In response to challenges such as outdated agricultural irrigation canal infrastructure and inefficient control and management methods, which result in low water resource utilization, a measurement and control integrated gate irrigation system based on the STM32 embedded microprocessor (STM32) has been developed. The system is based on the "Cloud-Edge-End" collaborative architecture (CEEA), utilizing agricultural Internet of Things (IoT) technologies such as advanced sensors, embedded systems, and wireless communication, and employs fuzzy logic (FL) control. The gate body is designed as a reliable, stable, and adjustable sliding plate structure. The opening and closing mechanism utilize a stepper motor to directly drive the threaded vice, generating a lifting movement of the gate plate. The control system includes key modules such as the main control unit, photovoltaic power supply, data acquisition, wireless communication, motor drive, and other essential components. The cloud platform facilitates remote monitoring and human-machine interaction via both web and mobile terminals, enabling convenient operation and data visualization. The regulation and control of the gate system is based on in-situ edge calculation by the master controller, which integrates the target flow rate with the real-time monitored flow rate. The FL control ensures accurate and stable regulation of the gate opening and flow rate. The performance test and experimental verification (during the rice jointing period) show that the integrated measurement and control gate system offers high regulation and control accuracy. Among them, the relative errors of local control and remote control are about 1 %, and the effect of local control is better than that of remote control. The correlations between the flow rate and the gate opening, as well as between the flow rate and the water level behind the gate are both relatively high. R2 of the latter is as high as 0.99. Additionally, the relative error of the automatic flow control remains within +/- 5 %, indicating that the system exhibits high precision in flow regulation and control. The system is particularly suitable for small and medium sized agricultural irrigation and drainage projects, providing new equipment support for the advancement of modern agricultural water conservation technology.

In response to the current key issues in the field of smart irrigation for farmland, such as the lack of data sources and insufficient integration, a low degree of automation in drive execution and control, and over-reliance on cloud platforms for analyzing and calculating decision making processes, we have developed nodes and gateways for smart irrigation. These developments are based on the EC-IOT edge computing IoT architecture and long range radio (LoRa) communication technology, utilizing STM32 MCU, WH-101-L low-power LoRa modules, 4G modules, high-precision GPS, and other devices. An edge computing analysis and decision model for smart irrigation in farmland has been established by collecting the soil moisture and real-time meteorological information in farmland in a distributed manner, as well as integrating crop growth period and soil properties of field plots. Additionally, a mobile mini-program has been developed using WeChat Developer Tools that interacts with the cloud via the message queuing telemetry transport (MQTT) protocol to realize data visualization on the mobile and web sides and remote precise irrigation control of solenoid valves. The results of the system wireless communication tests indicate that the LoRa-based sensor network has stable data transmission with a maximum communication distance of up to 4 km. At lower communication rates, the signal-to-noise ratio (SNR) and received signal strength indication (RSSI) values measured at long distances are relatively higher, indicating better communication signal quality, but they take longer to transmit. It takes 6 s to transmit 100 bytes at the lowest rate of 0.268 kbps to a distance of 4 km, whereas, at 10.937 kbps, it only takes 0.9 s. The results of field irrigation trials during the wheat grain filling stage have demonstrated that the irrigation amount determined based on the irrigation algorithm can maintain the soil moisture content after irrigation within the suitable range for wheat growth and above 90% of the upper limit of the suitable range, thereby achieving a satisfactory irrigation effect. Notably, the water content in the 40 cm soil layer has the strongest correlation with changes in crop evapotranspiration, and the highest temperature is the most critical factor influencing the water requirements of wheat during the grain-filling period in the test area.

悬臂式多级离心泵被广泛应用于国民经济的各个领域,鉴于其独特的结构形式,其振动相关研究十分重要.采用数值模拟的方法,重点分析了"干态"下悬臂式多级离心泵的响应性能.研究表明,随着不平衡力矩增大,泵体的振动幅度越大,尤其是泵启动后2.6～3.0 s内径向位移的不稳定突变明显.基于SAMCEF谐响应分析,发现二阶振型为扭转耦合振动,首级叶轮在二阶临界转速时的响应幅值随不平衡质量相位变化影响较大.当首级叶轮不平衡质量相位为180°时,螺母与首级叶轮的一阶加速度以及位移幅值达到最小.因此,通过提高零件的加工精度减小零件的不平衡力矩以及错开各级叶轮不平衡质量的相位可以减小转子径向振动,从而提高悬臂式离心泵的运行稳定性.

