MONITORING CROP GROWTH STATUS BASED ON OPTICAL SENSOR

Abstract: In order to detect the growth status and predict the yield of the crop, crop growth monitor measuring nitrogen content in the plant is developed based on optical principle. The monitor measures the spectral reflectance of the plant canopy at the 610 nm and 1220 nm wavebands, and then calculates the nitrogen content in the plant with the measured data. The field test was carried out to evaluate performance of the monitor. A portable multi-spectral radiometer named Crop Scan was used to measure the reflectance as a reference instrument. The result shows that the leaf reflectance measured by the monitor has a close linear correlation with that measured by Crop Scan at the 610 nm waveband (R2 = 0.7604), but the correlation between them is needed to be improved at the 1220 nm waveband. The hardware and the software of the monitor are also explained in detail. It is still need to be improved to satisfy the demand of ground-based remote sensing in precision farming

Estimation of Soil Total Nitrogen and Soil Moisture Based on NIRS Technology

Part 1: GIS, GPS, RS and Precision FarmingInternational audienceEstimation model between soil moisture content and the near infrared reflectance was established by the linear regression method and the models between soil total nitrogen content and the near infrared reflectance were also established by the BP neural network method and Support Vector Machine (SVM) method. Forty-eight soil samples were collected from China Agricultural University Experimental Farm. After the soil samples were taken into the laboratory, NIR absorbance spectra were rapidly measured under the original conditions by the FT-NIR (Fourier Transform Near Infrared Spectrum) analyzer. At the same time the soil moisture (SM) and soil total nitrogen (TN) were measured by the laboratory analysis methods. The results of the study showed that a linear regression method achieved an excellent regress effect for soil moisture. The correlation coefficient of the calibration (RC) was 0.88, and the correlation coefficient of the validation (RV) was 0.85. The model was passed F test and t test. For soil total nitrogen, the model effect of BP neural network was better than that of SVM method, and the correlation coefficient of the calibration (RC) coefficient and the validation (RV) was 0.92 and 0.88, respectively. Both RMSE and PMSE were low. The results provided an important reference for the development of a portable detector

Application of Color Featuring and Deep Learning in Maize Plant Detection

Maize plant detection was conducted in this study with the goals of target fertilization and reduction of fertilization waste in weed spots and gaps between maize plants. The methods used included two types of color featuring and deep learning (DL). The four color indices used were excess green (ExG), excess red (ExR), ExG minus ExR, and the hue value from the HSV (hue, saturation, and value) color space, while the DL methods used were YOLOv3 and YOLOv3_tiny. For practical application, this study focused on performance comparison in detection accuracy, robustness to complex field conditions, and detection speed. Detection accuracy was evaluated by the resulting images, which were divided into three categories: true positive, false positive, and false negative. The robustness evaluation was performed by comparing the average intersection over union of each detection method across different sub–datasets—namely original subset, blur processing subset, increased brightness subset, and reduced brightness subset. The detection speed was evaluated by the indicator of frames per second. Results demonstrated that the DL methods outperformed the color index–based methods in detection accuracy and robustness to complex conditions, while they were inferior to color feature–based methods in detection speed. This research shows the application potential of deep learning technology in maize plant detection. Future efforts are needed to improve the detection speed for practical applications

A New Coupled Elimination Method of Soil Moisture and Particle Size Interferences on Predicting Soil Total Nitrogen Concentration through Discrete NIR Spectral Band Data

Rapid and accurate measurement of high-resolution soil total nitrogen (TN) information can promote variable rate fertilization, protect the environment, and ensure crop yields. Many scholars focus on exploring the rapid TN detection methods and corresponding soil sensors based on spectral technology. However, soil spectra are easily disturbed by many factors, especially soil moisture and particle size. Real-time elimination of the interferences of these factors is necessary to improve the accuracy and efficiency of measuring TN concentration in farmlands. Although, many methods can be used to eliminate soil moisture and particle size effects on the estimation of soil parameters using continuum spectra. However, the discrete NIR spectral band data can be completely different in the band attribution with continuum spectra, that is, it does not have continuity in the sense of spectra. Thus, relevant elimination methods of soil moisture and particle size effects on continuum spectra do not apply to the discrete NIR spectral band data. To solve this problem, in this study, moisture absorption correction index (MACI) and particle size correction index (PSCI) methods were proposed to eliminate the interferences of soil moisture and particle size, respectively. Soil moisture interference was decreased by normalizing the original spectral band data into standard spectral band data, on the basis of the strong soil moisture absorption band at 1450 nm. For the PSCI method, characteristic bands of soil particle size were identified to be 1361 and 1870 nm firstly. Next, normalized index Np, which calculated wavelengths of 1631 and 1870 nm, was proposed to eliminate soil particle size interference on discrete NIR spectral band data. Finally, a new coupled elimination method of soil moisture and particle size interferences on predicting TN concentration through discrete NIR spectral band data was proposed and evaluated. The six discrete spectral bands (1070, 1130, 1245, 1375, 1550, and 1680 nm) used in the on-the-go detector of TN concentration were selected to verify the new method. Field tests showed that the new coupled method had good effects on eliminating interferences of soil moisture and soil particle size

