Measured Plant Height Research Articles

Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm

Plant genomics have progressed significantly due to advances in information technology, but phenotypic measurement technology has not kept pace, hindering plant breeding. As maize is one of China’s three main grain crops, accurately measuring plant height is crucial for assessing crop growth and productivity. This study addresses the challenges of plant segmentation and inaccurate plant height extraction in maize populations under field conditions. A three-dimensional dense point cloud was reconstructed using the structure from motion–multi-view stereo (SFM-MVS) method, based on multi-view image sequences captured by an unmanned aerial vehicle (UAV). To improve plant segmentation, we propose a column space approximate segmentation algorithm, which combines the column space method with the enclosing box technique. The proposed method achieved a segmentation accuracy exceeding 90% in dense canopy conditions, significantly outperforming traditional algorithms, such as region growing (80%) and Euclidean clustering (75%). Furthermore, the extracted plant heights demonstrated a high correlation with manual measurements, with R2 values ranging from 0.8884 to 0.9989 and RMSE values as low as 0.0148 m. However, the scalability of the method for larger agricultural operations may face challenges due to computational demands when processing large-scale datasets and potential performance variability under different environmental conditions. Addressing these issues through algorithm optimization, parallel processing, and the integration of additional data sources such as multispectral or LiDAR data could enhance its scalability and robustness. The results demonstrate that the method can accurately reflect the heights of maize plants, providing a reliable solution for large-scale, field-based maize phenotyping. The method has potential applications in high-throughput monitoring of crop phenotypes and precision agriculture.

Performing whole-genome association analysis of winter wheat plant height using the 55K chip

Plant height is a critical agronomic that affects both plant architecture and yield. To decipher the genetic mechanisms underlying winter wheat plant height and identify candidate genes associated with this trait, we conducted phenotypic analysis on 239 wheat varieties (lines) collected from around the world. This analysis was complemented by genotyping using the wheat 55K SNP chip. A Wholegenome association analysis (GWAS) of wheat plant height was conducted utilizing the MLM (Q+K) model within TASSLE software. The results revealed significant phenotypic variation in wheat plant height across different years, with coefficients of variation ranging from 0.96% to 1.97%. Additionally, there was a strong correlation in plant height measurements between different years. GWAS identified 44 SNP markers significantly associated with wheat plant height across various environments (P ≤ 0.00001), predominantly distributed on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 6B, 6D, and 7D, explaining individual phenotypic variance rates ranging from 5.00% to 11.11%. Further, by mining association loci with substantial phenotypic effects and stability across multiple environments, seven candidate genes related to wheat plant height have been identified. This study provides new genetic markers and resources for improving wheat plant height.

EVALUATION OF THE EFFECTS OF MICROBIOLOGICAL AGENTS ON THE REGENERATION OF A NUTRIENT SOLUTION WHEN GROWING VIGNA RADIATA MICROGREENS IN FLOOD & DRAIN SYSTEMS

Purpose. To evaluate the effectiveness of different microbiological agents in improving the nutrient solution quality and stimulating the growth and development of the mung bean (Vigna radiata) microgreens. Methods. Statistical processing, field assessments, analytical calculations. The study was conducted in the laboratory using three main types of microbiological agents: bacteria of the genera Bacillus and Pseudomonas as well as Rhodopseudomonas palustris. The tests were conducted on samples of the mung bean (Vigna radiata) microgreens grown in Flood & Drain systems, with regular monitoring of the nutrient solution parameters (pH, concentrations of major macro- and microelements, toxic compounds). To analyze the effects of microbiological agents, I used standard methods of hydroponics and chemical analyses of the nutrient solution. The plant growth was assessed by measuring plant height, number of leaves and total biomass. Results. It was found that the microbiological agents significantly improved the nutrient solution quality. Bacillus subtilis- and Rhodopseudomonas palustris-based products reduced the concentrations of toxic compounds such as ammonium and nitrates and increased the levels of available nutrients. In the experiment, an improvement in the growth rates of the mung bean (Vigna radiata) microgreens and an increase in the number of leaves and total biomass were noted. An increase in the chlorophyll level in the leaves was observed, indicating an improvement in photosynthetic activity. Discussion. The results confirmed the positive effects of the microbiological agents on the nutrient solution quality and the growth of the mung bean (Vigna radiata) microgreens. The Rhodopseudomonas palustris- and Bacillus subtilis-containing products proved to be more effective compared to the other microbiological agents, supporting published data on their abilities to bioactivate nutrients and reduce toxic decay products. It was demonstrated that the use of the microbiological products based on Rhodopseudomonas palustris and Bacillus subtilis will allow for a reduction in costs for solution regeneration. Conclusions. The use of microbiological agents seems to be a promising approach to improve the quality and extend the period of use of nutrient solutions in Flood & Drain systems and to stimulate the growth of the mung bean (Vigna radiata) microgreens. The microbiological products based on Rhodopseudomonas palustris and Bacillus subtilis were highly efficient in maintaining optimal conditions for the growth and development of the mung bean (Vigna radiata) plants. Further research is recommended to optimize the dosage and application conditions of agents and to elucidate their interactions with other components of hydroponic systems.

