Crop Growth Status Research Articles

Semitransparent Organic and Perovskite Photovoltaics for Agrivoltaic Applications

Greenhouse structures offer the ability to control the microclimate of plants, enabling year-round crop cultivation and precision agriculture techniques. To maintain optimal crop growth conditions, substantial energy is required to...

Using Date Palm Waste as an Alternative for Rockwool: Sweet Pepper Performance under Both Soilless Culture Substrates.

The degradation of soil quality due to environmental conditions and improper management practices has caused a shrinkage in land areas suitable for crop cultivation. This necessitates a transition towards soilless culture systems, which offer desirable conditions for crop growth and development and increase resource use efficiency. One of the growth-limiting factors in soilless culture systems is the type of growing substrate. The use of more sustainable resources and environmentally friendly growing substrates is a challenge that affects the soilless culture industry. This work evaluates the efficacy of date palm waste (DPW) and rockwool as growing substrates for sweet pepper (Capsicum annuum L.) under greenhouse conditions. The plant height, stem diameter, average total leaf area, φPSII, and Fm' of leaf fluorescence show significant increases when plants are grown in rockwool. No differences are found in terms of the total yield or the number of marketable fruits and fruit quality between the two substrates. However, the DPW substrate shows a significant decrease in the number of unmarketable fruits and number of Blossom End Rot (BER) fruits. Plants grown in both growing substrates consume equal water amounts for the optimal fruit production, while the water use efficiency of rockwool is better than that of DPW. Our results highlight DPW's role in soilless production and as a key solution for resource-saving production systems.

Energy Partitioning and Latent Heat Flux Driving Factors of the CAM Plant Pineapple (Ananas comosus (L.) Merril) Grown in the South Subtropical China.

Elucidation of different vegetation energy partitioning and environmental control factors at the agro-ecosystem levels is critical for better understanding and scientific management of farmland. Pineapple (Ananas comosus (L.) Merril) is a tropical plant widely cultivated in the southern subtropical region of China; however, the energy partitioning of crassulacean acid metabolism (CAM) plants like pineapple and their interactions with the environment remain not well understood. In this study, we investigated the energy partitioning patterns of pineapple fields and latent heat flux (LET) response to environmental factors using the Bowen ratio energy balance system and meteorological observation field data. The results showed that the CAM plant pineapple energy partitioning was significantly different from the common C3 and C4 crops during the study period, which was mainly attributed to the complex interactions between CAM plant transpiration and the environment. Specifically, sensible heat flux was the main component of net radiation (Rn), followed by the LET, accounting for 65.0% and 30.8% of the Rn, respectively. Soil heat flux accounts for a very small fraction (4.2%). The mean values of the Bowen ratio were 2.09 and 1.41 for sunny and cloudy days during the daytime and 0.74 and 0.46 at night, respectively. LET is a key factor in responding to crop growth status and agricultural water management, and the path analysis indicates that its variation is mainly influenced directly by Rn with a direct path coefficient of 0.94 on sunny days, followed by vapor pressure deficit, air temperature and relative humidity, which indirectly affect LET through the Rn pathway, whereas soil moisture and wind speed have a low effect on LET. On cloudy days, the effect of Rn on LET was overwhelmingly dominant, with a direct path coefficient of 0.91. The direct path coefficients of the remaining factors on LET were very small and negative. Overall, this study is an important reference for enhancing the impact of pineapple as well as CAM plants on the surface energy balance and regional climate.

Prediction of vertical distribution of SPAD values within maize canopy based on unmanned aerial vehicles multispectral imagery.

