Crop Height Research Articles

Using structure-from-motion to estimate cover crop biomass and characterize canopy structure

ContextVariability in biomass production poses a challenge for growers when using cover crops for weed control. However, most methods for assessing cover crop biomass are laborious and impractical on a field scale. ObjectiveThe goal of the present study was to determine the feasibility of using Structure-from-Motion (SfM) photogrammetry to estimate biomass in cereal rye (Secale cereale L.) and winter wheat (Triticum aestivum L.) cover crops by correlating biomass with 3-D point cloud pixel density and crop height. MethodsPoint clouds were generated using a SfM algorithm from RGB (red, green, and blue) videos collected by a hand-held GoPro camera over sixteen crop fields in North Carolina, Iowa, and Maryland, USA, throughout two growing seasons (2021–2023). Crop height, leaf area index (LAI), and photosynthetically active radiation (PAR) were also measured. ResultsBiomass was positively correlated with crop height for both cereal rye (R2 = 0.621) and wheat (R2 = 0.55). LAI was positively correlated with biomass accumulation and crop height for both species, increasing linearly in rye and exponentially in wheat. Conversely, PAR penetration below the canopy decreased with biomass accumulation and crop height in both species, with a more rapid extinction in wheat than rye. Point cloud pixel density showed a positive linear relationship with biomass in rye but saturated after 2.5 tonnes ha−1 (2500 kg ha−1). In wheat, point cloud pixel density was weakly and negatively correlated with biomass due to a denser canopy causing faster saturation of tissue detection by SfM point clouds. However, considering crop height and point cloud density integrating them both in the model allowed obtaining a positive relationship with biomass through levels of 8 tonnes ha−1 (8000 kg ha−1) in both species. When models were validated with independent data, predicted and measured biomass were positively correlated for both rye (R2 = 0.86) and wheat (R2 = 0.78). ConclusionsBased on the results, using SfM to generate 3-D point clouds can provide a more accurate estimation of biomass than canopy height alone by capturing species-level differences in canopy architecture. ImplicationsThe results of this study suggest that SfM can potentially be used as a non-destructive tool for growers to monitor biomass production in cereal cover crops other systems such as energy/forage crops, which can help inform management decisions and conserve resources.

Harvesting Route Detection and Crop Height Estimation Methods for Lodged Farmland Based on AdaBoost

Addressing the challenge of the current harvester route detection method’s reduced robustness within lodging-affected farmland environments and its limited perception of crop lodging, this paper proposes a harvesting operation image segmentation method based on SLIC superpixel segmentation and the AdaBoost ensemble learning algorithm. This segmentation enables two essential tasks. Firstly, the RANSAC algorithm is employed to extract the harvester’s operational route through straight-line fitting from the segmented image. Secondly, the method utilizes a 3D point cloud generated by binocular vision, combined with IMU information for attitude correction, to estimate the height of the harvested crop in front of the harvester. Experimental results demonstrate the effectiveness of this method in successfully segmenting the harvested and unharvested areas of the farmland. The average angle error for the detected harvesting route is approximately 1.97°, and the average error for crop height detection in the unharvested area is around 0.054 m. Moreover, the algorithm exhibits a total running time of approximately 437 ms. The innovation of this paper lies in its simultaneous implementation of two distinct perception tasks, leveraging the same image segmentation results. This approach offers a robust and effective solution for addressing both route detection and crop height estimation challenges within lodging-affected farmland during harvesting operations.

Effect of varying UAV height on the precise estimation of potato crop growth.

A phenotyping pipeline utilising DeepLab was developed for precisely estimating the height, volume, coverage and vegetation indices of European and Japanese varieties. Using this pipeline, the effect of varying UAV height on the precise estimation of potato crop growth properties was evaluated. A UAV fitted with a multispectral camera was flown at a height of 15m and 30m in an experimental field where various varieties of potatoes were grown. The properties of plant height, volume and NDVI were evaluated and compared with the manually obtained parameters. Strong linear correlations with R2 of 0.803 and 0.745 were obtained between the UAV obtained plant heights and manually estimated plant height when the UAV was flown at 15m and 30m respectively. Furthermore, high linear correlations with an R2 of 0.839 and 0.754 were obtained between the UAV-estimated volume and manually estimated volume when the UAV was flown at 15m and 30m respectively. For the vegetation indices, there were no observable differences in the NDVI values obtained from the UAV flown at the two heights. Furthermore, high linear correlations with R2 of 0.930 and 0.931 were obtained between UAV-estimated and manually measured NDVI at 15m and 30m respectively. It was found that UAV flown at the lower height had a higher ground sampling distance thus increased resolution leading to more precise estimation of both the height and volume of crops. For vegetation indices, flying the UAV at a higher height had no effect on the precision of NDVI estimates.

