Greenhouse technology is a flexible solution for sustainable year-round cultivation of Tomato (Lycopersicon esculentum Mill), particularly in regions with adverse climate conditions or limited land and resources. Accurate knowledge about plant requirements at different growth stages, and under various light conditions, can contribute to the design of adaptive control strategies for a more cost-effective and competitive production. In this context, different scientific publications have recommended different values of microclimate parameters at different tomato growth stages. This paper provides a detailed summary of optimal, marginal and failure air and root-zone temperatures, relative humidity and vapour pressure deficit for successful greenhouse cultivation of tomato. Graphical representations of the membership function model to define the optimality degrees of these three parameters are included with a view to determining how close the greenhouse microclimate is to the optimal condition. Several production constraints have also been discussed to highlight the short and long-term effects of adverse microclimate conditions on the quality and yield of tomato, which are associated with interactions between suboptimal parameters, greenhouse environment and growth responses.

Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture

The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.

Digital agriculture involving different tools and management practices has advanced considerably in recent years, intending to overcome climate risk and reduce food insecurity. Climate change and its impacts on agricultural production and food security are significant sources of public concern worldwide. The objective of this study was to provide an overview of the potential impact of digital agriculture technologies and practices that can reduce greenhouse gas emissions and enhance productivity while ensuring food security. Based on a comprehensive survey of the previously published works, it was found that due to global warming, altered precipitation patterns, and an increase in the frequency of extreme events, climate change has negatively impacted food security by reducing agricultural yields, slowing animal growth rates, and decreasing livestock productivity. The reviewed works also suggest that using digital technology in agriculture is necessary to mitigate the effect of climate change and food insecurity. In addition, issues regarding creating sustainable agricultural food systems, minimizing environmental pollution, increasing yields, providing fair and equitable food distribution, and reducing malnutrition leading to food security were discussed in detail. It was shown that while digital agriculture has a crucial role in mitigating climate change and ensuring food security, it requires a concerted effort from policymakers, researchers, and farmers to ensure that the benefits of digitalization are realized in a sustainable and equitable manner.

Microsatellites, or simple sequence repeats (SSRs), are one of the most informative and multi-purpose genetic markers exploited in plant functional genomics. However, the discovery of SSRs and development using traditional methods are laborious, time-consuming, and costly. Recently, the availability of high-throughput sequencing technologies has enabled researchers to identify a substantial number of microsatellites at less cost and effort than traditional approaches. Illumina is a noteworthy transcriptome sequencing technology that is currently used in SSR marker development. Although 454 pyrosequencing datasets can be used for SSR development, this type of sequencing is no longer supported. This review aims to present an overview of the next generation sequencing, with a focus on the efficient use of de novo transcriptome sequencing (RNA-Seq) and related tools for mining and development of microsatellites in plants.