The high-flux acquisition of crop growth information can be realized using field monitoring robotic platforms. However, most of the existing agricultural monitoring robots have been converted from expensive commercial platforms, and they thus have a hard time adapting to the farmland working environment, let alone satisfying the basic requirements of sensor testing. To address these problems, a wheeled crop-growth-monitoring robot that features the accurate, nondestructive, and efficient acquisition of crop growth information was developed based on the cultivation characteristics of wheat, the obstacle characteristics of the wheat field, and the monitoring mechanism of spectral sensors. By analyzing the phenotypic structural change characteristics and the requirements for the row spacing of different wheat varieties throughout the growth period, a four-wheel mobile chassis was designed with an adjustable wheel track and a high-clearance body structure that can effectively eliminate the risk of the robot destroying the wheat during operation. Moreover, considering the requirements for wheeled robots to overcome obstacles in field operations, a three-dimensional (3D) model of the robot was created in Pro/E. Models of obstacles in the field (e.g., pits and bumps) were created in Adams to simulate the operational stability of the robot. The simulation results showed that the mass center displacement of the robot was smaller than 0.2 cm on flat pavement and the maximum mass center displacement was 1.78 cm during obstacle crossing (10 cm deep pits and 10 cm high bumps). The field test showed that the robot equipped with active-light-source crop growth sensors achieved stable, real-time, nondestructive, and accurate acquisition of the canopy vegetation parameters—NDVI (normalized difference vegetation index) and RVI (ratio vegetation index)—and the wheat growth parameters—LAI (leaf area index), LDW (leaf dry weight), LNA (leaf nitrogen accumulation), and LNC (leaf nitrogen content).

Given the problem that droplets cannot stay on the surfaces of leaves and wet them effectively, resulting in high levels of pesticide input and environmental pollution, this work studied the dynamic behaviors of droplets with different diameters (400–550 um) falling on the surfaces of wheat leaves from different heights (2–16 cm) using contact angle-measuring instruments and a high-speed camera. The VOF method in Fluent software was used to establish a numerical model of droplets impacting the surfaces of wheat leaves. The results show that with an increase in the initial diameter and initial velocity of a droplet, the maximum diameter of the droplet during the spreading process also gradually increases. After a droplet impacts a wheat leaf, the droplet-spreading diameter first increases and then decreases. The maximum droplet spreading rate, βmax, increases with an increase in the Weber number, βmax ∈We14, which is consistent with the existing theory. The results of this study lay a foundation for studying the spread of droplets on the surfaces of leaves, which is conducive to improving the rate of pesticide utilization.

Soil profile moisture is a crucial parameter of agricultural irrigation. To meet the demand of soil profile moisture, simple fast-sensing, and low-cost in situ detection, a portable pull-out soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of a moisture-sensing probe and a data processing unit. The probe converts soil moisture into a frequency signal using an electromagnetic field. The data processing unit was designed for signal detection and transmitting moisture content data to a smartphone app. The data processing unit and the probe are connected by a tie rod with adjustable length, which can be moved up and down to measure the moisture content of different soil layers. According to indoor tests, the maximum detection height for the sensor was 130 mm, the maximum detection radius was 96 mm, and the degree of fitting (R2) of the constructed moisture measurement model was 0.972. In the verification tests, the root mean square error (RMSE) of the measured value of the sensor was 0.02 m3/m3, the mean bias error (MBE) was ±0.009 m3/m3, and the maximum error was ±0.039 m3/m3. According to the results, the sensor, which features a wide detection range and good accuracy, is well suited for the portable measurement of soil profile moisture.

The field mobile platform is an important tool for high-throughput phenotype monitoring. To overcome problems in existing field-based crop phenotyping platforms, including limited application scope and low stability, a rolling adjustment method for the wheel tread was proposed. A self-propelled three-wheeled field-based crop phenotyping platform with variable wheel tread and height above ground was developed, which enabled phenotypic information of different dry crops in different development stages. A three-dimensional model of the platform was established using Pro/E; ANSYS and ADAMS were used for static and dynamic performance. Results show that when running on flat ground, the platform has a vibration acceleration lower than 0.5 m/s2. When climbing over an obstacle with a height of 100 mm, the vibration amplitude of the platform is 88.7 mm. The climbing angle is not less than 15°. Field tests imply that the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) of a canopy measured using crop growth sensors mounted on the above platform show favorable linear correlations with those measured using a handheld analytical spectral device (ASD). Their R2 values are 0.6052 and 0.6093 and root-mean-square errors (RMSEs) are 0.0487 and 0.1521, respectively. The field-based crop phenotyping platform provides a carrier for high-throughput acquisition of crop phenotypic information.

