Corn Rows Research Articles

Integrating Automated Labeling Framework for Enhancing Deep Learning Models to Count Corn Plants Using UAS Imagery.

Plant counting is a critical aspect of crop management, providing farmers with valuable insights into seed germination success and within-field variation in crop population density, both of which are key indicators of crop yield and quality. Recent advancements in Unmanned Aerial System (UAS) technology, coupled with deep learning techniques, have facilitated the development of automated plant counting methods. Various computer vision models based on UAS images are available for detecting and classifying crop plants. However, their accuracy relies largely on the availability of substantial manually labeled training datasets. The objective of this study was to develop a robust corn counting model by developing and integrating an automatic image annotation framework. This study used high-spatial-resolution images collected with a DJI Mavic Pro 2 at the V2-V4 growth stage of corn plants from a field in Wooster, Ohio. The automated image annotation process involved extracting corn rows and applying image enhancement techniques to automatically annotate images as either corn or non-corn, resulting in 80% accuracy in identifying corn plants. The accuracy of corn stand identification was further improved by training four deep learning (DL) models, including InceptionV3, VGG16, VGG19, and Vision Transformer (ViT), with annotated images across various datasets. Notably, VGG16 outperformed the other three models, achieving an F1 score of 0.955. When the corn counts were compared to ground truth data across five test regions, VGG achieved an R2 of 0.94 and an RMSE of 9.95. The integration of an automated image annotation process into the training of the DL models provided notable benefits in terms of model scaling and consistency. The developed framework can efficiently manage large-scale data generation, streamlining the process for the rapid development and deployment of corn counting DL models.

Simulation of tomato and corn growth using a modified intercropping model considering radiation interception in two-dimensional space and air temperature stress

Intercropping system has been widely applied in the agricultural production worldwide. However, the difference in agricultural productivity in tomato–corn intercropping system with different spatial arrangements has not been determined. Meanwhile, the existing intercropping models cannot precisely capture inter-species crop growth dynamics. Therefore, a 10-year (2010–2019) experimental field study was conducted in Hetao irrigation district, China, for the parameterization and evaluation of the model. The experiment data from the typical years (2018 and 2019) was adopted to analyze the crop growth dynamics in different hydrological years and different systems [two rows of tomato intercropping two rows of corn (IC2–2), four rows of tomato intercropping two rows of corn (IC4–2), sole corn (SC), and sole tomato (ST)]. Moreover, a modified intercropping model considering radiation interception in two-dimensional space and air temperature stress (MICG) was deduced. The results revealed that the MICG model can precisely simulate inter-species crop growth in tomato–corn intercropping system with different spatial arrangements. The mean relative error (MRE), normalized root mean square error (nRMSE), determination coefficient (R2), and percentage bias (PBIAS) of leaf area index (LAI) simulated by the MICG model in different spatial arrangements were 9.0 %, 7.8 %, 0.97, and –5.3 % for corn and 11.0 %, 7.8 %, 0.95, and 0.1 % for tomato, respectively. An apparent difference in the simulation accuracy was observed among different intercropping models in the rapid and senescence crop growth stages. The simulation accuracy of the MICG model was apparently higher than that of ICGw and ICG models. However, the MICG model still needs to be further improved considering its some limitations, e.g., the soil respiration and soil nitrogen transformation modules need be introduced in this model. Additionally, an apparent difference in crop growth dynamics in different spatial arrangements was observed, particularly in different hydrological years. The largest difference in crop growth dynamics in different spatial arrangements was observed in the middle crop growth stage (days after sowing [DAS] 20–100) in the wet year and in the late crop growth stage (DAS 101–140) in the dry year. In general, the highest aboveground biomass and yields of corn and tomato were observed in the IC2–2 and ST systems, respectively. However, the highest water use efficiency for corn (23.9) and tomato (268.7) was observed in the IC4–2 and ST systems, respectively. The agricultural production benefits in the IC4–2 system were better than in the IC2–2 system. Average land equivalent ratio and water equivalent ratio in both years increased by 0.8 % and 12.5 % in the IC4–2 system compared with those in the IC2–2 system, respectively. Overall, the IC4–2 system could be recommended as the most optimal spatial arrangement for the tomato–corn intercropping system.