Effect of molybdenum trioxide nanoparticle-mediated seed priming on the productivity of green gram (Vigna radiata L.)

A field experiment was undertaken in the post-Rabi season of 2019-2020 to reveal the response of greengram (Vigna radiata L.) to seed dressing and seed priming with nano molybdenum trioxide (MoO3). The experiment was laid out in randomized block design (RBD) consisting of 10 different treatments i.e., M0 (no seed treatment with Mo); M1 (seed dressing with Sodium molybdate @ 400 ppm); M2, M3, M4, and M5 (seed dressing with nano Molybdenum trioxide-MoO3 @ 50, 100, 200 and 400 ppm, respectively); and M6, M7, M8 and M9 (seed priming with nano MoO3 @ 50, 100, 200 and 400 ppm, respectively). Inoculation of greengram seeds cv. Shreya (IPM 2-14) with Rhizobium sps. was undertaken in all treatments as per the recommended practice, except in M0 (control). The MoO3 nanoparticles (NPs) synthesized from Ammonium molybdate through calcination at 600 0C for 5 hours indicated globular-shaped NPs of 68.55 nm in TEM and XRD. Nanopriming with MoO3 @ 200 ppm (M8) was most promising in recording significantly superior growth and yield attributing parameters and yield, whereas Mo (control) produced the least. Crop height, number of branches, root length, shoot dry matter, pods/plant and seeds/plant and root nodulation at harvest in M8 were 39.4% and 22.6%; 39% and 5.6%; 23% and 9.3%; 43.9%, and 16.3%; 28.2% and 5.3%; 28.1% and 0.8%, and 73.3% and 36.5% higher than M0 (control) and M1 (farmers’ practice), respectively. Superior growth and yield attributing characters in M8 treatment produced the highest grain and stover yield of 0.88 and 3.74 t/ ha that was 32.53% and 8.37%, and 35.5% and 14.7% higher than M0 (control) and M1 (farmers’ practice), respectively. Seed priming with nano MoO3 @ 400 ppm (M9) and seed dressing with nano MoO3 @ 400 ppm (M5) were of second and third order in recording grain and stover yield but M0 recorded the lowest among all the treatments.

Cutting height of main crop has profound effects on cadmium but not arsenic concentration of ratoon crop in rice

ContextRice contamination with heavy metals seriously threatens human health. Ratoon rice plays a vital role in food security in China, whereas limited information is available on its heavy metal accumulation. Research questionCutting height of main crop is an important crop management practice affecting the growth and yield of ratoon crop (RC), whereas its effects on heavy metals accumulation in RC is still unknown. MethodsField experiments were conducted in 2019 and 2020 to investigate the effects of different cutting heights (high: 45 cm, medium: 15 or 25 cm, and low: 5 cm) on cadmium (Cd) and arsenic (As) concentrations in RC. ResultsCutting height had small and inconsistent effects on As concentration in RC. However, grain Cd concentration of RC in high and medium cutting heights were significantly reduced by 87.5 % and 79.8 % than that in low cutting height, respectively. Similarly, Cd concentrations of stubble and straw at maturity were also significantly lower in high and medium cutting heights than in low cutting height. Regenerated tillers in the low node position were responsible for the differences in Cd concentration among the cutting heights. ConclusionThe increase in cutting height of main crop significantly decreased Cd concentration but has small effects on As concentration in RC. ImplicationsOur results suggest that high cutting height during the harvest of main crop should be considered when rice ratooning is practiced in the environment with Cd-contaminated soil.