针对扇形喷嘴雾化特性问题,在Ansys Fluent中基于Taylor Analogy Breakup(TAB)破碎模型,采用Eulerian-Lagrangian连续相与离散相耦合算法,实现了扇形喷嘴的液滴破碎、雾化形成及气液两相流场的非定常数值模拟,完成了喷射压力与喷雾高度2个参数对扇形喷嘴液滴速度、液滴直径、离散相模型(DPM)质量浓度、液滴通量N等雾化特性参数影响的研究,通过激光粒度仪在试验台上得到了液滴索特平均直径D S M,并与模拟结果进行了对比.研究结果表明:随着喷射压力的升高,液滴的速度越大,液滴在计算域内平均停留时间越短,在计算域停留的液滴数越少;液滴的索特平均直径DSM、液滴体积中值直径DVM、数量中值直径DNM随着喷射压力的升高越来越小,喷射压力为0.3 MPa后液滴DSM减小的趋势变大,这有利于改善实际作业中的雾化质量,当然在有风状态下也会加大雾滴飘逸的风险.喷雾高度对液滴DSM影响不大.不同喷射压力下DPM质量浓度以及喷雾的覆盖面积不受喷射压力的影响,由于N的变化与液滴DSM呈三次方,与覆盖面积A成反比关系,液滴的数量通量随着喷射压力的变大而逐渐变大.DPM的质量浓度随着喷雾高度的升高而逐渐降低,喷雾的覆盖面积随着喷雾高度的升高而逐渐变大.由于液滴的DSM随喷雾高度的变化可忽略不计,因此液滴数量通量随着喷雾高度的增加而逐渐变小.不同喷射压力下和不同喷雾高度下试验和模拟计算所得到的DSM变化趋势一致,整个过程的误差不超过10％.

Grain quality involves the appearance, nutritional, and safety attributes of grains. With the improvement of people’s living standards, problems pertaining to the quality of grains have received greater attention. Modern quality detection techniques feature unique advantages including rapidness, non-destructiveness, accuracy, and efficiency in detecting grain quality. This review summarizes research progress of these techniques in detection of quality indices of grains. Particularly, the review focuses on detection techniques based on physical properties including acoustic, optical, thermal, electrical, and mechanical properties, and those simulating sensory analysis such as electronic noses, electronic tongues, and electronic eyes. According to the current technological development and application, the challenges and prospects of these techniques are demonstrated.

Canopy spectral reflectance can indicate both crop nutrient and canopy structural information. Differences in canopy structure can affect spectral reflectance. However, a non-imaging spectrometer cannot distinguish such differences while monitoring crop nutrients, because the results are likely to be influenced by the canopy structure. In addition, nitrogen application rate is one of the main factors influencing the canopy structure of crops. Strong correlations exist between indices of canopy structure and leaf nitrogen, and thus, these can be used to compensate for the spectral monitoring of nitrogen content in wheat leaves. In this study, canopy structural indices (CSI) such as wheat coverage, height, and textural features were obtained based on the RGB and height images obtained by the RGB-D camera. Moreover, canopy spectral reflectance was obtained by an ASD hyperspectral spectrometer, based on which two vegetation indices—ratio vegetation index (RVI) and angular insensitivity vegetation index (AIVI)—were constructed. With the vegetation indices and CSIs as input parameters, a model was established to predict the leaf nitrogen content (LNC) and leaf nitrogen accumulation (LNA) of wheat based on partial least squares (PLS) and random forest (RF) regression algorithms. The results showed that the RF model with RVI and CSI as inputs had the highest prediction accuracy for LNA, the coefficient of determination (R2) reached 0.79, and the root mean square error (RMSE) was 1.54 g/m2. The vegetation indices and coverage were relatively important features in the model. In addition, the PLS model with AIVI and CSI as input parameters had the highest prediction accuracy for LNC, with an R2 of 0.78 and an RMSE of 0.35%, among the vegetation indices. In addition, parts of both the textural and height features were important. The results suggested that PLS and RF regression algorithms can effectively integrate spectral and canopy structural information, and canopy structural information effectively supplement spectral information by improving the prediction accuracy of vegetation indices for LNA and LNC.

