Inefficiencies and imprecise input control in agriculture have caused devastating consequences to ecosystems. Urban controlled environment agriculture (CEA) is a proposed approach to mitigate the impacts of cultivation, but precise control of inputs (i.e., nutrient, water, etc.) is limited by the ability to monitor dynamic conditions. Current mechanistic and physiological plant growth models (MPMs) have not yet been unified and have uncovered knowledge gaps of the complex interplay among control variables. Moreover, because of their specificity, MPMs are of limited utility when extended to additional plant species or environmental conditions. Simultaneously, although machine learning (ML) can uncover latent interactions across conditions, phenotyping bottlenecks have hindered successful application. To bridge these gaps, we propose an integrative approach whereby MPMs are used to construct the foundations of ML algorithms, reducing data requirements and costs, and ML is used to elucidate parameters and causal inference in MPM. This review highlights research about control and automation in CEA, synthesizing literature into a framework whereby ML, MPM, and biofeedback inform what we call dynamically controlled environment agriculture (DCEA). We highlight synergistic characteristics of MPM and ML to illustrate that a DCEA framework could contribute to urban resilience, human health, and optimized productivity and nutritional content.
La consommation de produits certifiés n’est plus l’apanage des pays développés. Au Kenya, les premiers marchés biologiques sont apparus à Nairobi en 2006. Ils sont approvisionnés par des maraîchers, confrontés à une diversité de défis : construire une certification biologique...
The objective of this paper is to explore the extent to which systems approaches to innovation are reflected in the crop protection literature and how such approaches are used. A systematic literature review is conducted to study the relation between...
L’eau d’irrigation est une ressource cruciale pour le développement économique et social en Tunisie. Dans un contexte de décentralisation et de délégation du rôle de l’État, une part importante de la gestion de cette eau d’irrigation a été confiée aux...
Asian agriculture is faced with major new challenges as a result of globalisation, urbanisation and environmental problems such as climate change. To meet these challenges, Asian agriculture needs to become more knowledge intensive and innovation oriented. This article frames the...
L’ouest du Rio Grande do Sul est dominé par la culture du soja, du riz et par l’élevage bovin. Dans la partie sableuse, le milieu est affecté par des phénomènes d’érosion produisant des modelés éoliens spectaculaires (arenização) rappelant dans l’imaginaire...