Real-time monitoring of canopy chlorophyll content is significant in understanding crop growth status and guiding precision agricultural management. Remote sensing methods have demonstrated great potential in this regard. However, the spatiotemporal heterogeneity of chlorophyll content within crop canopies poses challenges to the accuracy and stability of remote sensing estimation models. Hence, this study aimed to develop a novel method for estimating canopy chlorophyll content (represented by SPAD values) in maize (Zea mays L.) canopies. Firstly, we investigated the spatiotemporal distribution patterns of maize canopy SPAD values under varying nitrogen application rates and different growth stages. The results revealed a non-uniform, "bell-shaped" curve distribution of maize canopy SPAD values in the vertical direction. Nitrogen application significantly influenced the distribution structure of SPAD values within the canopy. Secondly, we achieved satisfactory results by fitting the Lorentz peak distribution function to the SPAD values of different leaf positions in maize. The fitting performance, evaluated using R2 and RMSE, ranged from 0.69 to 0.98 and 0.45 to 3.59, respectively, for the year 2021, and from 0.69 to 0.77 and 2.38 to 6.51, respectively, for the year 2022.Finally, based on the correlation between canopy SPAD values and vegetation indices (VIs) at different growth stages, we identified the sensitive leaf positions for the selected CCCI (Canopy Chlorophyll Index) in each growth stage. The 6th (r = 0.662), 4th (r = 0.816), 12th (r = 0.722), and 12th (r = 0.874) leaf positions exhibited the highest correlations. Compared to the estimation model using canopy wide SPAD values, the model based on sensitive leaf positions showed improved accuracy, with increases of 34%, 3%, 20%, and 3% for each growth stage, respectively. In conclusion, the findings of this study contribute to the enhancement of chlorophyll content estimation models in crop canopies and provide valuable insights for the integration of crop growth models with remote sensing methods.

Development of an applicability and performance evaluation tool based on energy simulation for smart greenhouse cooling packages

Rapid population growth and a surge in the demand for agriculture products have necessitated improvements in food security and sustainable food production. Shortening the fallow period due to high temperature through cooling in a smart greenhouse during summer, extending the crop harvest period, and improving productivity can help achieve this goal. Cooling during summers requires excessive cooling energy for maintaining optimal crop growth conditions. Therefore, for effective decision-making in introducing and controlling the cooling system, a greenhouse model that accurately predicts the process of mass and energy change, including the effect of crops, and an adequate evaluation tool are necessary. In this study, three cooling packages applicable to smart greenhouses were proposed, and a greenhouse energy model realizing plant transpiration was developed using EnergyPlus. Subsequently, 1,296 simulation datasets were constructed based on the developed model and a combination of four design variables: installation area, scale, crops, and covering materials. Therefore, a cooling package applicability and performance evaluation tool was developed based on the simulation datasets through three evaluation indices: temperature control, humidity control, and energy cost. The developed evaluation tool can enable to select an efficient cooling package for sustainable crop production.

Potato Leaf Chlorophyll Content Estimation through Radiative Transfer Modeling and Active Learning

Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. This study was carried out based on a potato nitrogen and potassium fertilizer gradient experiment in the year 2022 at Keshan Farm, Qiqihar Branch of Heilongjiang Agricultural Reclamation Bureau. Leaf hyperspectral and leaf chlorophyll content data were collected at the potato tuber formation, tuber growth, and starch accumulation periods. The PROSPECT-4 radiative transfer model was employed to construct a look-up table (LUT) as a simulated data set. This was accomplished by simulating potato leaves’ spectral reflectance and chlorophyll content. Then, the active learning (AL) technique was used to select the most enlightening training samples from the LUT based on the measured potato data. The Gaussian process regression (GPR) algorithm was finally employed to construct the inversion models for the chlorophyll content of potato leaves for both the whole and single growth periods based on the training samples selected by the AL method and the ground measured data of the potatoes. The R2 values of model validation accuracy for the potato whole plantation period and three single growth periods are 0.742, 0.683, 0.828, and 0.533, respectively with RMSE values of 4.207, 4.364, 2.301, and 3.791 µg/cm2. Compared with the LCC inversion accuracy through LUT with a cost function, the validation accuracies of the GPR_PROSPECT-AL hybrid model were improved by 0.119, 0.200, 0.328, and 0.255, and the RMSE were reduced by 3.763, 2.759, 0.118, and 5.058 µg/cm2, respectively. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato LCC estimation, which provides a valid tool for accurately monitoring crop growth and growth health.