Sitting on the Watcher’s Stage: Networks of Kinship and Sovereignty Among the Corn Rows

Abstract: In the decades following the Civil War, hundreds of Cherokees living East of the Mississippi River were compelled to emigrate west. Post-war poverty, loss of land, and political struggles threatened the survival of Eastern Cherokee communities. Having received an invitation from the Cherokee Nation to be accepted as tribal citizens, many turned away from their eastern homelands after avoiding Removal and outlasting decades of pressures to emigrate. Though the Trail of Tears of 1838–39 is generally understood as the history of Cherokee Removal, this study draws from scholarship on the Long Removal Era to problematize the typical narrative. This postwar emigration phenomenon was a definitive challenge which shaped the newfound Eastern Band of Cherokee Indians. Contrary to typical characterizations, their survival in the east was not a foregone conclusion, instead facing its gravest challenges in the Civil War era. A power vacuum resulted in divisive factionalism, with rival councils from Qualla and Cheoah competing for legitimacy. The emigration of leading Cheoah politicians signaled their defeat, galvanizing and consolidating the Eastern Band. This article offers a social history of this emigration process, investigating the phenomenon through memoirs and oral histories, tribal council minutes, and petitions and correspondence to Washington. Following the paper trail of emigrants, the study incorporates federal records alongside archival records from North Carolina, Oklahoma, and Arkansas. Interrogating the overlap of the Long Removal era and Long Civil War era, the study offers a new history of the Eastern Band in its harrowing formative years.

Assembling corn varieties containing high amylopectin and high productivity

It is imperative that fluffy corn be developed as a substitute staple grain at this time. The goal of this research is to produce high-yielding corn cultivars with a high amylopectin content. This study had a randomized block design and using the Back Cross method. Plant three rows of the Srikandi Putih variety and one row of local waxy corn in the first year. F1 was created by crossing the Local Waxy Corn (♂) with the Srikandi Putih Variety (♀). F1 is planted in two rows, while F2 is produced by selfing. The second year, 105 F2 samples’ seeds were examined for amylopectin content before being chosen. After planting the chosen F2, it selfes to create F3. 100 rows of F3 were sown, and 263 F3 plants (♀) were chosen for backcrossing with the Srikandi Putih Variety (♂), yielding BC1F1. The following seed amylopectin levels tended to rise as selection proceeded, according to the results: F1 (74.25%), F2 (74.80%%), F3 (76.60%), and BC1F1 (79.15%). The following seed production per hectare was achieved between 2018 and 2019: F1 (2.93 t), F2 (3.05 t), F3 (4.61 t), and BC1F1 (5.09 t).

Light partitioning strategies impact relative fitness of weeds and cover crops when drill-interseeding in corn

AbstractDrill-interseeding cover crops into corn (Zea mays L.) is an emerging establishment method in northern U.S. production regions. However, cover crop performance in interseeded systems remains variable, and creating environments that are conducive to cover crop but not weed growth is challenging. Cultural practices that partition resources between corn and interseeded cover crops have potential to improve performance if weeds are adequately managed. This study evaluated interactions among corn hybrids differing in leaf architecture (upright, pendulum), corn row spacing (76 cm, 152 cm), and interseeding timing (V3, V6) on light transmittance, relative fitness of cover crop species (cereal rye [Secale cereale L.], annual ryegrass (Lolium multiflorum Lam), red clover [Trifolium pratense L.]) and weeds, and corn grain yield at three U.S. Northeast locations. Results showed that light transmittance through the corn canopy was greater in 152-cm row spacing compared with 76-cm row spacing at the V6 growth stage, with the magnitude of difference increasing at the V10 corn growth stage. Corn hybrids had a marginal effect on light transmittance. The effect of row spacing and interseeding timing on fall cover crop biomass varied across cover crop species and locations. In 76-cm rows, interseeding earlier (V3) increased cover crop biomass production. The relative fitness of cover crops was greater than that of weeds in each combination of cultural practices that included narrow spacing (76 cm), whereas the relative fitness of weeds was greater than that of cover crops when interseeding in wide rows (152 cm). The effect of row spacing on corn yield varied among locations, with higher yields observed in 76-cm row spacing compared with 152-cm at two of three locations. Our results show that interseeding early (V3) on 76-cm row spacing can balance cover crop and corn production management goals, while placing cover crops at a relative fitness advantage over weeds.