Use of supervised and unsupervised approaches to make zonal application maps for variable-rate application of crop growth regulators in commercial cotton fields

BackgroundZonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices. For cotton, zonal maps for crop growth regulator (CGR) applications under variable-rate (VR) strategies are commonly based exclusively on vegetation indices (VIs) variability. However, VIs often saturate in dense crop vegetation areas, limiting their effectiveness in distinguishing variability in crop growth. This study aimed to compare unsupervised framework (UF) and supervised framework (SUF) approaches for generating zonal application maps for CGR under VR conditions. During 2022–2023 agricultural seasons, an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton, satellite imagery, soil texture, and phenology data. Subsequently, a SUF (based on historical data between 2020–2021 to 2022–2023 agricultural seasons) was developed to predict plant height using remote sensing and phenology data, aiming to replicate same zonal maps but without relying on direct field measurements of plant height. Both approaches were tested in three fields and on two different dates per field.ResultsThe predictive model for plant height of SUF performed well, as indicated by the model metrics. However, when comparing zonal application maps for specific field-date combinations, the predicted plant height exhibited lower variability compared with field measurements. This led to variable compatibility between SUF maps, which utilized the model predictions, and the UF maps, which were based on the real field data. Fields characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF approaches. This was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field environments. While VR application approach can facilitate product savings during the application operation, other key factors must be considered. These include the availability of specialized machinery required for this type of applications, as well as the inherent operational costs associated with applying a single CGR product which differs from the typical uniform rate applications that often integrate multiple inputs.ConclusionPredictive modeling shows promise for assisting in the creation of zonal application maps for VR of CGR applications. However, the degree of agreement with the actual variability in crop growth found in the field should be evaluated on a field-by-field basis. The SUF approach, which is based on plant heigh prediction, demonstrated potential for supporting the development of zonal application maps for VR of CGR applications. However, the degree to which this approach aligns itself with the actual variability in crop growth observed in the field may vary, necessitating field-by-field evaluation.

Flight Altitude and Sensor Angle Affect Unmanned Aerial System Cotton Plant Height Assessments

Plant height is a critical biophysical trait indicative of plant growth and developmental conditions and is valuable for biomass estimation and crop yield prediction. This study examined the effects of flight altitude and camera angle in quantifying cotton plant height using unmanned aerial system (UAS) imagery. This study was conducted in a field with a sub-surface irrigation system in Lubbock, Texas, between 2022 and 2023. Images using the DJI Phantom 4 RTKs were collected at two altitudes (40 m and 80 m) and three sensor angles (45°, 60°, and 90°) at different growth stages. The resulting images depicted six scenarios of UAS altitudes and camera angles. The derived plant height was subsequently calculated as the vertical difference between the apical region of the plant and the ground elevation. Linear regression compared UAS-derived heights to manual measurements from 96 plots. Lower altitudes (40 m) outperformed higher altitudes (80 m) across all dates. For the early season (4 July 2023), the 40 m altitude had r2 = 0.82–0.86 and RMSE = 2.02–2.16 cm compared to 80 m (r2 = 0.66–0.68, RMSE = 7.52–8.76 cm). Oblique angles (45°) yielded higher accuracy than nadir (90°) images, especially in the late season (24 October 2022) results (r2 = 0.96, RMSE = 2.95 cm vs. r2 = 0.92, RMSE = 3.54 cm). These findings guide optimal UAS parameters for plant height measurement.