A Study on Leveraging Unmanned Aerial Vehicle Collaborative Driving and Aerial Photography Systems to Improve the Accuracy of Crop Phenotyping

Unmanned aerial vehicle (UAV)-based aerial images have enabled a prediction of various factors that affect crop growth. However, the single UAV system leaves much to be desired; the time lag between images affects the accuracy of crop information, lowers the image registration quality and a maximum flight time of 20–25 min, and limits the mission coverage. A multiple UAV system developed from our previous study was used to resolve the problems centered on image registration, battery duration and to improve the accuracy of crop phenotyping. The system can generate flight routes, perform synchronous flying, and ensure capturing and safety protocol. Artificial paddy plants were used to evaluate the multiple UAV system based on leaf area index (LAI) and crop height measurements. The multiple UAV system exhibited lower error rates on average than the single UAV system, with 13.535% (without wind effects) and 17.729–19.693% (with wind effects) for LAI measurements and 5.714% (without wind effect) and 4.418% (with wind effects) for crop’s height measurements. Moreover, the multiple UAV system reduced the flight time by 66%, demonstrating its ability to overcome battery-related barriers. The developed multiple UAV collaborative system has enormous potential to improve crop growth monitoring by addressing long flight time and low-quality phenotyping issues.

Role of biochar on improvement of physical characteristics of re-vegetated ex coal mined soil and production of black corn (Zea mays L. var. Black Aztek)

Ex coal mined soil generally had problem for farming activities, even though it had been re-vegetated for few years. This research was aimed to determine the effect of biochar derived from corn cob to improve the physical characteristics of ex coal mined soil and production of black corn. A pot trial was conducted in Glasshouse at Andalas University, Padang. Soil was taken from re-vegetated of ex coal mined soil in Sawah Lunto, West Sumatra, Indonesia. The soil was mixed with biochar at 4 different doses (0; 0.5; 1.0; and 1.5%) of the weight basis. The experimental units were allocated based on Completely Randomized Design. Parameters analyzed were soil texture, bulk density (BD), total soil pore (TSP), hydraulic conductivity (HC), soil organic matter (SOM), and plant available water (PAT), as well as corn production parameters. The result showed that biochar significantly increased the SOM. SOM content at 1.5% biochar application increased by 32.4% compared to the OM content of the soil without biochar (0 T ha−1). Improving SOM due to biochar application, improved the soil physical properties, especially it decreased the soil BD (r=-0.71); increased HC (r=0.65); TSP (r=0.71); PAW (r=0.86); aeration pore (r=0.54); field capacity (r=0.83); and decreased wilting point (r=-0.66). Biochar application also improved production of black corn. It linearly increased crop height (r=0.76); corn stalk dry weight (DW) (r=0.75); grain yield DW (r=0.75); shoot DW (r=0.74); and root DW (r=0.76). Biochar derived from corn cob could be used to improve physical properties of ex coal mined soil.

The Critical Period of Weed Control Influences Sunflower (Helianthus annuus L.) Yield, Yield Components but Not Oil Content

Field studies were conducted in the northeastern part of the Republic of Croatia to determine the influence of the critical period of weed interference on sunflower (Helianthus annuus) yield, yield components, and oil content. For this purpose, different durations of competition were established, allowing weeds to infest the crop for increasing periods of time after planting or maintaining plots weed-free for increasing periods of time after planting. The beginning and the end of the critical period of weed control (CPWC), based on a 5% and 10% loss of sunflower yield, were determined by fitting the four-parameter log-logistic equations to the relative seed yield. The total weed biomass increased progressively in relation to the increase in the competition. The beginning of the CPWC period, based on a 5% acceptable yield loss, ranged from 141 to 234 growing degree days (GDD), which corresponded to the two-to-four true leaf development stage (the V2–V4 growth stages) across both sites and years. The crop had to be kept weed-free until a period when sunflower inflorescence began to open and flower (the R4–R5 growth stage) or from 1365 to 1932 GDD. The sunflower yield and yield components varied between the years and among locations. An increasing duration of weed interference negatively affected crop height, head diameter, and 1000-kernel weight, but not seed oil content.

Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications

Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras offer an affordable and practical solution for efficiently obtaining crop AGB. However, traditional vegetation indices (VIs) alone are insufficient in capturing crop canopy structure, leading to poor estimation accuracy. Moreover, different flight heights and machine learning algorithms can impact estimation accuracy. Therefore, this study aims to enhance wheat AGB estimation accuracy by combining VIs, crop height, and texture features while investigating the influence of flight height and machine learning algorithms on estimation. During the heading and grain-filling stages of wheat, wheat AGB data and UAV RGB images were collected at flight heights of 30 m, 60 m, and 90 m. Machine learning algorithms, including Random Forest Regression (RFR), Gradient Boosting Regression Trees (GBRT), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Regression (SVR), were utilized to construct wheat AGB estimation models. The research findings are as follows: (1) Estimation accuracy using VIs alone is relatively low, with R2 values ranging from 0.519 to 0.695. However, combining VIs with crop height and texture features improves estimation accuracy, with R2 values reaching 0.845 to 0.852. (2) Estimation accuracy gradually decreases with increasing flight height, resulting in R2 values of 0.519–0.852, 0.438–0.837, and 0.445–0.827 for flight heights of 30 m, 60 m, and 90 m, respectively. (3) The choice of machine learning algorithm significantly influences estimation accuracy, with RFR outperforming other machine learnings. In conclusion, UAV RGB images contain valuable crop canopy information, and effectively utilizing this information in conjunction with machine learning algorithms enables accurate wheat AGB estimation, providing a new approach for precision agriculture management using UAV remote sensing technology.

The effectiveness of sewage sludge biochar amendment with Boehmeria nivea L. in improving physicochemical properties and rehabilitating microbial communities in mine tailings

Biochar amendment can be adopted to improve soil substrate, in turn facilitated phytoremediation. However, improvements to the properties of tailings following different feedstocks of biochar amendment in phytoremediation, particularly the impacts on nitrogen cycle and the related nitrogen-fixing microorganisms remain unclear. In this study, a 100-day pot experiment was designed to determine the co-effects of different combinations of woody and non-woody biochar, namely hibiscus cannabinus core biochar (HB), sewage sludge biochar (SB), chicken manure biochar (MB) and two crops (Cassia alata L., Boehmeria nivea L.). It was found that, on the one hand, biochar amendment directly immobilized heavy metal (loid) contamination in the tailings; on the other hand, biochar amendment, particularly non-woody SB, improved soil properties (i.e., the combination of SB with crops increased the total nitrogen content by 4.7–7.5 times). This indirectly improved phytostabilization (i.e., SB increased crop height 1.5–1.8 fold, root length 3.3–3.7 fold, decreased NH4NO3-extractable Pb, Cu, Cd and also increased the relative abundance of nitrogen-fixing bacteria such as Mesorhizobium, Bradyrhizobium, and Rhizobium). Besides this, redundant analysis shown that the carbon, nitrogen sources, and pH provided by the biochar were identified as the key factors associated with the nitrogen-fixing bacteria. Through the comprehensive evaluation of different biochar amendment in phytoremediation, it was found that the non-woody SB got higher comprehensive score (3.1–3.6) in biochar amendment in phytoremediation, especially in Boehmeria nivea L. Thus, the combination of non-woody SB and Boehmeria nivea L. improved microbial function, while the microorganisms in turn promoted crop growth. Our results revealed the prospect of using non-woody SB assisted Boehmeria nivea L. for phytoremediation in multi-metal mine tailings.

Calibrating UAV thermal sensors using machine learning methods for improved accuracy in agricultural applications

Accurate temperature measurements are essential for detecting crop stress, managing irrigation, and monitoring vegetation health. However, various factors can affect thermal sensors that can introduce measurement errors. To address this, machine learning (ML) algorithms were used to calibrate unmanned air vehicle (UAV) thermal sensor measurements. In this study, commercially available two different types of UAV thermal sensors, including Micasense Altum and Flir Duo Pro-R (FDP-R), have been tested and evaluated its performance by comparing the calibrated ground thermal measurements. For this purpose, five different ML algorithms, namely Random Forest, Support Vector Machine, K-NN and XGBoost, were used to calibrate UAV thermal sensors. Results showed that, after thermal calibration with XGBoost, the RMSE decreased by 2.84 °C (from 4.23 °C to 1.39 °C) for Micasense Altum and by 2.51 °C (from 3.84 °C to 1.33 °C) for FDP-R, while R2 increased from 0.89 to 0.96 for Micasense Altum and from 0.87 to 0.94 for FDP-R. In addition, we conducted correlation analyses between the calibrated temperature measurements and various sorghum phenotype parameters, such as leaf area index, crop height, and soil moisture. The results indicate that both sensors have performed well in terms of correlation coefficients. Micasense Altum has shown slightly better performance for crop height and soil moisture (r = -0.78 and r = -0.59, respectively), while FDP-R has performed better for leaf area index (r = -0.70). This study demonstrates the potential of using calibrated UAV thermal sensors for precision agriculture tasks and highlights the importance of validating the calibration with ground measurements.