Low-Temperature Fermented Straw Compost Regulates Rice Growth and Yield by Affecting Soil Physicochemical Properties and the Expression of Important Signaling Pathway Genes

Soil physicochemical properties affect crop growth and yield. The addition of fertilizers can improve the soil quality during crop cultivation, leading to increased agricultural production. Organic fertilizers may be produced by composting straw that would otherwise be discarded as agricultural waste, with potential implications for sustainable agricultural development. However, the mechanism underlying the effects of straw compost on crop growth is unknown. In this study, a microbial agent suitable for straw decomposition in cold regions was used for a large-scale biological fermentation. Organic compost was obtained after the decomposition of straw. The straw compost was mixed with soil in different proportions and then used to cultivate Songjing 2 rice plants. The addition of straw compost significantly increased the growth and yield of the rice plants and enhanced various physiological indices. Moreover, the straw compost treatment significantly improved soil physicochemical properties (e.g., pH, enzyme activity, nutrient composition, and microbial diversity) and optimized the soil conditions for crop growth. In addition, the application of straw compost influenced the expression of genes in rice metabolic pathways as well as pathways mediating secondary metabolite synthesis and plant hormone signal transduction. The study data reflect the potential applicability of low-temperature straw fermentation technology for maximizing crop production.

Design and implementation of intelligent monitoring system for agricultural environment in IoT

In the context of population growth and limited resources, the efficiency and sustainability of agricultural production became particularly important. Smart agricultural technology and Internet of Things (IoT) technology have helped farmers with better management tools to improve crop yield and quality. This research takes the IoT as the core and combines sensor, actuator and cloud platform technologies to build a smart greenhouse control system. Environmental parameters such as temperature, humidity, and light are obtained through sensors. And it is uploaded to the cloud platform for storage and analysis. At the same time, light, ventilation, water, and fertilizer inside the greenhouse are controlled automatically by our system to achieve the best crop growth condition. System design adopts a modular design concept. We separately realize the functions of sensor, actuator and cloud platform, where data is transmitted via wireless communication and integrated together. The experimental results show that the cloud control system of smart agricultural greenhouse can effectively monitor the greenhouse environment, optimize efficiency of energy and resource utilization, and help farmers manage greenhouse conveniently and quickly.

Maximizing wheat yield through soil quality enhancement: A combined approach with Azospirillum brasilense and bentonite

Maintaining soil fertility to obtain higher crop production is essential for food security and sustainability. Soil amendments not only improve soil fertility but also enhance plant growth. Azospirillum brasilense is an important plant growth-promoting rhizobacteria participating in the progression of wheat (Triticum aestivum) yield; however, how it works in bentonite soil for enhancing wheat production is not clear yet. To investigate the combined applications of Azospirillum brasilense and bentonite, a planned experiment was performed in the wirehouse. Through studying various agronomic, physiological, and soil-related parameters, it was found that the sole application of Azospirillum brasilense is more beneficial as compared to the sole application of ben-tonite. Bentonite application at 10 g kg−1 of soil is more beneficial for the wheat crop growth and soil fertility status as compared to the bentonite application at 5 g kg−1 of soil. Co-application of Azospirillum brasilense and bentonite at 10 g kg−1 of soil increases the grain yield by 42 %, LAI by 20.2 %, and Stomatal conductance by up to 13.88 %. The resulting increase was due to an increase in the soil organic matter contents, microbial activities, growth hormones production, and more availability of soil water for plant growth.

Inversion of Leaf Water Content of Cinnamomum camphora Based on Preferred Spectral Index and Machine Learning Algorithm