Comparison Study on Effect of Groundwater, Influent and Effluent of STP on Irrigation Land in Bhudihal Area, Davanagere Taluk

Groundwater is important for irrigation because it supplies essential for irrigation. The incoming flow of water to STP refers to influent and effluent. This study investigates the effect of groundwater (GW), effluent, and influent on soil properties and plant growth which focuses on fenugreek cultivation Results showed that most changes observed in soil irrigated with influent with increase of soil pH, electrical conductivity (EC), and nutrient levels N, P, K of 7.91, 1.01, 241, 41.89, 96.73.The Plant height measurement indicates the superior growth with influent and effluent irrigation compared to GW, with influent irrigation resulting in the highest plant growth.

The effect of swallow guano application on the growth of mustard plants (Brassica juncea L.)

This study aims to determine the effect of giving swallow guano on the growth of mustard greens (Brassica juncea L.) using the dose that produces the best growth of mustard greens (Brassica juncea L.). This type of experimental research used a completely randomised design (CRD), X0 (control), X1 (122 grams), X2 (245 grams) and X3 (490 grams) each with 6 repetitions. The population in this study were all mustard plants with a total sample of 24 plants. Parameter measurements performed consisted of plant height, leaf length, leaf width, number of leaves and fresh weight. The data analysis technique was descriptive analysis and the BJND advanced test was carried out for analysis of variance. The results of the descriptive analysis of mustard plant growth from the 22nd HST measurement to the 40th HST experienced an increase in the graph with treatment X2 (245 grams) giving the best treatment for measuring plant height parameters, namely 40.63 cm, leaf length 25.81 cm, leaf width 16.06 cm, the number of leaves is 9.8 and the wet weight is 149.5 grams. The results of the analysis of variance from the 22nd HST to the 40th HST obtained the results of Fhit > Ftab. The results of the BJND HST test showed that treatment X2 (245 grams) gave a real treatment of measuring the parameters of plant height, length, width, and number of leaf blades and wet weight, compared to treatments X1 (122 grams) and X3 (490 grams).

Single plant segmentation and growth parameters measurement of maize seedling stage based on point cloud intensity

Maize growth parameters are key pieces of information describing the growth, development and yield of plants, and they are of great significance for high quality and high yield maize breeding. Plants counting, row spacing, and plant spacing are closely related to maize sowing quality and yield. Plant height is closely related to maize photosynthetic rate and biomass. The main problem with measuring phenotypic parameters during the maize seedling stage is that maize plants are small, making it difficult to segment maize plant point clouds from ground point clouds, and there is a lack of automatic methods for measuring growth parameters. A point cloud intensity segmentation and improved Euclidean clustering method for single plant segmentation was proposed based on terrestrial laser scanning (TLS) technical, and an automatic measuring method of growth parameters of maize seedling stage was realized. First, TLS was used to scan maize plants at two planting densities at V1 (first leaf is fully expanded) and V2 (second leaf is fully expanded) stages. Second, the segmentation based on point cloud intensity method and the improved Euclidean clustering single plant segmentation method were used to achieve the segmentation of a single maize plant. Finally, the automatic measurement of maize plants counting, row spacing, plant spacing, and plant height were realized. When plants counting of maize, the percentage error was lower than 1.70%. In the measurement of row spacing, plant spacing and plant height of maize plants, the mean absolute percentage error (MAPE) were lower than 1.50%, 5.50% and 3.10%, respectively. These results demonstrate that the point cloud intensity segmentation and growth parameters measurement method is feasible for maize V1 and V2 stages and different planting densities. The automatic measurement values of the number of maize plants, row spacing, plant spacing and plant height has high measurement accuracy and small error. This study provides a rapid, automatic and accurate measurement method for researchers to measure maize growth parameters.