A Ground Point Fitting Method for Winter Wheat Height Estimation Using UAV-Based SfM Point Cloud Data

Height is a key factor in monitoring the growth status and rate of crops. Compared with large-scale satellite remote sensing images and high-cost LiDAR point cloud, the point cloud generated by the Structure from Motion (SfM) algorithm based on UAV images can quickly estimate crop height in the target area at a lower cost. However, crop leaves gradually start to cover the ground from the beginning of the stem elongation stage, making more and more ground points below the canopy disappear in the data. The terrain undulations and outliers will seriously affect the height estimation accuracy. This paper proposed a ground point fitting method to estimate the height of winter wheat based on the UAV SfM point cloud. A canopy slice filter was designed to reduce the interference of middle canopy points and outliers. Random Sample Consensus (RANSAC) was applied to obtain the ground points from the valid filtered point cloud. Then, the missing ground points were fitted according to the known ground points. Furthermore, we achieved crop height monitoring at the stem elongation stage with an R2 of 0.90. The relative root mean squared error (RRMSE) of height estimation was 5.9%, and the relative mean absolute error (RMAE) was 4.6% at the stem elongation stage. This paper proposed the canopy slice filter and fitting missing ground points. It was concluded that the canopy slice filter successfully optimized the extraction of ground points and removed outliers. Fitting the missing ground points simulated the terrain undulations effectively and improved the accuracy.

Grain yield response to planting date and cutting height of the first crop in rice ratooning

AbstractRice (Oryza sativa L.) ratooning is one of the most essential production strategies to increase grain yield. Field experiments were conducted in southwestern Japan in 2021 and 2022 to determine the effects of planting date and cutting height of the first crop on grain yield. Grain yield of the second crop was higher at early planting date than at late planting date in 2021, but the yield difference was narrowed in 2022. The higher grain yield at early planting date resulted from the higher number of spikelets and percentage of filled spikelets due to the higher stubble nonstructural carbohydrate (NSC) content, leaf area index (LAI), and air temperatures. Grain yield of the second crop was higher at high cutting height than at low cutting height in 2021, but the yield difference was narrowed in 2022. The higher grain yield at high cutting height resulted from the higher number of spikelets due to the higher NSC content and LAI. In the weather conditions, grain yield had a strong positive correlation with the air temperature, which had positive correlations with the number of panicle and percentage of filled spikelets. In the stubble conditions, grain yield had a strong positive correlation with NSC content, which had positive correlations with the number of panicle and percentage of filled spikelets. Therefore, early planting, which secures high air temperatures, and high cutting height, which produces high NSC content, are recommended to increase grain yield of the second crop.

Comparison of Methods for Estimating Damage by Wild Ungulates on Field Crops

High numbers of large ungulates are locally accompanied by high levels of damage to field crops, causing economic losses and increased costs for the protection of agricultural fields. Quantifying the levels of damage can be problematic, with the degree of accuracy depending on the method used. The aim of this study was to compare the accuracy, workload and cost of four methods commonly used for estimating damage to wheat fields caused by large ungulates (esp. wild boar) in the Czech Republic. The results suggest that the manual processing of aerial photographs (“Uncrewed Aerial Systems [UAS] with Operator Delineation Method”) was very laborious and the least accurate method, with a high risk of error. In comparison, the automatic evaluation of aerial images (“UAS Crop Height Method”) and the “Ground-Based Assessment” both provided similar results when carefully analyzed and were equally demanding. The “Yield Method”, comparing the net yield from damaged and undamaged areas, provided the same result of assessment and was the least laborious, although it does require the existence of comparable areas and for the conditions to be created in advance before the method is used. Equivalent results were achieved by the UAS Crop Height Method, which we recommend using in cases where the Yield Method cannot be applied.