Fermentative parameters and chemical composition of mixed silages from corn-crotalaria intercropping

The objective of the study was to evaluate the influence of corn-crotalaria intercropping on the fermentative process, the organic acid profile, and the chemical composition of the silages produced. The trial was implemented in a randomized block design, in a 3 × 2 factorial scheme, with three types of intercrops [single corn (CSC), corn + Crotalaria juncea (CCJ), and corn + Crotalaria ochroleuca (CCO)] in two spatial arrangements [A1 (corn and legume sown in the same row) and A2 (corn and legume sown in alternate rows)], with six repetitions per treatment, evaluated in two agricultural years. The silages were evaluated for fermentative losses, pH, organic acids, ammonia nitrogen, chemical composition, and in vitro dry matter degradability. The least amount of gas loss was observed in the silages produced in the first year with CCJ and the CCO (11.6 g/kg dry matter for both). The lowest pH values were verified in the silage of the treatments CSC and CCJ (3.46 and 3.44, respectively) in the second year, and for ammoniacal nitrogen (NH3-N), the lowest concentrations were observed for CSC and CCO (120.7 and 138 g/kg total nitrogen, respectively). Higher levels of lactic acid and crude protein were obtained in the silage produced with CCJ, in both years of evaluation, whereas lower contents of acid detergent fiber were observed in the silage of the treatments CSC and CCO in the spatial arrangement A2. The largest coefficients of in vitro dry matter degradability were observed in the silages originated from CSC and CCO (820.6 and 798.0 g/kg dry matter, respectively). These results suggest that the intercropping of corn with C. ochroleuca sown in alternate rows increase the crude protein content without reducing the fermentative and nutritional quality of the silages. The planting of both legumes in the same row with corn negatively influenced the fermentative and nutritional characteristics of the silages, and this method is not recommended.

Corn Row Navigation Line Extraction Method Based on the Adaptive Edge Detection Algorithm

Aiming at the problems of long detection time and large detection error when agricultural machinery extracts corn row navigation lines, a method of corn row navigation line extraction based on the adaptive edge detection algorithm is proposed. First, the improved super-green feature algorithm and the maximum inter-class variance method are used to automatically obtain the green feature binary image, and the morphological processing is used to improve the image quality, determining dynamic regions of interest by constraining pixel thresholds, extraction of corn edge contour using adaptive edge detection algorithm, finally, the feature points are fitted by the Theil-Sen estimation method. Experimental results show: the super-green feature algorithm reflects the green content in the image more realistically, using the adaptive edge detection algorithm to extract corn row features, the accuracy rate is 94%, and the processing time of a single frame image is 104ms. Compared with the Hough algorithm extraction and the vertical projection algorithm, the navigation line extraction accuracy is increased by 15% and 8% respectively, and the time-consuming is reduced by 258ms and 150ms respectively. In addition, the stability of the algorithm is analyzed in different environments, all with good timeliness.

Nitrogen and potassium fertilization strategies in integrated systems in different black oat management on corn crop

This study aimed to evaluate levels of potassium and nitrogen fertilizer inversion in the black oat crop on corn crop yields. The experimental design was a randomized block design with subdivided plots with three repetitions. The treatments were four nitrogen fertilization strategies, four potassium fertilization strategies, and two oat crop managements (forage and cover crop). The strategies involved winter (oats) and summer (corn) application, respectively, with the following combinations of nitrogen (200-0; 150-50; 50-150; 0-200 kg N ha-1) and potassium (80-0; 60-20; 20-60; 0-80 kg K ha-1). We evaluated the morphological aspects, grain productivity, chlorophyll contents, and light interception of oats for forage production and ground cover, and for corn the yield components and productivity. Oats managed as forage showed higher forage mass production but lower values of residual biomass, canopy height, extended tiller length, lodging index, radiation interception, and oat chlorophyll A and total. Light interception in the oat crop was higher when nitrogen was applied in the pasture. For the corn crop, nitrogen applied partially on black oat and the remaining on corn row crop increases stalk diameter. Fertilization strategies showed no significant effect on both crops in the system, indicating that it is possible to reverse or apply the entire amount of N and K predicted for the two crops in winter.