Evaluation of Three Cucurbitaceae Cultivars Resistant against Begomoviruses Based on Morphological Symptoms and Molecular Analysis

Cucurbitaceae is a group of annual horticultural crops with high economic prospects and benefits. The Faculty of Biology, Universitas Gadjah Mada has developed several new cultivars within the Cucurbitaceae family, such as the melon cultivar 'Hikapel', watermelon 'Citra Jingga', and butternut squash 'Citra Laga'. However, development of these cultivars has been hindered by yellow disease, typically triggered by Begomovirus infection. This study aimed to determine the morphological symptoms of yellow disease, to detect Begomovirus infection using molecular approach, and assess resistance levels of the three Cucurbitaceae cultivars. The method involved observing morphological symptoms in plants and measuring plant height. The degree of symptom severity was then converted into a vulnerability index (VI). The symptomatic leaf samples were tested using Krusty and Homer primer to confirm the symptoms caused by Begomovirus. The PCR results were sequenced and analyzed using Maximum Likelihood method in MEGA 11. The results showed that symptoms included curling tip of leaves, yellow mosaic on the leaves, and stunted growth in some plants, which were suggested as Begomovirus infection. Based on the Vulnerability Index (VI), the watermelon cultivar 'Hikapel' was considered susceptible to Begomovirus infection (VI = 85%), while the yellow squash 'Citra Laga' was moderately susceptible (VI = 57.5%). However, the watermelon 'Citra Jingga' was considered moderately resistant to this infection (VI = 50%). The presence of DNA the Begomovirus at three species Cucurbitaceae were characterized by a 550 bp band. The 'Hikapel' melon and 'Citra Laga' butternut were infected with Tomato leaf curl New Delhi virus (ToLCNDV), while the ‘Citra Jingga’ watermelon were infected with Squash leaf curl Philippine virus (SLCuPV).

Comparison of Imaging and Actual Measurement ​​of Plant Height and Leaf Area of ​​Cucumber Grafted Seedlings using LiDAR and Multispectral Camera

Comparison of Imaging and Actual Measurement ​​of Plant Height and Leaf Area of ​​Cucumber Grafted Seedlings using LiDAR and Multispectral Camera

Vegetative traits, floral biology, and mutualistic interactions in the tropical mountain shrub Ribes ciliatum (Grossulariaceae)

Plant species in tropical mountain ecosystems experience significant environmental variation across short spatial distances. These systems offer an opportunity to assess geographic variation in plant traits in relation to biotic and abiotic environments. We examined plant vegetative and reproductive traits in the shrub R. ciliatum across two native conifer forests in central Mexico. We measured plant height, number of branches, leaf size, flower production, floral morphology, floral longevity, and plant reproductive success in R. ciliatum populations found in fir forests (3100 m) and Hartweg's pine forests (subalpine habitat, 3700 m). We also quantified the level of pollinator dependence, pollinator visitation rates, and fruit consumption rates by frugivorous birds in both populations. Plants were shorter and had smaller leaves in the subalpine forest than in the fir forest. Floral longevity decreased in the subalpine forest, but flower production, flower size, and fruit weight remained similar across populations. Fruit set and pollinator visitation rates were higher in the subalpine forest; however, seed weight and fruit consumption rates did not differ between sites. Hummingbirds and bumblebees were the primary pollinators of R. ciliatum, and the bird Ptiliogonys cinereus was the most important fruit consumer at both sites. Fruit set was entirely dependent on pollinators. Reduced plant size in the subalpine forest coincides with a general pattern associated with high mountain environments. In contrast, reproductive characters had a closer relationship to the pollination environment. The absence of variation in flower size and shape was consistent with the similarity in the composition of the pollinator community. Meanwhile, compared to lower-elevation forests, the subalpine forest exhibited shorter-lived flowers and increased fruit set, associated with higher pollinator visitation rates. Ribes ciliatum is a key resource for pollinators and frugivores at a time of year when few other floral resources are available in these high-elevation mountain forests.

Analysis of the Application of Vitamin B1 on the Response of Salinity Stress Resistance in Several Varieties of Rice (Oryza Sativa L.)

One of the most common stresses in rice cultivation is salinity. Rice plants stressed by salinity exhibit changes such as yellowing leaves, drying tips, and chlorosis. The efforts made by the government and farmers so far include implementing cultivation scheduling techniques, planting patterns, and using stress-resistant varieties, as well as improving soil to increase water-holding capacity through lime application. Each of these efforts comes with its own risks. Another approach to enhance the growth and yield of rice plants is the application of vitamins. Providing vitamins can stimulate the growth of plant organs, as they play a crucial role in the growth process by acting as catalysts for metabolism. Research has indicated that vitamin B1 can significantly promote plant growth under stressful conditions. This study aims to investigate the positive effects of various concentrations of vitamin B1 on the growth and yield of rice plants while also reducing salinity stress. The method used involved planting three varieties of rice—IR-46, Inpari-32, and Pokkali—in planting buckets using the TABELA system. Vitamin B1 was applied at concentrations of 0, 5, and 10 mM during the peak vegetative phase, with salinity stress of 6 dS/m introduced one day after vitamin application. The plants were maintained under salinity stress conditions until harvest, during which morphological and phytochemical analyses were conducted. Morphological analysis included measurements of plant height, number of tillers, number of productive tillers, number of grains per panicle, and percentage of healthy grains. Biochemical parameters measured included total chlorophyll and electrolyte leakage analysis. The results indicate that vitamin B1 can effectively reduce stress in plants affected by salinity.

