD was generally in the East > the West > the Middle. In the Middle, the average LWD was the shortest of 4.83h/d. The average LWD in rainy days was longer than that in sunny days and cloudy days, the average LWD in spring and autumn rainy days were 17.15 h/d and 17.41 h/d. These changes and differences had an important impact on the distribution of leaf wetness duration in the horizontal direction of cucumber population in greenhouse, which was closely related to the occurrence rule of most high humidity cucumber diseases. In this research, the method of regional analysis of the wet duration of cucumber leaves in greenhouse was proposed, which could provide a reference for simulating the spatial distribution of LWD in greenhouse, and also had a certain reference significance for the establishment of cucumber disease early warning system.

Table of Content