Effect of the Number of Rows and Cultivars of Soybeans on Damage Intensity of Pest and Predator Populations in Corn-Soybean Intercropping, South Sulawesi Indonesia

Background: Soybean productivity in Indonesia remains low yield due to the occurrence of pests and diseases. Here, our study aimed to determine the number of soybean rows and varieties in the soybean-corn intercropping system and the damage level, pest and predator populations. Methods: The study used a split-plot design with three replications. The main plot was soybean varieties (Dena-1, Detap-1 and Deja-1) and the subplots consisted of monoculture (soybean); three rows of soybeans and two rows of corn; four rows of soybean and two rows of corn; six rows of soybean and two rows of corn. Result: The six rows of soybeans and two rows of corn showed the lowest attack rate of Spodoptera litura and Etiella zinckenella, the lowest population of Aphis glycines and Riptortus linearis, the highest predatory population of Crocothemis servilia and Coccinella sp. and the highest predator populations of Euborellia annulata. The highest attack rate of S.litura was in Dena-1. In contrast, the highest intensity of E.zinckenella pest damage was in Dena-1 and Deja-1. The corn-soybean intercropping was recommended as the best practice for sustainable agriculture production due to facilitating in the increase of growth of predator population and reducing the level of crop damage due to pest attacks.

Comparative Advantage of Productivity and Soil Properties on Corn-Soybean Intercropping to Monoculture System in Marginal Dry Land of Gunungkidul

Most of agricultural land in Gunungkidul Regency is in the form of dry land. The appropriate cropping pattern in term of optimizing land in secondary planting season is intercropping system. The aim of this study was to show the comparison between corn or soybean monoculture with corn-soybean intercropping of two different varieties on growth, yield, LER value and soil properties on dry land of Gunungkidul. This study was conducted on Kampung, Ngawen, Gunungkidul. Soybean seeds were planted 14 days before corn seeds to avoid hindering the irradiation of soybean plant. Soybean seeds were planted 5 rows between two rows of corn with a spacing 40 cm x 20 cm. The soybean varieties were used Dena-1 and Grobogan, while corn varieties used Pioneer 35 and Bisi-2. The planting distance of corn were used the legowo system, that was (65-50) cm x 30 cm. Results of the study was shown that the highest corn productivity was achieved by Pioneer 35 variety, which intercropped with Dena-1 variety, that yielded 6.73 ton. ha−1 dry corn and 1.65 ton.ha−1 soybean seeds.. This yield was lower 9.11% and 10.13%, respectively compare to monoculture system on the same corn or soybean cultivation. The advantage of intercropping corn-soybean system was added biomass of soybean, which can be used as animal feed. The chemical soil properties of corn-soybean intercropping increased on N total about 33.33%, P and K available increased 25.71% and 23.62%, respectively. Soil CEC increased about 19.53%, while soil pH did not significantly different compared to monoculture.

Towards reducing chemical usage for weed control in agriculture using UAS imagery analysis and computer vision techniques

Currently, applying uniform distribution of chemical herbicide through a sprayer without considering the spatial distribution information of crops and weeds is the most common method of controlling weeds in commercial agricultural production system. This kind of weed management practice lead to excessive amounts of chemical herbicides being applied in a given field. The objective of this study was to perform site-specific weed control (SSWC) in a corn field by: (1) using a unmanned aerial system (UAS) to map the spatial distribution information of weeds in the field; (2) creating a prescription map based on the weed distribution map, and (3) spraying the field using the prescription map and a commercial size sprayer. In this study, we assumed that plants growing outside the corn rows are weeds and they need to be controlled. The first step in implementing such an approach is identifying the corn rows. For that, we are proposing a Crop Row Identification algorithm, a computer vision algorithm that identifies corn rows on UAS imagery. After being identified, the corn rows were then removed from the imagery and remaining vegetation fraction was classified as weeds. Based on that information, a grid-based weed prescription map was created and the weed control application was implemented through a commercial-size sprayer. The decision of spraying herbicides on a particular grid was based on the presence of weeds in that grid cell. All the grids that contained at least one weed were sprayed, while the grids free of weeds were not. Using our SSWC approach, we were able to save 26.2% of the acreage from being sprayed with herbicide compared to the current method. This study presents a full workflow from UAS image collection to field weed control implementation using a commercial size sprayer, and it shows that some level of savings can potentially be obtained even in a situation with high weed infestation, which might provide an opportunity to reduce chemical usage in corn production systems.