The Effect of Use of Cow Manure on the Growth of Two Cultivars of Glycine Max L. Using a Verticulture Planting System

This study aims to provide data on the verticulture method planting and the use of cow manure to black soybeans (Glycine max L.). This study used a factorial Randomized Block Design (RAK) containing two factorials and four replications. K1 refers to the unit 1 soybean cultivar, while K2 stands for the pearl 2 cultivar. One of the cultivar factors is this (K). Inside the interval (P), we find three values: P1= 0 kg, P2= 0.18 kg, and P3= 0.32 kg. “Among the variables that underwent changes were plant height, leaf count, leaf area index, chlorophyll content, relative growth rate (LPR), net assimilation rate (LAB), and active root nodules. According to the results, there was no significant difference between using 0.16 kg/bed of organic fertilizer or no fertilizer at all when measuring plant height at 7, 14, 21, 28, 35, and 42 HST for Cultivar K1 (detam 1)”. The availability of organic fertilizer at 0.32 kg/bed was significantly affected by manure at 7, 14, 21, 28, 35, and 42 HST at the third dosage. The results of this study show that neither cultivar 1 nor pearl cultivar 2 had any discernible impact on the vegetative development of black soybean plants when manure was applied at dosages of 0 kg, 0.16 kg, and 0.32 kg.

QTL mapping of maize plant height based on a population of doubled haploid lines using UAV LiDAR high-throughput phenotyping data

Maize (Zea mays L.) is a globally significant crop that plays a crucial role in feeding the growing global population. Among its various traits, plant height is particularly important as it affects yield, lodging resistance, ecological adaptability, and other important factors. Traditional methods for measuring plant height often lack cost-efficiency and accuracy. In this study, we employed a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) to collect point cloud data from 270 doubled haploid (DH) lines. This innovative application of UAV-based LiDAR technology was explored for high-throughput phenotyping in maize breeding. We constructed high-density genetic maps and assessed plant height at both single-plant and row scales across multiple developmental stages and genetic backgrounds. Our findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with an R2 of 0.67 versus 0.56 and an RMSE of 0.12 m vs. 0.17 m, respectively. Two high-density genetic maps were constructed based on SNP markers. In Sanya and Xinxiang, the F1DH and F2DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively. The study successfully identified and validated QTLs associated with plant height, revealing novel genetic loci and candidate genes. This research highlights the potential of UAV-based remote sensing to advance precision agriculture by enabling efficient, large-scale phenotyping and gene discovery in maize breeding programs.

Availability of Recycled Phosphorus on Biochar Reacted with Wastewater to Support Growth of Lactuca sativa

The use of biochar in water resource and recovery facilities (WRRF) shows promise for recovery of phosphorus (P) to use as a biochar-based fertilizer (BBF) that can replace conventional fertilizers, promote carbon sequestration, and improve soil quality. In this study, biochar was recovered after being dosed into secondary-treated discharge from a municipal WRRF. The value of the recovered biochar as a BBF was tested in a lettuce (Lactuca sativa) growth trial. The BBF was compared to an inorganic fertilizer, raw biochar, and controls that had either only nitrogen (N) fertilizer or no amendment. The ability of the treatments to support plant growth was determined by measuring plant height, biomass, leaf tissue total N and P concentration, and plant quality. Plant quality for the Fe-modified biochar used in the WRRF was 9.05 (±0.44) on a 10-point scale compared to 9.61 (±0.46) for the inorganic fertilizer treatment and 2.22 (±0.82) for the untreated control. Plant tissue P concentrations were 6.28 (±0.83), 9.88 (±0.90), 15.46 (±2.54), and 6.36 (±1.91) g plant−1 for the raw biochar, Fe-modified biochar used in the WRRF, inorganic fertilizer, and no amendment treatments, respectively. Soil P availability and P uptake amount in the leaves indicated that the BBF released P more slowly than the inorganic P fertilizer; however, it was sufficiently available for uptake to support plant growth to maturity. Results from these experiments show that Fe-modified biochar used in WRRF can supply adequate P to plants. The slow release will reduce P leaching into surface waters.