Plant leaf water content significantly influences photosynthetic efficiency and crop yield. Leaf water content (LWC) and equivalent water thickness (EWT) are indicators that reflect the water state within plant tissues, and they play a crucial role in assessing plant water supply and usage. In recent years, there has been a growing focus on the rapid and precise determination of plant water content. In this study, Cinnamomum camphora (C. camphora) was chosen as the subject of investigation. After acquiring spectral data, three types of vegetation indices were computed: the empirical vegetation index, the random combination dual-band vegetation index, and the ‘trilateral’ parameter. Four groups of optimal spectral index screening strategies were established, namely an empirical vegetation index group (G1), a random combination dual-band vegetation index group (G2), a ‘trilateral’ parameter group (G3), and a mixed group (G4). Three algorithms, specifically random forest (RF), radial basis function neural network (RBFNN), and support vector machine (SVM), were employed for the estimation of leaf water content (LWC) and equivalent water thickness (EWT) in mature C. camphora. The results demonstrated that the G4 group displayed superior performance, yielding five optimal spectral indices for LWC: water index (WI), optimized soil-adjusted vegetation index (OSAVI), difference vegetation index (DVI) at wavelengths 734 and 956 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1009 and 774 nm, and red-edge amplitude (Dr). With regard to EWT estimation, the five optimal spectral indices encompassed the red-edge normalized difference vegetation index (RE-NDVI), simple ratio water index (SRWI), difference vegetation index (DVI) at wavelengths 700 and 1167 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1182 and 1514 nm, and red-edge area (SDr). Utilizing these indices as inputs significantly enhanced the accuracy of the models, with the RF model emerging as the most effective for estimating LWC and EWT in C. camphora. Based on the LWC estimation model of the G4 group and the RF algorithm, the determination coefficient (R2) for both the training and test sets reached 0.848 and 0.871, respectively. The root mean square error (RMSE) was 0.568% for the training set and 0.582% for the test set, while the average relative error (MRE) stood at 0.806% and 0.642%, respectively. Regarding the EWT estimation model, R2 values of 0.887 and 0.919 were achieved for the training and test sets, accompanied by RMSE values of 0.6 × 10−3 g·cm−2 and 0.7 × 10−3 g·cm−2, and MRE values of 3.198% and 2.901%, respectively. These findings lay a solid foundation for hyperspectral moisture monitoring in C. camphora and offer valuable reference for the rapid assessment of crop growth status.

Hydrological consequences of controlled drainage with subirrigation

Controlled drainage with subirrigation (CD-SI) could be a viable measure to cope with the imbalance in water demand and supply in groundwater dependent regions. CD-SI has the potential to i) retain, ii) recharge and iii) discharge groundwater, i.e. anticipate both dry and wet weather extremes. The aim of this paper is to specify the hydrological consequences of CD-SI at field scale. Four field CD-SI pilots with varying geohydrological conditions in Dutch sandy Pleistocene uplands were monitored (minimum 5 years) to study the effects on groundwater level, soil moisture content and soil water potential. Dynamic modelling using the SWAP-model, calibrated with PEST, was used to quantify all water balance components for the field pilots, needed for responsible implementation of CD-SI. Using SWAP-PEST, measured groundwater levels and soil moisture conditions were reproduced sufficiently accurate. Both the water supply rates and the water and crest levels in the control pit of CD-SI systems could be simulated dynamically, which is an important improvement to earlier modelling approaches. Model calculations were used to study the hydrological responses of CD-SI for meteorological dry to wet years and for each of the field sites. Simulations show that transpiration increases with 38 to 206 mm in dry years for sites with respectively low and higher hydraulic resistances in the subsoil. Simulations also show that the required water volume can be large (664 – 728 mm, respectively). CD-SI could thus improve hydrological conditions for crop growth, but the success depends on subtle differences in geohydrologic characteristics.

Fine Particulate Matter Reduction through Crop Surface Adsorption in an Agricultural Field Using the Coefficient Derived from Wind Tunnel Experiments

Fine dust can have serious effects on human health and crop growth. Fodder crops can reduce airborne dust by coagulating soil particles and reducing wind speed on the surface and have the effect of reducing fine dust by adsorbing it on the crop surface. In this study, the dust reduction coefficient of crops was derived through a self-manufactured wind tunnel experiment to quantitatively assess the dust reduction effect of crops by type and planting density. Additionally, a dust reduction formula considering crop growth and weather conditions during the cultivation period was derived. The dust reduction coefficient was measured by the gravimetric method and the real-time size distribution of dust concentration before and after the crop. The PM reduction coefficient showed triticale at PM-2.5 82.2 mg/m3, PM-10 120 mg/m3, and barley at PM-2.5 14.5 mg/m3, PM-10 26.9 mg/m3 under moderate planting density. During the general planting density cultivation period of triticale and barley, PM-10 was reduced by 37.8 kg/ha and 8.5 kg/ha, respectively, and PM-2.5 was reduced by 25.9 kg/ha and 4.6 kg/ha. The dust reduction effect during the cultivation period was up to 126.1 kg/ha in terms of PM-10 when triticale was cultivated with densely sowing planting density.

