CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements

The phenotypic characterization of crop genotypes is an essential, yet challenging, aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agricultural research due to the diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

Tobit model-based analysis on the influencing factors of wheat loss during harvesting by the combine harvester

Based on the investigation data of wheat mechanized harvest in eight major wheat producing areas from the south to the north of Henan Province, the main factors affecting wheat mechanized harvest loss were identified and the influence of each factor was decomposed. In this article, the loss rate of wheat mechanical harvest was calculated by using the method of artificial measurement of wheat yield in the field, and the influencing factors of wheat mechanical harvest operation in 8 regions of Henan province were treated and analyzed by using Tobit regression model. In this paper, the loss rate of wheat mechanical harvest was calculated by using the method of wheat field artificial yield measurement and the influencing factors of wheat mechanical harvest operation in eight regions of Henan province were treated and analyzed by using Tobit regression model. The results show that the average harvest loss rate in the field amounts to 2.96%, the average harvest loss rate at the edge of field amounts to 3.06%, whereas the loss rate in the normal operation area amounts 2.86%. The main factors that caused the harvest loss of wheat field machinery were the maturity of wheat, the area of operation field, the diseases and pests, weather conditions and the accumulated working hours of harvester drivers in a single day. Therefore, the main technical measures to reduce the operation loss of wheat combine harvester were put forward to provide a theoretical basis for promoting the deep integration of agricultural machinery and agronomy.

Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China

It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMESA−B), Y = 0.801x + 16.925 (CMESA−C), and Y = 0.959x + 3.458 (CMESC−D); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R2 are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMESA−B models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMESA−C and CI-BPNN-CMESA−C-CMESC−D models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas.

Allelopathic Effects of Chrysanthemoides monilifera subsp. monilifera on Lolium rigidum in Wheat Field: Implications on the Reduction of Chemical Loads in Agro-Ecosystems

Weed control through allelopathic plants is a promising approach that may minimize many of negative consequences of synthetic herbicides. We have studied potential of Chrysanthemoides monilifera subsp. monilifera (boneseed) leaf extract for controlling growth of Lolium rigidum (annual ryegrass) in wheat (Triticum aestivum) fields. Both pre-and post-emergent ryegrass-control experiments were conducted in greenhouse using field soil. Treatments such as boneseed leaf extracts (5 and 10% for pre-emergent and 10 and 20% for post-emergent experiments) alone or as a mixture combined with different strength (¼ and ½ strength) of pre-emergent (boxer gold) and post-emergent (hussar OD) herbicides were applied on pre- and post-emergent ryegrass and wheat. The findings revealed that none of the boneseed leaf extracts alone or as mixture had significant inhibitory impact on pre-emergent ryegrass compared with herbicide alone. Although we observed significant inhibitory impacts on post-emergent ryegrass with boneseed leaf extracts alone (10 and 20%) compared with control, they were negligible compared to full strength herbicides. Mixtures had significant inhibitory impact on post-emergent ryegrass compared with herbicide alone with same doses and impact increased with herbicide concentration. Despite the greater impacts by higher herbicides concentration alone, findings suggest the use of mixture of ¼-strength herbicide and 10% boneseed leaf extract was able to control ryegrass successfully than the herbicide alone without adverse impacts on wheat. This study suggests that use of boneseed leaf extract mixed with lower doses of post-emergent herbicides may be effective in controlling ryegrass with concomitant reductions in expenses and ecological health risks linked with the practice of synthetic herbicides.

Prevalence and correlates of phenazine resistance in culturable bacteria from a dryland wheat field

Phenazines are a class of bacterially-produced redox-active natural antibiotics that have demonstrated potential as a sustainable alternative to traditional pesticides for the biocontrol of fungal crop diseases. However, the prevalence of bacterial resistance to agriculturally-relevant phenazines is poorly understood, limiting both the understanding of how these molecules might shape rhizosphere bacterial communities and the ability to perform risk assessment for off-target effects. Here, we describe profiles of susceptibility to the antifungal agent phenazine-1-carboxylic acid (PCA) across more than 100 bacterial strains isolated from a wheat field where PCA producers are indigenous and abundant. We find that Gram-positive bacteria are typically more sensitive to PCA than Gram-negative bacteria, but that there is also significant variability in susceptibility both within and across phyla. Phenazine-resistant strains are more likely to be isolated from the wheat rhizosphere, where PCA producers are also more abundant, compared to bulk soil. Furthermore, PCA toxicity is pH-dependent for most susceptible strains and broadly correlates with PCA reduction rates, suggesting that uptake and redox-cycling are important determinants of phenazine toxicity. Our results shed light on which classes of bacteria are most likely to be susceptible to phenazine toxicity in acidic or neutral soils. In addition, the taxonomic and phenotypic diversity of our strain collection represents a valuable resource for future studies on the role of natural antibiotics in shaping wheat rhizosphere communities.

CBM: An IoT Enabled LiDAR Sensor for In-field Crop Height and Biomass Measurements

Phenotypic characterization of crop genotypes is an essential yet challenging aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agriculture research due to diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. Phenotypic traits of crop fresh biomass, dry biomass, and plant height estimated by CBM data had high correlation with ground truth manual measurements in wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

Changes in Bacterial Soil Biota under Cultivation of Crops Near a Municipal Landfill Site

The study aimed to evaluate the changes in the quantitative composition of a soil bacterial community near a municipal waste landfill, and attempted to use a bacteriological coefficient to assess the degree of soil degradation. The research was carried out near a landfill site located in southern Poland. Soil samples were collected from plots on which spring wheat, field bean and potato were cultivated. Microbiological analyses included the determination of the total number of bacteria in active and dormant (sporulating) stages. The highest ratio of sporulating bacteria in relation to vegetative bacteria was found in the reclaimed sector of the landfill site. The proposed bacteriological indicator of soil quality (i.e., the ratio of the number of sporulating bacteria to the number of vegetative forms) seems to be a good index for the assessment of soil quality near the landfill site.

Population-level deep sequencing reveals the interplay of clonal and sexual reproduction in the fungal wheat pathogen Zymoseptoria tritici

Pathogens cause significant challenges to global food security. On annual crops, pathogens must re-infect from environmental sources in every growing season. Fungal pathogens have evolved mixed reproductive strategies to cope with the distinct challenges of colonizing growing plants. However, how pathogen diversity evolves during growing seasons remains largely unknown. Here, we performed a deep hierarchical sampling in a single experimental wheat field infected by the major fungal pathogen Zymoseptoria tritici. We analysed whole genome sequences of 177 isolates collected from 12 distinct cultivars replicated in space at three time points of the growing season to maximize capture of genetic diversity. The field population was highly diverse with 37 SNPs per kilobase, a linkage disequilibrium decay within 200–700 bp and a high effective population size. Using experimental infections, we tested a subset of the collected isolates on the dominant cultivar planted in the field. However, we found no significant difference in virulence of isolates collected from the same cultivar compared to isolates collected on other cultivars. About 20 % of the isolate genotypes were grouped into 15 clonal groups. Pairs of clones were disproportionally found at short distances (<5 m), consistent with experimental estimates for per-generation dispersal distances performed in the same field. This confirms predominant leaf-to-leaf transmission during the growing season. Surprisingly, levels of clonality did not increase over time in the field although reproduction is thought to be exclusively asexual during the growing season. Our study shows that the pathogen establishes vast and stable gene pools in single fields. Monitoring short-term evolutionary changes in crop pathogens will inform more durable strategies to contain diseases.