Predicting Dry Pea Maturity Using Machine Learning and Advanced Sensor Fusion with Unmanned Aerial Systems (UASs)

Maturity is an important trait in dry pea breeding programs, but the conventional process predominately used to measure this trait can be time-consuming, labor-intensive, and prone to errors. Therefore, a more efficient and accurate approach would be desirable to support dry pea breeding programs. This study presents a novel approach for measuring dry pea maturity using machine learning algorithms and unmanned aerial systems (UASs)-collected data. We evaluated the abilities of five machine learning algorithms (random forest, artificial neural network, support vector machine, K-nearest neighbor, and naïve Bayes) to accurately predict dry pea maturity on field plots. The machine learning algorithms considered a range of variables, including crop height metrics, narrow spectral bands, and 18 distinct color and spectral vegetation indices. Backward feature elimination was used to select the most important features by iteratively removing insignificant ones until the model’s predictive performance was optimized. The study’s findings reveal that the most effective approach for assessing dry pea maturity involved a combination of narrow spectral bands, red-edge, near-infrared (NIR), and RGB-based vegetation indices, along with image textural metrics and crop height metrics. The implementation of a random forest model further enhanced the accuracy of the results, exhibiting the highest level of accuracy with a 0.99 value for all three metrics precision, recall, and f1 scores. The sensitivity analysis revealed that spectral features outperformed structural features when predicting pea maturity. While multispectral cameras achieved the highest accuracy, the use of RGB cameras may still result in relatively high accuracy, making them a practical option for use in scenarios where cost is a limiting factor. In summary, this study demonstrated the effectiveness of coupling machine learning algorithms, UASs-borne LIDAR, and multispectral data to accurately assess maturity in peas.

Machine learning techniques in estimation of eggplant crop evapotranspiration

This study predicted the daily evapotranspiration of eggplant (Solanum melongena L.) under full and deficit irrigation in the Bafra district of Samsun province, Turkey, using machine learning methods. Artificial neural networks (ANNs), deep neural networks (DNN), M5 model tree (M5Tree), random forest (RF), support vector machine (SVM), k-nearest neighbor (kNN), and adaptive boosting were investigated as machine learning approaches. Determination of evapotranspiration in this study consists of three methods: (i) The reference evapotranspiration (ETo) was obtained from the Food and Agriculture Organization-56 Penman–Monteith equation, (ii) the values of evapotranspiration (ETc) calculated by multiplying the reference evapotranspiration by the crop coefficient (Kc), and (iii) the values of evapotranspiration (ETa) measured using soil water balance between successive soil water measurements as the outputs. The model’s performance in ETo estimation was higher when minimum and maximum temperature (Tmax and Tmin), wind speed (u2), average relative humidity (RHavg), solar radiation (Rs), and days of the year were used as inputs. The best performance was obtained in the ANN model with a coefficient of determination (R2) value of 0.984, a mean absolute error (MAE) of 0.098 mm d−1, a root-mean-square error (RMSE) of 0.153 mm d−1, and Nash–Sutcliffe efficiency of 0.983. The model’s performance in ETc estimation was significantly improved with the addition of leaf area index (LAI) and crop height (hc) to the climate parameters (MAE and RMSE values decreased by 22.6 and 23.2%, respectively). The accuracy of ETc estimation for some plant traits (hc and LAI) and average temperature (Tavg) was sufficient. The best statistical performance in estimating ETa was obtained by the RF model (Tavg, u2, RHavg, and Rs) using climate parameters. DNN proved to be the least successful model compared to the other six models in predicting ETo, ETc, and ETa.