Nitrogen Use Efficiency in an Agrisilviculture System with Gliricidia sepium in the Cerrado Region.

Gliricidia (Gliricidia sepium) is a tree legume that has great potential for use in agriculture because of its multiple-use characteristics. However, there is little information in the literature about the effect of agrisilvicultural systems on nitrogen (N) cycling. This study evaluated the effect of densities of gliricidia on N cycling under an agrisilvicultural system. The treatments were composed of different densities of gliricidia: 667, 1000 and 1333 plants ha-1, with a fixed spacing of 5 m between the alleys. The efficiency of N use was investigated by using the 15N isotope tracer. In each plot, a transect perpendicular to the tree rows was established in two positions: (i) in the corn (Zea mays) row adjacent to the trees, and (ii) in the corn row in the center of the alley. The N fertilizer recovery efficiency ranged from 39% in the density of 667 plants ha-1 to 89% with 1000 plants ha-1. The effect of gliricidia on the N uptake by corn was higher in the central position of the alley with 1000 plants ha-1. The agrisilvicultural system with 1000 plants ha-1 was highly efficient in the recovery of mineral N, representing an excellent option for integrated production systems in tropical regions.

Establishment of alfalfa interseeded into corn in response to one cycle of selection and hybridization

AbstractIntercropping alfalfa (Medicago sativa L.) in corn (Zea mays L.) can improve forage production, but wet growing conditions and high corn yields adversely impact alfalfa establishment. Therefore, we evaluated breeding alfalfa specifically for intercropping by selecting surviving plants from previous alfalfa varietal evaluations under corn and assigning them to five polycrosses to form cycle‐1 synthetics. Pollen from four polycrosses was also used to generate hybrid seed using a male‐sterility system. Corn was planted in May, intensively managed, and harvested for silage during September at Arlington and Prairie du Sac Wisconsin during 2019. Parental, polycross, and hybrid alfalfa entries were interseeded as swards between corn rows. Two subplots within each whole plot were treated with prohexadione, fungicide, and insecticide (PFI) or left as nontreated controls. Competition from high yielding corn combined with 43% above average precipitation during the growing season contributed to complete stand failure of nontreated alfalfa. Application of PFI improved foliar health, increased root mass, and enabled alfalfa establishment. Following corn silage harvest average stand density of PFI‐treated polycross entries was 35% greater than parental entries (105 vs. 78 plants m−2); broad‐sense heritability was 0.54. Overall, PFI‐treated polycross and hybrid entries had similar stand densities after corn, but posthoc comparisons indicated two hybrids had twofold greater stand density than parental entries. These results indicate plant selection can be highly effective for developing polycross and hybrid germplasm with improved survival under corn. Our findings should stimulate further breeding work to improve alfalfa and other forages for use in intercropping systems.

Interseeding Wide-Row Corn with Forage Cover Crops: Investigating System Potential for Expanded Economic Opportunities in Corn Production Systems

Intercropping forages with corn can improve cropping system productivity relative to single crop systems. However, limited light resources in 76 cm corn rows may impede successful forage establishment. This study assessed whether the combination of intercropped high value forage cover crops and wider corn rows could result in economically viable crop production systems in the Upper Midwest. A high value forage mixture was interseeded into standing corn at three working farms in the Rice and Goodhue Counties, MN, USA. Treatments were comprised of four row widths: 76 cm with no forage cover crop (best management practices, BMP), 76 cm with a forage cover crop (BMP + CC), 76 cm + CC, and two skip rows every fourth row (Balanced), and 152 cm + CC (WIDE). The WIDE, Balanced, and BMP + CC corn treatment reduced corn yields relative to the 76-cm treatments. However, the forage cover crop yields for all treatments optimized for light resources (Balanced and WIDE) ranged from 945 to 1865 kg ha−1 a forage quality (CP and RFV) equivalent to alfalfa. Our economic analysis revealed that high yielding, quality forage crops can offset up to 12.6% of economic losses caused by grain reductions. Wide-row intercropped systems may be economically viable for producers looking for opportunities to reintegrate their crop and livestock production systems, but further work is needed to refine this system for farm use.