A near-infrared (NIR) spectrometer can perceive the change in characteristics of the grain reflectance spectrum quickly and nondestructively, which can be used to determine grain quality information. The full-band spectral information of samples of multiple physical states can be measured using existing instruments, yet it is difficult for the full-band instrument to be widely used in grain quality detection due to its high price, large size, non-portability, and inability to directly output the grain quality information. Because of the above problems, a phenotypic sensor about grain quality was developed for wheat, and four wavelengths were chosen. The interference of noise signals such as ambient light was eliminated by the phenotypic sensor using the modulated light signal and closed sample pool, the shape and size of the incident light spot of the light source were determined according to the requirement for collecting the reflectance spectrum of the grain, and the luminous units of the light source with stable light intensity and balanced luminescence were developed. Moreover, the sensor extracted the reflectance spectrum information using a weak optical signal conditioning circuit, which improved the resolution of the reflectance signal. A grain quality prediction model was created based on the actual moisture and protein content of grain obtained through Physico-chemical analyses. The calibration test showed that the R2 of the relative diffuse reflectance (RDR) of all four wavelengths of the phenotypic sensor and the reflectance of the diffusion fabrics were higher than 0.99. In the noise level and repeatability tests, the standard deviations of the RDR of two types of wheat measured by the sensor were much lower than 1.0%, indicating that the sensor could accurately collect the RDR of wheat. In the calibration test, the root mean square errors (RMSE) of protein and moisture content of wheat in the Test set were 0.4866 and 0.2161%, the mean absolute errors (MAEs) were 0.6515 and 0.3078%, respectively. The results showed that the NIR phenotypic sensor about grain quality developed in this study could be used to collect the diffuse reflectance of grains and the moisture and protein content in real-time.

The aim of this study is to overcome disturbance of downwash flow field caused by the low-altitude operation of a multirotor unmanned aerial vehicle (UAV) on crop canopies and interference in spectral reflection information of canopies. For this purpose, a crop growth sensor aboard a fixed-wing UAV was developed through flight dynamics simulation analysis of a fixed-wing UAV. This sensor can collect index data on-line and in real-time including: the ratio vegetation index (RVI) of crop leaves, leaf area index (LAI), leaf dry weight (LDW), and leaf nitrogen content (LNC). Flight dynamics simulation analysis of the fixed-wing UAV was conducted by the automatics dynamic analysis of mechanical system (ADAMS) software to obtain the deflection angle of the UAV during flight. According to the flight characteristics and load on the UAV, a ball rolling-type sensor support was designed to ensure that the crop growth sensor is always aimed vertically downwards in-flight. The field test results show that the crop growth sensor aboard the fixed-wing UAV has good dynamic stability and high measurement accuracy. The RVIs measured by the onboard crop growth sensor in the plots and field were fitted with the results measured by a FieldSpec HandHeld 2 spectroradiometer (ASD, Analytical Spectral Device Co., USA). By analysing the fitted results, the coefficients of determination (R2) are 0.763 and 0.833 and the root mean square errors (RMSEs) are 0.16 and 0.17, respectively. By linearly fitting RVIs measured by the UAV with rice growth indices including LAI, LDW, and LNC, the coefficients of determination (R2) are 0.633, 0.581, and 0.528 and RMSEs are 0.18, 0.18, and 0.21, respectively.

为了提升高速井泵的水力性能,探讨影响其性能的主次因素,以100QJ10型高速井泵为研究对象,按照L18(37)正交表,选取叶片出口宽度、叶轮出口直径、叶片数等7个因素,每个因素选取3个水平,共设计18组叶轮,并分别与同一个导叶装配.应用CFX 15.0软件对18组模型泵进行全流场数值模拟,利用极差分析法研究影响100QJ10型高速井泵性能的主要和次要因素.结果表明:叶片出口安放角和叶轮出口边斜切角度对高速井泵的水力性能影响较大;本次优化最优方案为叶轮出口宽度b2=6.5 mm,叶轮出口直径D2=80.5 mm,叶片出口安放角β2=27°,叶片数Z=7,叶片进口直径D1=40 mm,叶轮后盖板与反导叶最底端轴向间距h=3.5 mm,叶轮出口斜切角度为0°.分别对初始模型和优化模型进行外特性试验并对比分析,验证了正交试验结合数值模拟方法在高速井泵优化设计方面的可行性.

