Empowering vertical farming through IoT and AI-Driven technologies: A comprehensive review

The substantial increase in the human population dramatically strains food supplies. Farmers need healthy soil and natural minerals for traditional farming, and production takes a little longer time. The soil-free farming method known as vertical farming (VF) requires a small land and consumes a very small amount of water than conventional soil-dependent farming techniques. With modern technologies like hydroponics, aeroponics, and aquaponics, the notion of the VF appears to have a promising future in urban areas where farming land is very expensive and scarce. VF faces difficulty in the simultaneous monitoring of multiple indicators, nutrition advice, and plant diagnosis systems. However, these issues can be resolved by implementing current technical advancements like artificial intelligence (AI)-based control techniques such as machine learning (ML), deep learning (DL), the internet of things (IoT), image processing as well as computer vision. This article presents a thorough analysis of ML and IoT applications in VF system. The areas on which the attention is concentrated include disease detection, crop yield prediction, nutrition, and irrigation control management. In order to predict crop yield and crop diseases, the computer vision technique is investigated in view of the classification of distinct collections of crop images. This article also illustrates ML and IoT-based VF systems that can raise product quality and production over the long term. Assessment and evaluation of the knowledge-based VF system have also been outlined in the article with the potential outcomes, advantages, and limitations of ML and IoT in the VF system.