A Vehicle-mounted Crop Detector with Wireless Sensor Network

In order to detect crop chlorophyll content in real-time, a new vehicle-mounted detector for measuring crop canopy spectral characteristics was developed. It was designed to work as a wireless sensor network with several optical sensor nodes and one control unit. All the optical sensor nodes were mounted on an on-board mechanical structure so that they could collect the canopy spectral data while in mobile condition. Each optical sensor node was designed to contain four optical channels, which allowed it work at the wavebands of 550, 650, 766 and 850 nm. The control unit included a PDA (Personal Digital Assistant) device with a ZigBee wireless network coordinator and a GPRS module. It was used to receive, display, store all the data sent from optical sensor nodes and send data to the server through GPRS module. The calibration tests verified the stability of the wireless network and the measurement precision of the sensors. Both stationary and moving field experiments were also conducted in a winter wheat experimental field. Results showed that the correlation between chlorophyll content and vegetation index had high significance with the highest R2 of 0.6824. Those results showed the potential of the detector for field application

Development and Performance Test for a New Type of Portable Soil EC Detector

International audienceThe soil electrical conductivity (EC) refers to the capability for soil to conduct current. It is a comprehensive reflection of soil salinity and moisture. Therefore, acquiring soil EC rapidly and accurately can provide better guidance for farming production. Based on improving four-electrode method, a new portable soil EC detector with six electrodes was developed and its performance was tested. Inside two electrodes and outside two electrodes were used to measure soil EC near the surface and in deeper soil, respectively. And middle two electrodes were used to input a constant current to soil. The stability tests of the current source showed that the amplitude fluctuation was less than 3%

Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy

Visible-near-infrared spectrum (Vis-NIR) spectroscopy technology is one of the most important methods for non-destructive and rapid detection of soil total nitrogen (STN) content. In order to find a practical way to build STN content prediction model, three conventional machine learning methods and one deep learning approach are investigated and their predictive performances are compared and analyzed by using a public dataset called LUCAS Soil (19,019 samples). The three conventional machine learning methods include ordinary least square estimation (OLSE), random forest (RF), and extreme learning machine (ELM), while for the deep learning method, three different structures of convolutional neural network (CNN) incorporated Inception module are constructed and investigated. In order to clarify effectiveness of different pre-treatments on predicting STN content, the three conventional machine learning methods are combined with four pre-processing approaches (including baseline correction, smoothing, dimensional reduction, and feature selection) are investigated, compared, and analyzed. The results indicate that the baseline-corrected and smoothed ELM model reaches practical precision (coefficient of determination (R2) = 0.89, root mean square error of prediction (RMSEP) = 1.60 g/kg, and residual prediction deviation (RPD) = 2.34). While among three different structured CNN models, the one with more 1 × 1 convolutions preforms better (R2 = 0.93; RMSEP = 0.95 g/kg; and RPD = 3.85 in optimal case). In addition, in order to evaluate the influence of data set characteristics on the model, the LUCAS data set was divided into different data subsets according to dataset size, organic carbon (OC) content and countries, and the results show that the deep learning method is more effective and practical than conventional machine learning methods and, on the premise of enough data samples, it can be used to build a robust STN content prediction model with high accuracy for the same type of soil with similar agricultural treatment

Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe

Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343⁻2222 nm), apparent electrical conductivity (ECa), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, ECa, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties

Development and Application of a Vehicle-Mounted Soil Texture Detector

It is of great significance to obtain soil texture information quickly for the realization of farmland management. Soil with good particle condition can well regulate the needs of plants for water, nutrients, air, and temperature during crop growth, thereby promoting high crop yields. The existing methods of measuring soil texture cannot meet the requirements of time and spatial resolution. For this reason, a vehicle-mounted soil texture detector was designed and developed based on machine vision and soil electrical conductivity devices. The detector does not require pretreatment such as air-drying and screening of the soil, and completely uses the original information of the farmland. The whole process can obtain the soil texture information in real time, omitting the complicated chemical process, and saving manpower and material resources. The vehicle-mounted detector is divided into a mechanical part, a control part, and a display part. The mechanical part provides measurement support for the acquisition of soil texture information; the control part collects and processes signals and images; the measurement results can be intuitively observed and recorded on the display, and can be operated through the mobile phone. The vehicle-mounted detector obtains soil conductivity through 4 disc electrodes, while the vehicle-mounted industrial camera captures the soil surface image, and extracts texture parameters through image processing, takes EC and texture parameters as input, and the embedded SVM model of the instrument was used to perform soil texture prediction. In order to verify the measurement accuracy of the detector, farmland verification experiments were carried out on farmland loam in Tongzhou District and Haidian District of Beijing. The R2 of the correlation between the measured value of soil EC and the actual value was 0.75, and the accuracy of soil texture prediction was 84.86%. It shows that the developed vehicle-mounted soil texture detector can meet the requirements for rapid acquisition of farmland texture information

Real Time Detection of Soil Moisture in Winter Jujube Orchard Based on NIR Spectroscopy

International audienceThe measurement and control of soil moisture are the key technologies of precision agriculture. In order to real-time detect soil moisture content faster and more accurately, a portable soil moisture sensor based on NIR spectroscopy was developed. With the sixty soil samples collected from a winter jujube orchard, a linear regression model was established. The determination coefficients of the calibration ($R^2_c$) and validation ($R^2_v$) reached 0.88 and 0.92, respectively. The model passed F-test and t-test and showed robust. Subsequently, two spatial distribution maps of soil moisture were generated based on the data obtained by the portable soil moisture detector and the data obtained by oven drying method, respectively. Finally, the correlation between these two maps was investigated by using the software of Surfer 8.0. The zones of dry and wet soil could be distinguished easily in both maps. The results of the study showed that the developed detector was practical