A new Large Dataset and a Transfer Learning Methodology for Plant Phenotyping in Vertical Farms

Vertical farming has emerged as a solution to enhance crop cultivation efficiency and overcome limitations in conventional farming methods. Yet, abiotic stresses significantly impact crop quality and increase the risk of food loss. The integration of advanced automation, sensor technology, and deep learning models offers a promising solution for quality monitoring addressing the limitations of stress-specific approaches. Due to the large range of possible quality issues, there is a need for a general method. This study proposes a new plant canopy dataset, dubbed AGM of 1M images, annotated with 18 classes, an in-depth analysis of its quality for its use in transfer learning, and a methodology for detecting canopy stresses in vertical farming. The present study trains ViTbase8, ViTsmall8, and ResNet50 both on ImageNet and the proposed dataset on crop classification. Features from AGM and ImageNet are used for a downstream task on healthy and stress detection using a small annotated validation dataset obtaining 0.97%, 0.93%, and 0.92% best accuracy with the AGM features. We compare with standard datasets like Cassava, PlantDoc, and RicePlant obtaining significant accuracy. This research contributes to improved crop quality, prolonged shelf life, and optimized nutrient content in vertical farming, enhancing our understanding of abiotic stress management.