Abstract Understanding how plants respond to environmental conditions such as temperature, CO2, humidity, and light radiation is essential for plant growth. This paper proposes an Artificial Neural Network (ANN) model to predict plant response to environmental conditions to enhance crop production systems that improve plant performance and resource use efficiency (e.g. light, fertiliser and water) in a Chinese Solar Greenhouse. Comprehensive data collection has been conducted in a greenhouse environment to validate the proposed prediction model. Specifically, the data has been collected from the CSG in warm and cold weather. This paper confirms that CSG’s passive insulation and heating system was effective in providing adequate protection during the winter. In particular, the CSG average indoor temperature was 18 $$^{\circ }$$ ∘ C higher than the outdoor temperature. The difference in environmental conditions led to a yield of 320.8g per head in the winter after 60 growing days compared to 258.9g in the spring experiment after just 35 days. Three different architectures of Bayesian Neural Networks (BNN) models have been evaluated to predict plant response to environmental conditions. The results show that the BNN network is accurate in modelling and predicting crop performance.

Genome-editing biotechnologies, such as clustered regularly inter-spaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas), are disrupting both the research and commercial genomics industries. Genome editing has become a major tool within both the Fourth Industrial Revolution and Green Revolution 2.0, making claim to revolutionise healthcare, environmental conservation, agriculture, and food security. With the technology rapidly advancing the fields of plant genomics and breeding, an increasing number of gene-edited food crops are now launching to both industry and consumer markets. New market-ready gene-edited food crops and products include a healthier soya bean oil and the first consumer-harvested CRISPR/Cas crop, the Sicilian Rouge High GABA tomato. However, as gene-edited crops begin to arrive on the market, debates previously seen with GMOs surrounding social acceptance, bioethics, and changing regulatory environments for agriculture and food safety are being renewed. In this article, we provide an overview of plant genome-editing technologies, including CRISPR/Cas, transcription activator-like effector nucleases (TALENs), and zinc finger nucleases (ZFNs); market-ready gene-edited food crops; and regulatory environments across the world.

Threats from climate change, environmental degradation, and exhausted Green Revolution technologies have caused widespread concern for the future of global crop production and food security. Today, the agri-sector is transitioning on a global scale, away from traditional farming practices and towards adopting the novel technologies needed to sustainably intensify food production. With the global population projected to reach nine billion by 2050, with 68% of us living in urban environments, it is essential to find ways of producing food crops sustainably both in cities as well as out in the field. Modern innovation presents an opportunity to remedy these issues through the implementation of novel technologies and practices, including vertical farming, light supplementation via light-emitting diodes (LEDs), artificial intelligence (AI), advances within plant breeding genetics, and the application of biostimulants made from organic extracts. Throughout this review, we will provide insights into the future of farming by discussing a range of contemporary agricultural techniques and technologies, alongside the cultural, economic, and environmental contexts that necessitate their increased application.