A Multi-Plant Height Detection Method Based on Ruler-Free Monocular Computer Vision

Plant height is an important parameter of plant phenotype as one indicator of plant growth. In view of the complexity and scale limitation in current measurement systems, a scaleless method is proposed for the automatic measurement of plant height based on monocular computer vision. In this study, four peppers planted side by side were used as the measurement objects. Two color images of the measurement object were obtained by using a monocular camera at different shooting heights. Binary images were obtained as the images were processed by super-green grayscale and the Otsu method. The binarized images were transformed into horizontal one-dimensional data by the statistical number of vertical pixels, and the boundary points of multiple plants in the image were found and segmented into single-plant binarized images by filtering and searching for valleys. The pixel height was extracted from the segmented single plant image and the pixel displacement of the height was calculated, which was substituted into the calculation together with the reference height displacement to obtain the realistic height of the plant and complete the height measurements of multiple plants. Within the range of 2–3 m, under the light condition of 279 lx and 324 lx, this method can realize the rapid detection of multi-plant phenotypic parameters with a high precision and obtain more accurate plant height measurement results. The absolute error of plant height measurement is not more than ±10 mm, and the absolute proportion error is not more than ± 4%.

Development of a deep-learning phenotyping tool for analyzing image-based strawberry phenotypes.

In strawberry farming, phenotypic traits (such as crown diameter, petiole length, plant height, flower, leaf, and fruit size) measurement is essential as it serves as a decision-making tool for plant monitoring and management. To date, strawberry plant phenotyping has relied on traditional approaches. In this study, an image-based Strawberry Phenotyping Tool (SPT) was developed using two deep-learning (DL) architectures, namely "YOLOv4" and "U-net" integrated into a single system. We aimed to create the most suitable DL-based tool with enhanced robustness to facilitate digital strawberry plant phenotyping directly at the natural scene or indirectly using captured and stored images. Our SPT was developed primarily through two steps (subsequently called versions) using image data with different backgrounds captured with simple smartphone cameras. The two versions (V1 and V2) were developed using the same DL networks but differed by the amount of image data and annotation method used during their development. For V1, 7,116 images were annotated using the single-target non-labeling method, whereas for V2, 7,850 images were annotated using the multitarget labeling method. The results of the held-out dataset revealed that the developed SPT facilitates strawberry phenotype measurements. By increasing the dataset size combined with multitarget labeling annotation, the detection accuracy of our system changed from 60.24% in V1 to 82.28% in V2. During the validation process, the system was evaluated using 70 images per phenotype and their corresponding actual values. The correlation coefficients and detection frequencies were higher for V2 than for V1, confirming the superiority of V2. Furthermore, an image-based regression model was developed to predict the fresh weight of strawberries based on the fruit size (R2 = 0.92). The results demonstrate the efficiency of our system in recognizing the aforementioned six strawberry phenotypic traits regardless of the complex scenario of the environment of the strawberry plant. This tool could help farmers and researchers make accurate and efficient decisions related to strawberry plant management, possibly causing increased productivity and yield potential.

Larvicidal and feeding deterrent properties of extracts from Dieffenbachia maculata (Dumb cane) against Spodoptera frugiperda in maize (Zea mays)