Effect of Corn Row Spacing on Herbicide Effectiveness for Weed Control in 2022

Effect of Corn Row Spacing on Herbicide Effectiveness for Weed Control in 2022

Performance of suru sugarcane under different intercropping systems

The investigation was carried out during suru season of 2014–15, 2015–16 and 2016–17 at Agronomy Farm, Dapoli, Maharashtra, to study performance of suru sugarcane under different intercropping systems. The field experiment was laid out in randomized block design, comprising 7 treatment combinations with 3 replications. Treatments consisted of Sole sugarcane with 90-cm-row spacing, Sole paired row planted sugarcane (60 cm × 60 cm-120 cm), Paired row-planted sugarcane (PRPS) + groundnut (3 rows of groundnut at 30 cm spacing between paired row), PRPS + sweet corn (Zea mays L.) (2 rows of sweet corn at 45 cm spacing in between paired row), PRPS + cabbage (Brassica oleracea var. capitata L.) (2 rows of cabbage at 45 cm spacing in between paired row), PRPS + amaranth (4 rows of amaranths at 20 cm spacing in between paired row) and PRPS + greengram [Vigna radiata (L.) R. Wilczek] (3 rows of greengram at 30 cm spacing in between paired row). Higher yield and net returns were obtained when suru sugarcane was planted in paired rows at 60 cm × 60 cm – 120 cm and intercropped with 2 rows of sweet corn at 45 cm spacing between paired row.

Quantifying interspecies competition for water in tomato–corn intercropping system using an improved evapotranspiration model considering radiation interception by neighboring plants in two-dimensional profile

Intercropping system is widely adopted in agricultural production due its significant advantages. Usually, the radiation interception of plants with low canopy would be decreased in the vertical and horizontal directions because of the heterogeneity of the underlying surface of the intercropping system, which causes reduction in the water consumption of such plants. However, the existing evapotranspiration (ET) models consider the effect of radiation interception on water consumption only in the vertical direction.Thus, the objectives of this study were to quantify water competition between intercropping components in the tomato-corn intercropping system with various spatial arrangements and to determine suitable spatial arrangements. An improved ET model (DERIN) was deducted by inducing a two-dimensional radiation interception model based on ERIN model, and the corresponding accuracy of ET estimation was compared with that of ERIN and MERIN models. Additionally, a 2-year (2018–2019) field experiment was conducted to validate the accuracy of the model and assess interspecies competition for water in the following systems: two rows of tomato intercropping with two rows of corn (IC2–2), four rows of tomato intercropping with two rows of corn (IC4–2), sole corn (SC), and sole tomato (ST). The result showed that the DERIN modelcan precisely quantify the radiation interception by crop canopy in various intercropping systems compared with ERIN and MERIN models. Mean relative error (MRE) of ET estimated by DERIN model in various intercropping systems (i.e., IC2–2 and IC4–2) decreased by 59.9% compared with that estimated by MERIN model. Moreover, the average MRE of evaporation estimated by DERIN model decreased by 53.8% and 79.8% compared with MERIN and ERIN models, respectively. The interspecies competition for water between tomato and corn in various planting systems was similar during the entire crop growth season. The highest water competition ratio for corn (CRc) occurred in IC2–2 system among various intercropping systems, whereas IC4–2 system exhibited the highest land equivalent ratio for water (LERT). Average LERT of IC4–2 system increased by 6.7% and 5.8% compared with IC2–2 system in 2018 and 2019, respectively. Therefore, IC4–2 system can be recommended as the optimal intercropping system for sustainable agriculture development. Collectively, the DERIN model is an important tool to quantify interspecies water competition between commercial and grain crops. Also, the DERIN model is currently available method to optimize spatial arrangements of an intercropping system in different spatial position and planting orientation.