Semantic segmentation for plastic-covered greenhouses and plastic-mulched farmlands from VHR imagery

ABSTRACTDue to their important role in maintaining temperature and soil moisture, agricultural plastic covers have been widely utilized around the globe for improving crop-growing conditions, which include both plastic-covered greenhouses (PCGs) and plastic-mulched farmlands (PMFs). However, it is a challenging and long-neglected issue to separate PCGs from PMFs due to their spectral similarity. The objective of this study is to propose a deep semantic segmentation model for accurate PCG and PMF mapping based on very high-resolution satellite images and to improve the model’s spatial generalization capability using a transfer learning strategy. Specifically, the proposed semantic segmentation model has an encoder-decoder structure, where the encoder is composed of a new convolutional neural network for discriminative spatial feature learning, while the decoder utilizes a multi-task strategy to improve the predictions on the boundaries. Meanwhile, a transfer learning framework is adopted to increase mapping performance and generalization ability under limited samples. Experimental results in several typical regions across the Eurasian continent show that the proposed model could separate PCGs from PMFs accurately with a mean overall accuracy of 94.49% and an average mIoU of 0.8377. Ablation studies verify the role of encoder-decoder and transfer learning strategy in improving classification performance.

Definition of thermal comfort of crops within naturally ventilated greenhouses

Controlling the microclimate condition inside a greenhouse is very important to ensure the best indoor conditions for both crop growth and crop production. To this regard, this paper provides the results of a novel approach to study a greenhouse, aiming to define a porous media model simulating the crop presence. As first, an experimental campaign has been carried out to evaluate air temperature and air velocity distributions in a naturally ventilated greenhouse with sweet pepper plants cultivated in pots. Then, the main aspects of energy balance, in terms of mass transfer and heat exchange, and both indoor and outdoor climate conditions have been combined to set up a computational fluid dynamics model. In the model, in order to simulate the crop presence and its effects, an isotropic porous medium following Darcy’s law has been defined based on the physical characteristics of the crops. The results show that the porous medium model could accurately simulate the heat and mass transfer between crops, air, and soil. Moreover, the adoption of this model helps to clarify the mechanism of thermal exchanges between crop and indoor microclimate and allows to assess in more realistic ways the microclimate conditions close to the crops.

Diagnosing crop water status based on canopy temperature as a function of film mulching and deficit irrigation

Film mulching with deficit irrigation is considered an effective way to maximize water productivity and ensure a stable crop yield in the arid region of Northwest China. Deficit irrigation requires precise knowledge of crop response to drought conditions, whereas the canopy temperature and the related crop water stress index (CWSI) monitor crop water stress status. Although canopy temperature and CWSI are closely related to meteorological conditions, cultivation measure and crop growing conditions, it is unclear how film mulching influences the canopy temperature and CWSI. A two-year field experiment was conducted in a maize field with three irrigation levels and different film mulching conditions, i.e., non-mulching, plastic film mulching and biodegradable film mulching treatments, in the Shiyang River Basin of Northwest China. The study aimed to quantify the effects of film mulching on canopy temperature and CWSI, as well as to compare CWSI values obtained from the nearby meteorological station data (CWSI_M), and in situ (field) measurements (CWSI_I). The results showed that plastic film and biodegradable film significantly (p < 0.05) reduced the canopy temperature. The non-water-stressed baseline based on the air temperature and relative humidity of the nearby meteorological station was significantly (p < 0.05) different from those of the field measurement at both the vegetative and the reproductive stages. No significant differences in the non-water-stressed baselines (p > 0.05) were observed among different film mulching conditions. CWSI was calculated using the air temperature and relative humidity data from the aforementioned two sources. The results revealed showing that CWSI_I better correlated with the crop yields of various treatments when compared to CWSI_M. Irrigation scheduling based on CWSI_I could save 83 mm of irrigation amount. This underscore the preference of using CWSI_I (if available) as the primary choice for irrigation scheduling. These findings highlight the impact of meteorological and planting conditions on the canopy temperature, non-water-stressed baseline and CWSI, which may be valuable for diagnosing crop water status more accurately by regional water managers.

Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining

The calculation of volumes for irregular bodies holds significant relevance across various production processes. This spans tasks such as evaluating the growth status of crops and fruits, conducting morphological analyses of spatial objects based on volume parameters, and estimating quantities for earthwork and excavation. While methods like drainage, surface reconstruction, and triangulation suffice for smaller irregular bodies, larger ones introduce heightened complexity. Technological advancements, such as UAV photogrammetry and LiDAR, have introduced efficient point cloud data acquisition methods, bolstering precision and efficiency in calculating volumes for substantial irregular bodies. Notably, open-pit mines, characterized by their dynamic surface alterations, exemplify the challenges posed by large irregular bodies. Ensuring accurate excavation quantity calculations in such mines is pivotal, impacting operational considerations, acceptance, as well as production cost management and project oversight. Thus, this study employs UAV-acquired point cloud data from open-pit mines as a case study. In practice, calculating volumes for substantial irregular bodies often relies on the point cloud slicing method. However, this approach grapples with distinguishing multi-contour boundaries, leading to inaccuracies. To surmount this hurdle, this paper introduces an enhanced point cloud slicing method. The methodology involves segmenting point cloud data at fixed intervals, followed by the segmentation of slice contours using the Euclidean clustering method. Subsequently, the concave hull algorithm extracts the contour polygons of each slice. The final volume calculation involves multiplying the area of each polygon by the spacing and aggregating these products. To validate the efficacy of our approach, we employ model-derived volumes as benchmarks, comparing errors arising from both the traditional slicing method and our proposed technique. Experimental outcomes underscore the superiority of our point cloud volume calculation method, manifesting in an average relative error of 1.17%, outperforming the conventional point cloud slicing method in terms of accuracy.

Design and experimentation of soil organic matter content detection system based on high-temperature excitation principle

With the continuous development of precision agriculture, the integration of sensor technology, control technology, and agricultural production activities has become increasingly tight. Soil organic matter (SOM) content is an important parameter in precision agriculture, closely related to the growth status of crops. Therefore, in-situ detection of SOM is an important part of variable seeding. Currently, most researchers use spectroscopy to detect SOM. However, the accuracy of spectroscopy is affected by factors such as soil texture, soil moisture content, soil iron oxide content, and soil particle size. Additionally, spectroscopic instruments are expensive and complex. To address these issues, this study designed and developed a real-time detection system for soil organic matter content based on the high-temperature excitation principle. The SOM detection system includes the selection and application of carbon dioxide sensor, the design of detection devices, the design of control systems, and the design of human–machine interface. Benchtop tests showed that the system's response time standard deviation was 0.252 s, indicating good stability. The filtering and cooling effects of the system met the requirements of the detection system. Five different natural soils were collected, and two of them, along with organic soil, were used to create artificially graded soil samples. Multiple linear regression was used for modeling, selecting six models with an R2 greater than 0.8 for accurate prediction of the remaining three different soil textures. The modeling results showed that a heating depth of 10 mm was susceptible to external interference, and the accuracy of the model with a heating depth of 15 mm was similar to that of the model with a heating depth of 20 mm. When the heating time exceeded 10 s, both models with different heating depths had an R2 greater than 0.8. The accuracy of the models increased with increasing heating time. Among them, the 15 mm-20 s model had the highest accuracy, with an average prediction accuracy of 91.4 %, a maximum accuracy of 94.1 %, and a minimum accuracy of 86.7 %. The experimental results showed that the SOM detection system designed in this study had good predictive capabilities for SOM in different soil textures and could achieve in-situ detection of SOM.

Crop monitoring and biomass estimation based on downscaled remote sensing data in AquaCrop model (case study: Qazvin Plain, Iran).

