Smart Farming enabled by IoT and Spectral Imaging

Smart Farming System is an emerging concept which utilizes sensors in the field enabled through IoT to get live data from the farm. This paper aims at developing such a Smart Farming system using the highly advanced technology of Texas instruments microcontrollers, MSP430 and TIVA C Series TM4C1294. Along with IoT the system uses Multispectral Imaging in conjunction with Wireless Soil Embedded Sensor Networks. The goal of the system is to provide reliable live data which is obtained from the multiple sensor nodes placed throughout the farm, that use the sink nodes to transfer the data to the cloud. The farmer can access this data using the Blynk Mobile app and can thus take further calculated actions towards maintaining the farm and further monitor the soil/crop health to increase the ultimate yield from his farm.

Genomics and informatics, conjoined tools vital for understanding and protecting plant health

Genomics’ impact on crop production continuously expands. The number of sequenced plant and microbial species and strains representing diverse populations of individual species rapidly increases thanks to the advent of next generation sequencing technologies. Their genomic blueprints revealed candidate genes involved in various functions and processes crucial for crop health and helped understand how the sequenced organisms have evolved at the genome level. Functional genomics quickly translates these blueprints into a detailed mechanistic understanding of how such functions and processes work and are regulated; this understanding guides and empowers efforts to protect crops from diverse biotic and abiotic threats. Metagenome analyses help identify candidate microbes crucial for crop health and uncover how microbial communities associated with crop production respond to environmental conditions and cultural practices, presenting opportunities to enhance crop health by judiciously configuring microbial communities. Efficient conversion of disparate types of massive genomics data into actionable knowledge requires a robust informatics infrastructure supporting data preservation, analysis, and sharing. This review starts with an overview of how genomics came about and has quickly transformed life science. We illuminate how genomics and informatics can be applied to investigate various crop health-related problems using selected studies. We end the review by noting why community empowerment via crowdsourcing is crucial to harnessing genomics to protect global food and nutrition security without continuously expanding the environmental footprint of crop production.

Farmland and Crop Quality Improvement for Heavy Metals Based on Six Sigma

Scientific methodology of soil and farmland health management and crop quality improvement is urgent to set up. Our objective was to recognize and sweep away the choke point and improve the farmland health course by applying the philosophy and method of six sigma. Farmland and crop quality improvement for heavy metals belongs to Systems Engineering. SIPOC flux of heavy metals indicates the key process by causal factor analysis through quantitative way. Multi-source and multi-scale data suggest that key process of performance is source control. Measurement processes analyse data and eliminate root causes to improve sigma levels. The study was conducted on farmland and crop health management with concern at the inputs and mechanism that had choke point and high defect rate. DMAIC of sources and exports of cd in the soil will improve and control the Quality of crop and farmland. The premise of implementing six sigma is that the chosen metrics are measurable and effective, but we find a lack of good correlation between terminal index and intermediate index in heavy metal pollution. Suggestion is as follows: Big data could be shared and processed intelligently; policy and culture to pollution-free is more important to farmland and crop health by six sigma.

Detection of the Phoma pathogens Plenodomus biglobosus subclades ‘brassicae’ and ‘canadensis’ on wasabi, and ‘canadensis’ in Europe

Phoma stem canker / blackleg is an internationally important disease of Brassicas including B. napus (oilseed rape, OSR), caused by multiple genetic subclades of the fungi Plenodomus lingam (formerly Leptosphaeria maculans) and P. biglobosus (L. biglobosa). In Spring 2021, Phoma-like disease symptoms were observed on leaves and stems of Eutrema japonicum (wasabi) crops at three UK sites (Northern Ireland, Southern England and the West Midlands). Fungal isolation from wasabi leaf spots yielded colonies with two distinct phenotypes on potato dextrose agar (PDA). Isolates from the Northern Ireland and Southern England sites had white colonies with abundant pink cirri that were confirmed (based on ITS rDNA, beta tubulin and actin sequences) as P. biglobosus subclade ‘canadensis’ (Pbc). Those from the West Midlands site, however, had yellow pigmented colonies and were confirmed by sequencing as P. biglobosus subclade ‘brassicae’ (Pbb). Greenhouse pathogenicity testing showed that Pbb and Pbc wasabi isolates were pathogenic not only to this host but also OSR, B. oleracea (cabbage), and B. rapa (pak choi). Re-isolation of the fungi was attempted and confirmed from lesions that developed on inoculated OSR and wasabi, thus completing Koch’s postulates. These findings represent new discoveries for both Pbb and Pbc on wasabi, plus for Pbc in Europe. The crop health implications of these results are briefly considered.

LegumeSSRdb: A Comprehensive Microsatellite Marker Database of Legumes for Germplasm Characterization and Crop Improvement

Microsatellites, or simple sequence repeats (SSRs), are polymorphic loci that play a major role as molecular markers for genome analysis and plant breeding. The legume SSR database is a webserver which contains simple sequence repeats (SSRs) from genomes of 13 legume species. A total of 3,706,276 SSRs are present in the database, 698,509 of which are genic SSRs, and 3,007,772 are non-genic. This webserver is an integrated tool to perform end-to-end marker selection right from generating SSRs to designing and validating primers, visualizing the results and blasting the genomic sequences at one place without juggling between several resources. The user-friendly web interface allows users to browse SSRs based on the genomic region, chromosome, motif type, repeat motif sequence, frequency of motif, and advanced searches allow users to search based on chromosome location range and length of SSR. Users can give their desired flanking region around repeat and obtain the sequence, they can explore the genes in which the SSRs are present or the genes between which the SSRs are bound design custom primers, and perform in silico validation using PCR. An SSR prediction pipeline is implemented where the user can submit their genomic sequence to generate SSRs. This webserver will be frequently updated with more species, in time. We believe that legumeSSRdb would be a useful resource for marker-assisted selection and mapping quantitative trait loci (QTLs) to practice genomic selection and improve crop health. The database can be freely accessed at http://bioinfo.usu.edu/legumeSSRdb/.

Evaluation of Agronomic Performance of Maize (Zea mays L.) under a Fertilization Gradient in Transylvanian Plain

In the last few years, Romania has become a top maize producer. Export potential is sustained by ensuring high-quantity and -quality maize. Success of maize crop is highly dependent on inputs. In this context, insight into the potential of different fertilizers to maximize crop performance could shed light on best practices to enhance yields and other traits of interest. The aim of this study was to assess the agronomic performance of maize under a fertilization gradient. Six fertilizer regimes were tested on three maize hybrids between 2018 and 2020, in conditions from the Transylvanian Plain. Results showed that fertilization had a significant influence on yield, thousand kernels weight, grain quality (starch and protein content) and crop health. The experimental year also played a significant role in the expression of productivity potential of maize genotypes. Different fertilizer regimes could be used for targeting desired outcomes, but top performance across all or multiple agronomic components remains a challenge and should receive further attention for optimization.