Effects of Fertilizer Level and Intercropping Planting Pattern with Corn on the Yield-Related Traits and Insect Community of Soybean

Intercropping of corn and soybean is widely practiced in agricultural production. However, few studies have investigated the effect of intercropping and fertilizer reduction on soybean yield. In the present study, corn and soybean were interplanted in 2:2, 2:3 and 2:4 ratios. Two fertilizer levels (normal: 600 kg/ha VS. reduced: 375 kg/ha) were set. The effects of fertilizer levels and intercropping planting patterns on the growth and yield of intercropping soybeans were studied based on the changes in enzyme activities related to nitrogen metabolism and insect community in the field. The results show that fertilizer reduction significantly reduced the biomass, 100-seed weight and yield of soybean. Intercropping also reduced these yield-related traits; a decreasing trend was more obvious with a decrease in soybean ratio. Intercropping had greater effect on soybean plant biomass, 100-seed weight and yield than fertilizer reduction. Reduction in fertilizer reduced the activities of nitrogen-metabolism-related enzymes in soybean. In addition to increased NR (nitrate reductase) enzyme activity in R5, intercropping planting pattern also had negative effect on the activities of nitrogen-metabolism-related enzymes in soybean. Reduced fertilizer only significantly reduced the Pielou evenness index. Reduced fertilizer application was beneficial with respect to the outbreak of greenhouse whitefly. However, an intercropping planting pattern can significantly increase the number of species, as well as the Shannon–Wiener diversity index and the Pielou evenness index of the insect community, and significantly reduce the Simpson dominance index and the population of the important pest, green leafhopper. In conclusion, C2S4 (two corn rows with four rows of soybean) is a scientific intercropping planting pattern that can reduce the occurrence of pests through ecological regulation and does not significantly reduce the activity of enzymes-related to nitrogen metabolism in most cases, ensuring soybean yield.

Combined effect of corn in the barrier crop and plant extracts against Cowpea mild mottle virus infecting soybean [Glycine max (L.) Merr.] in the field

Cowpea mild mottle virus (CpMMV) is one of the problems that can decrease soybean production. The research was conducted on the combined effects of corn in the barrier crop with plant extracts against CpMMV infecting soybean in the field. The field data was conducted using a Completely Randomized Design. The mean of disease incidence and disease severity is measured from total plants in each replicate plot on each treatment. Planting one or two of corn lines were grown at the edge four weeks before planting soybeans. Cashew nut shell (CNS), pagoda leaf, and rhizome of ginger extracts were applied using the sprayer and applied at 24 h before virus acquisition and transmission by whiteflies. The result showed that the virus incubation period ranged from 9−38 days after transmission longer than the untreated control. Planting two corn lines at the edge with CNS extract as bioactivator on soybean was the most extended incubation period of the virus and the lowest absorbance value DAS-ELISA of 0.20. There was a 73.11 % increase in the relative inhibition level of the virus. Planting corn at the edge with CNS extract proved to be more effective than soybean monoculture with CNS extract. However, soybean monoculture with CNS extract provides a better relative inhibition level of the virus (64.32 %) than planting two rows of corn on the edge combined with ginger of rhizome extract and planting two rows of corn on the edge with pagoda leaf extract as bioactivator on a soybean plant.

Design of a Reconfigurable Crop Scouting Vehicle for Row Crop Navigation: A Proof-of-Concept Study.

Pest infestation causes significant crop damage during crop production, which reduces the crop yield in terms of quality and quantity. Accurate, precise, and timely information on pest infestation is a crucial aspect of integrated pest management practices. The current manual scouting methods are time-consuming and laborious, particularly for large fields. Therefore, a fleet of scouting vehicles is proposed to monitor and collect crop information at the sub-canopy level. These vehicles would traverse large fields and collect real-time information on pest type, concentration, and infestation level. In addition to this, the developed vehicle platform would assist in collecting information on soil moisture, nutrient deficiency, and disease severity during crop growth stages. This study established a proof-of-concept of a crop scouting vehicle that can navigate through the row crops. A reconfigurable ground vehicle (RGV) was designed and fabricated. The developed prototype was tested in the laboratory and an actual field environment. Moreover, the concept of corn row detection was established by utilizing an array of low-cost ultrasonic sensors. The RGV was successful in navigating through the corn field. The RGV’s reconfigurable characteristic provides the ability to move anywhere in the field without damaging the crops. This research shows the promise of using reconfigurable robots for row crop navigation for crop scouting and monitoring which could be modular and scalable, and can be mass-produced in quick time. A fleet of these RGVs would empower the farmers to make meaningful and timely decisions for their cropping system.