Ensuring food security requires monitoring crop growth conditions prior to harvesting. This research encompass three stages: remote sensing, crop modeling, and establishing a connection between the two. Besides, Fusion satellite images have been implemented to monitor and investigate fodder corn in three study areas. In the remote sensing stage, after implementing the downscaling algorithm and producing the leaf area index (LAI) time series, the results were compared with the estimated values from Landsat 8 and MODIS images, which were associated with overestimation in all cases. Furthermore, the results exhibited statistically significance with R2 > 95% and P-value < 0.05. The AquaCrop model was first calibrated and implemented in the crop modeling section at each growth stage based on the observational data measured in each field. The accuracy of the simulated model was checked according to the results of statistical indicators with high accuracy (NRMSE = 10% and RMSE = 0.03 (Ton/ha)) at significant level (95%) and was associated with underestimation. Using the SVM decision support algorithm, the relationship between downscaled LAI and calibrated CC (crop canopy) was estimated. This relationship was generated with 70% of the data and subsequently validated using the remaining 30% (R2 = 0.99, NRMSE = 0.01). On this basis, CC values were predicted. Finally, biomass values were compared with observed biomass values. According to the results of statistical indicators (RMSE = 0.19 (Ton/ha), NRMSE = 0.01, R2 = 0.96, P-value < 0.05), biomass estimation was highly accurate. These results demonstrate the reliability and accuracy of the model and the proposed method for simulating and estimating biomass before harvesting.

Riverine Bathymetry Survey Using Unmanned Aerial Vehicle-Based Multispectral Image

River basin surveys are conducted with the aim of providing essential foundational information for the formulation of water management policies, as mandated by relevant laws and regulations. These surveys cover key investigation areas necessary for river basin management, including basic conditions, water conveyance, dimensions, environmental ecology, and more. Among the survey methods, the utilization of remote sensing data, such as drone monitoring imagery and satellite imagery, is employed for various purposes such as the safety management of hydraulic structures like dams and embankments, water quality monitoring, river terrain surveys, and assessments of changes in riverbeds. Recently, research in river basin studies has been conducted using hyperspectral imagery, which includes hundreds of spectral bands, in addition to standard RGB imagery. Hyperspectral imagery offers the advantage of high spectral resolution, making it suitable for multi-parameter assessments. However, it comes with the drawback of large initial data volumes and complex preprocessing requirements due to the abundance of spectral information. On the other hand, multispectral imagery, which collects spectral information from fewer than ten bands, is widely used, especially in agriculture and forestry. It allows for immediate monitoring of parameters like the normalized difference vegetation index (NDVI) using just two bands and facilitates the analysis of crop growth status and more. Research on bathymetric estimation using hyperspectral imagery has traditionally relied on the Optimal Band Ratio Analysis (OBRA), which utilizes band ratios highly correlated with depth to construct bathymetric maps. In this study, we applied the existing hyperspectral bathymetric estimation technique to multispectral imagery to assess the feasibility of bathymetric estimation using reduced spectral bands. We captured multispectral imagery and constructed bathymetric maps to evaluate the applicability of multispectral imagery in river basin applications. Furthermore, to overcome the limitations of traditional OBRA, we employed Gaussian mixture models for image clustering to improve the accuracy of bathymetric estimation.

Design and Testing of an Automatic Strip-Till Machine for Conservation Tillage of Corn

Successive years of straw mulching and returning straw to the fields in Northeast China have made strip-tillage necessary, and reasonable strip-tillage operations can create conditions for crop growth. However, there are limited research studies on the related equipment applicable to this area. In this paper, an automatic control strip-tillage machine is designed. According to the conventional planting pattern of maize in this region, the operative processes of the machine were determined, and a suitable strip seedbed structure could then be constructed under straw mulching conditions. The type of coulters and the structural parameters of the V-type soil-crushing wheel were determined through theoretical analysis. Based on the air spring and electric linear actuator, the plowing depth control system and the straw width control system were developed, respectively, so as to improve the stability of the machine operation. Field tests showed that when the forward speed, tillage depth, and theoretical width were 6–12 km/h, 6–12 cm, and 18–24 cm, respectively, the straw clearing rate, soil crushing rate, and tillage depth and breadth stability were higher than 90%, and the soil flatness was less than 2 cm. All the indexes satisfied the agronomic and technological requirements of corn cultivation. The results of this study can provide equipment and technical support for the further popularization of conservation tillage technology.