Single-modal images carry limited information for features representation, and RGB images fail to detect grass weeds in wheat fields because of their similarity to wheat in shape. We propose a framework based on multi-modal information fusion for accurate detection of weeds in wheat fields in a natural environment, overcoming the limitation of single modality in weeds detection. Firstly, we recode the single-channel depth image into a new three-channel image like the structure of RGB image, which is suitable for feature extraction of convolutional neural network (CNN). Secondly, the multi-scale object detection is realized by fusing the feature maps output by different convolutional layers. The three-channel network structure is designed to take into account the independence of RGB and depth information, respectively, and the complementarity of multi-modal information, and the integrated learning is carried out by weight allocation at the decision level to realize the effective fusion of multi-modal information. The experimental results show that compared with the weed detection method based on RGB image, the accuracy of our method is significantly improved. Experiments with integrated learning shows that mean average precision ( mAP ) of 36.1% for grass weeds and 42.9% for broad-leaf weeds, and the overall detection precision, as indicated by intersection over ground truth ( IoG ), is 89.3%, with weights of RGB and depth images at α = 0.4 and β = 0.3. The results suggest that our methods can accurately detect the dominant species of weeds in wheat fields, and that multi-modal fusion can effectively improve object detection performance.

Nitrogen is an important nutrient element for crop growth. A timely understanding of plant nitrogen information helps to adopt appropriate agricultural production management to maintain high yield and quality in wheat production. Crop spectral monitoring technology can obtain the leaf nitrogen content (LNC) information of wheat quickly and nondestructively. However, optical radiation interacts with the atmosphere, the canopy, and the soil before being captured by the sensor, and the capability to intercept, reflect, and transmit the radiation is different for different canopy structures. In wheat LNC monitoring, differences in target canopy structures will lead to changes in canopy reflectance, which can affect the monitoring accuracy. In this study, RGB and depth images were used to obtain wheat canopy structure indices. By analyzing the correlations between wheat LNC, canopy structure indices, and spectral reflectance, the indices that had large influence on spectral reflectance were screened; the change dynamics of these canopy structure indices under different internal and external factors were compared, and factors with greater impact were selected as the basis for grouping. On this basis, this study used the spectral indices RVI (660, 815) and RVI (730, 815) as fixed-effect variables and wheat population grouping variables as random-effect variables to construct linear mixed models of wheat LNC. In addition, the spectral indices and canopy structure indices were used as input parameters and wheat population grouping variables as output parameters to construct a random forest classifier for wheat canopy types. When monitoring the target population, we first predicted the classification of the unknown canopy by the classifier. The classification results and spectral indices were then input into the linear mixed models to realize the prediction of wheat LNC. The R2 of the prediction for wheat LNC with RVI (660, 815) and RVI (730, 815) increased from 0.57 and 0.71 to 0.76 and 0.80, respectively, and the RRMSE decreased from 20.86% and 17.33% to 15.58% and 14.20%, respectively. This study used digital image information to compensate for spectral information, broadened the amount of information used for the remote sensing monitoring of farmland, and effectively improved the accuracy and universality of wheat LNC monitoring.

为降低泄漏流对双螺杆液力透平效率的影响,以2/3齿双螺杆泵透平为例,根据双螺杆泵内齿间间隙的构成原理,首先建立其简化几何模型,利用透平腔内存在相对运动引起的剪切流动与各级腔室压差导致的压差流动理论,建立起齿间泄漏通道的数学模型.使用SCORG和Pumplinx软件对双螺杆液力透平进行全流场数值模拟,得到了双螺杆透平不同齿间间隙下的流场分布和流量变化规律.研究结果表明:不同间隙下螺杆的压力分布规律保持基本一致,每个密封腔室内的压力分布基本均匀,从进口到出口各相邻腔室的压力呈线性下降趋势;螺杆在齿间间隙附近出现多处泄漏通道,在各腔室压差的作用下,第一级腔内没有出现明显的高流速区域,从第二级腔内开始发生明显的高速泄漏;随着齿间间隙的不断增大,同一级腔内的齿侧间隙泄漏面积、泄漏速度以及流体从进口到出口的泄漏流量和容积损失都随之增大,当齿间间隙为0.04,0.08,0.12,0.16和0.20 mm时,由该间隙所造成的容积损失的值分别为4.03％,4.57％,5.00％,5.43％和5.72％.