Secondary metabolites of the granular form of Pseudomonas fluorescens P60 and its applications to control tomato bacterial wilt

Abstract. Soesanto L, Saputra DA, Sastyawan MWR, Mugiastuti E, Suprapto A, Rahayuniati RF. 2023. Secondary metabolites of the granular form of Pseudomonas fluorescens P60 and its applications to control tomato bacterial wilt. Biodiversitas 24: 2475-2482. Bacterial wilt is one of the important diseases of tomatoes caused by Ralstonia solanacearum (Smith 1896) Yabuuchi et al. 1996. Its control can be achieved by applying secondary metabolites of Pseudomonas fluorescens Migula 1895 P60 in granular form. The study aimed to determine the shelf life of the secondary metabolites P. fluorescens P60' in granular form and its application to manage bacterial wilt and the performance of tomatoes. The study was conducted for five months at the Plant Protection Laboratory and Experimental Farm, Faculty of Agriculture, Universitas Jenderal Soedirman. A randomized block design was used with four replicates and six treatments, consisting of R. solanacearum (control), R. solanacearum + 1, 5, 10, or 15 g, and bactericide (a.i. agrimycin sulphate 20%). Variables observed were inhibition zone, incubation period, disease intensity, the area under disease progress curve (AUDPC), crop height, crop fresh and dry weight, root fresh and dry weight, and phenolic compounds qualitatively. Results of the research showed that the granular formula until the 4th week still had an inhibition zone between 21.67-23.34%. However, the granules stored for as much as 15 g for four weeks effectively reduced disease intensity by 75.00%, decreased AUDPC value by 74.72%, and increased plant height, root dry weight, and fresh crop weight as well 44, 62.5, and 65.65 %, respectively. In addition, the granular formula increased the content of tomato phenolic compounds qualitatively.

Glyphosate‐resistant alfalfa can exhibit injury after glyphosate application in the Intermountain West

AbstractGlyphosate‐resistant (GR) alfalfa (Medicago sativa L.) has been widely adopted in the United States. Exceptional tolerance of GR alfalfa to glyphosate application has been reported as a strength of this technology; however, growers have recently reported potential crop injury under specific environmental conditions. The purpose of this study was to document and characterize the injury and determine best management practices for avoiding injury to GR alfalfa in the Intermountain West. The effects of glyphosate rate and application timing during various seasons were investigated at 24 sites over 5 years, measuring the impact on alfalfa crop height and biomass yield. Summer glyphosate applications did not injure alfalfa. However, spring applications reduced crop height at 76% of sites and biomass yield at 62% of sites. At responsive sites, low (869 g ae ha−1) and high (1739 g ae ha−1) glyphosate rates reduced yield by 0.53 and 1.06 Mg ha−1, respectively. Alfalfa treated with high rates in the late spring, when 15–20 cm tall, had mean yield reductions of 16%–17% compared with untreated alfalfa. These results suggest that glyphosate applications made at tall crop heights or high rates on GR alfalfa are more likely to reduce crop height and yield in the Intermountain West compared with earlier applications at lower rates. We recommend that spring applications using low glyphosate rates occur before alfalfa is 10 cm tall to mitigate the risk of injury. If a high glyphosate rate is necessary, then an application should be made before alfalfa is greater than 5 cm tall.

Development of a Quick-Install Rapid Phenotyping System.

In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing of the crop canopy with other diverse measurements under varying environmental conditions. The system demonstrates a throughput increase by a factor of 50 when compared to a manual setup, allowing for efficient mapping of crop status across a field with crops grown in rows of any spacing. Tests presented in this paper illustrate the type of experimentation that can be performed with the platform, emphasizing the output from each sensor. The system integration, versatility, and ergonomics are the most significant contributions. The presented system can be used for studying plant responses to different treatments and/or stresses under diverse farming practices in virtually any field environment. It was shown that crop height and several vegetation indices, most of them common indicators of plant physiological status, can be easily paired with corresponding environmental conditions to facilitate data analysis at the fine spatial scale.

High-precision plant height measurement by drone with RTK-GNSS and single camera for real-time processing

Conventional crop height measurements performed using aerial drone images require 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation time and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths (approximately 1 m) during the flight by linking the RTK-GNSS and aerial image capture points. As the baseline length of a typical stereo camera is fixed, once the camera is calibrated on the ground, it does not need to be calibrated again during the flight. However, the proposed system requires quick calibration in flight because the baseline length is not fixed. A new calibration method that is based on zero-mean normalized cross-correlation and two stages least square method, is proposed to further improve the accuracy and stereo matching speed. The proposed method was compared with two conventional methods in natural world environments. It was observed that error rates reduced by 62.2% and 69.4%, for flight altitudes between 10 and 20 m respectively. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the error rates were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.