The valence–space metaphor posits that emotion concepts map onto vertical space such that positive concepts are in upper locations and negative in lower locations. Whilst previous studies have demonstrated this pattern for positive and negative emotions e.g. ‘joy’ and ‘sadness’, the spatial location of neutral emotions, e.g. ‘surprise’, has not been investigated, and little is known about the effect of linguistic background. In this study, we first characterised the emotions joy, surprise and sadness via ratings of their concreteness, imageability, context availability and valence before examining the allocation of these emotions in vertical space. Participants from six linguistic groups completed either a rating task used to characterise the emotions or a word allocation task to implicitly assess where these emotions are positioned in vertical space. Our findings suggest that, across languages, gender, handedness, and ages, positive emotions are located in upper spatial locations and negative emotions in lower spatial locations. In addition, we found that the neutral emotional valence of surprise is reflected in this emotion being mapped mid-way between upper and lower locations onto the vertical plane. This novel finding indicates that the location of a concept on the vertical plane mimics the concept’s degree of emotional valence.

Vertical farms have become increasingly popular in today’s society. Its popularity owes to the increasing relevance of vertical farms as a panacea for the effect of desertification and urbanization as proposed by experts and researchers. Urbanization is increasing annually, as at the middle of year 2020 according to statistica.com the rate of urbanization was at 56 percent, and it has been estimated that by the year 2050 about 70-90% of global population will live in cities. The tripod effect of urbanization, desertification and climate change has made vertical farms a convenient alternative. While vertical farm is helping to solve this problem, the interface of computers with vertical farms has exponentially increased the efficiency, it has helped to create convenient environments which can be controlled, as such facilitating and all-around production of food crops all through the year despite changing environmental conditions. In this paper, we are taking a step further to see how computer vision can help in this process. So far IoT has been used to monitor the farm extrinsic factors, and get relevant data, the problem with that method is that only the external factors are being monitored, in this paper we will be exploring how computer vision can monitor intrinsic factors, but beyond that, we will also explore how computer vision and machine learning methods could be used together with IoT for the control of vertical farms as well to create favorable conditions for the planting of vertical farms.