从组成部分、理论研究、数值计算研究以及试验研究4个方面介绍离心泵水动力噪声的研究现状.在国内外学者研究工作的基础上,从内声场和外声场2个方面详细概述了离心泵水动力噪声的组成部分及影响因素的研究现状,其中内声场从噪声的频谱特性可分为离散噪声和宽带噪声,离散噪声主要是由于动静干涉导致,能量集中在叶频及其谐频;而宽带噪声的研究目前还有很多工作待开展.FW-H方程是声比拟理论中计算离心泵水动力噪声的基本方程,应用广泛,同时涡声理论也表现出其很好的发展前景.基于声比拟法的混合数值模拟是目前离心泵水动力噪声模拟的主流途径,而诸如线性欧拉法(LEE)等声传播方程法的应用相对较少,未来随着对离心泵中高频段噪声研究的深入,声传播方程法也将会得到应用.用水听器构建双端口模型,可以准确测得离心泵低频段噪声,但该方法对试验条件要求较高,成本较大;麦克风阵列法在测量离心泵内部声场中的高频段噪声成分时具有良好的研究前景.最后提出在未来的研究中需要解决的难题以及值得关注的方向.

A numerical model was developed to determine the water drop movement and mean droplet size diameter at any distance from a sprinkler as a function of nozzle size and pressure. Droplet size data from 4, 5, 6, and 7 mm nozzle sizes verified the model. Data for model prediction were generated throughout lab experiments. The results demonstrated that the correlation between the observed and predicted droplet size diameter values for all the nozzle sizes and pressures is quite good. Nozzle size and pressure had a major influence on droplet size. Higher pressure produced smaller droplets over the entire application profile. The wetted distance downwind from the sprinkler increased as wind velocity increased, for example at a constant working pressure of 300 kPa, at wind speeds of 3.5 m/s and 4.5 m/s, 20% and 32% of the total volume exceeded the wet radius respectively. Larger droplets (3.9–4.5 mm), accounting for 3.6% and 6.3% of the total number of distributed droplets, respectively. The model can also predict the droplet size distribution at any wind direction overall the irrigated pattern.

Compared with ordinary azo dye pollutants, metal complex azo dyes are usually more recalcitrant and have more serious harm to our environment. This study focused on biodecolorization of acid blue 193 (AB 193), a typical metal complex azo dye, by Shewanella oneidnesis MR-1 (MR-1) under anaerobic conditions with neutral red (NR) as electron shuttle. The results indicated that effective biodecolorization of AB 193 by MR-1 was dependent on the presence of NR as electron shuttle. At presence of 1-5 mu M NR, MR-1 could successfully decolorize AB 193, but hardly decolorized it without NR added. NR was found to be able to significantly improve decolorization capacity of MR-1 by accelerating electron transfer between dyes and cells. And the optimal biodecolorization parameters gotten by response surface methodology (RSM) were temperature 30.1oC, pH 7.0, 52.7 mg/L of initial AB 193 concentration and 3.5 mu M of NR dosage, respectively to achieve maximum AB 193 decolorization (95.98%) after 144 h. Furthermore, the phytotoxicity of AB 193 to rice was significantly reduced during the decolorization process.

Drought is a natural phenomenon caused by the variability of climate. This study was conducted in the Songhua River Basin of China. The drought events were estimated by using the Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI) which are based on precipitation (P) and potential evapotranspiration (PET) data. Furthermore, drought characteristics were identified for the assessment of drought trends in the study area. Short term (3 months) and long term (12 months) projected meteorological droughts were identified by using these drought indices. Future climate precipitation and temperature time series data (2021–2099) of various Representative Concentration Pathways (RCPs) were estimated by using outputs of the Global Circulation Model downscaled with a statistical methodology. The results showed that RCP 4.5 have a greater number of moderate drought events as compared to RCP 2.6 and RCP 8.5. Moreover, it was also noted that RCP 8.5 (40 events) and RCP 4.5 (38 events) showed a higher number of severe droughts on 12-month drought analysis in the study area. A severe drought conditions projected between 2073 and 2076 with drought severity (DS-1.66) and drought intensity (DI-0.42) while extreme drying trends were projected between 2097 and 2099 with drought severity (DS-1.85) and drought intensity (DI-0.62). It was also observed that Precipitation Decile predicted a greater number of years under deficit conditions under RCP 2.6. Overall results revealed that more severe droughts are expected to occur during the late phase (2050–2099) by using RDI and SPI. A comparative analysis of 3- and 12-month drying trends showed that RDI is prevailing during the 12-month drought analysis while almost both drought indices (RDI and SPI) indicated same behavior of drought identification at 3-month drought analysis between 2021 and 2099 in the research area. The results of study will help to evaluate the risk of future drought in the study area and be beneficial for the researcher to make an appropriate mitigation strategy.