This study examined the larvicidal and feeding deterrent properties of extracts from Dieffenbachia maculata against Spodoptera frugiperda (Fall army worm) in growing maize. A 9 m x 7 m space in the green house was divided into forty-two plots of 1 m by 0.75 m with 0.5 m discard between rows. This is to allow for a 2 x 3 x 4 factorial experiment replicated three times. Maize seeds were sown at two seeds per hole at a spacing of 90 cm by 60cm in each plot. Each plot was barricaded with wire mesh to prevent the migration of larvae from plot to plot. The irrigation system in the greenhouse supplied equal volume of water at the base of the maize seedlings every morning. Infested maize plants were treated with different Dumb cane plant extracts at the concentration of 25ml/l and30ml/l along with positive and negative controls. (The positive control was treated with Cypermethrine at 3l/ha) to compare the effect of concentrations on the fall army worm activity. In this experiment, the numbers of leaves affected was assessed every week and recorded. Leave perforation index was calculated. Dead armyworms were observed by prodding the larvae for movement and were recorded. Plant vigour was obtained by measuring plant height and the number of leaves formed. Effect of the worm on yield was determined by comparing total yield from sampled maize plants and this was compared to expected yield from the variety of maize used for the research. The positive control group exhibited a significantly lower level of infestation after treatment with a corresponding yield increase, (16.06% and 80.14%) respectively when compared to the treated groups. Similarly, leave extract using acetone at 30ml record significantly better results with 17.73% level of infestation after treatment application and 66.44% yield increase. This was closely followed by leaf extract using methanol at 30ml with significantly lower level of infestation and increased yield respectively. (21.35% and 60.42%). Conversely, the lowest value was recorded for the negative control. Among the extract treatments, the leaf extract using acetone at 30ml showed the highest efficacy in reducing infestation levels and demonstrated the most significant increase in maize yield. In this study, it was observed that application of the 30ml leaf extract of Dumb cane using acetone was effective and significantly increased army worm larval mortality, reduced leaf damage, and increased maize grain yield compared to the untreated control. The results indicated that the extracts from dumb cane leaves and stems exhibited promising capacity in the control of Fall army worm in maize. Further research is warranted to optimize the application protocols and evaluate any residual effects of the extracts on maize grain and ecosystem dynamics

Fermentasi Limbah Cair Produksi Tempe Terhadap Pertumbuhan Seledri (Apium graveolens L.)

Copyright©2024 The process of producing tempe produces waste material called waste, which consists of two types: solid and liquid waste. The liquid waste that remains contains numerous organic elements that decompose easily and emit an unpleasant smell. This study was to determine the effect of tempeh liquid waste as an organic liquid fertilizer on the growth of celery (Apium graveolens L). The method used was an experimental method using a completely randomized design (CRD) with a long treatment of tempeh liquid waste fermentation of 3 treatments, namely X0 (control), X1 (2 days of fermentation), and X2 (4 days of fermentation), and 3 replications. The data collection included direct measurements of plant height, number of leaves, and fresh weight, as well as environmental measurements like temperature, humidity, and the pH parameters of tempeh liquid waste. The data obtained were analyzed using analysis of variance (ANOVA). The results showed that X2 treatment (4 days of fermentation) resulted in higher growth of celery plants (stem height, number of leaves, and fresh weight) compared to X0 (dick) and X1 (2 days of fermentation). Practical implications for science from this research include reducing liquid waste, increasing agricultural productivity, developing value-added food products, increasing resource efficiency, and most importantly, it can be an innovation for conducting further research, especially related to liquid waste fermentation and plant growth.

Effectiveness of Eco Farming as Organic Fertilizer on Vegetative Growth Basil (Ocimum Basilicum L.)

The Indonesian people are familiar with the herbaceous vegetable plant known as basil (Ocimum basilicum L.). The leaves of the basil plant are typically used to flavor cuisine and as fresh vegetables. This study set out to determine how applying liquid organic fertilizer from eco farming affected basil plants' ability to grow vegetatively (Ocimum basilicum L.) as well as the optimal dosage for this growth. August and September of 2023 saw the completion of this study. Using three treatments and five replications, the research employed a Randomized Block Design (RAK) methodology. Treatment A0 was the control, receiving no fertilizer; treatment A1 received 4 milliliters of eco-farm fertilizer; and treatment A2 received 6 milliliters of eco-farm fertilizer. The characteristics that need to be taken into account include plant height, number of branches, leaf area index, number of leaves, initial and final soil analysis, and chlorophyll content. The study data will be analyzed using statistical products and services solutions (SPSS) software version 25. The administration of 6 milliliters of eco-farm fertilizer to plants, or treatment A2, had a significant effect on the vegetative development of Ocinum basilicum L. basil plants, according to the research findings. Measurements of plant height are affected by a concentration of 6 ml. The highest average value for number of leaves is 30.00, while the highest average value for height is 39.60, the highest average value of 3.35 for leaf area index, and the highest average for leaf area index. 4.0 for number of branches.