UrbanAgri: a transfer learning-based plant stress identification framework for sustainable smart urban growth

The speed of urbanization around the world is decreasing the arable land endangering food security since the population is estimated to reach 9.7 billion by the year 2050. Urban agriculture provides a long-term solution to food production in urban areas but has issues of good monitoring of plant diseases because growing areas are fragmented, microclimates change, and resources are limited. However, biotic (e.g., pathogens) and abiotic stresses must be accurately detected to reduce wastage in crop and ensure sustainability in urban farming ecosystems. This paper will suggest a new deep learning model that integrates ResNet101 and the Sparrow Search Optimization (SSO) algorithm to identify plant stress in urban agriculture environments. Based on the capabilities of transfer learning, the model makes use of optimal feature extraction with small datasets, resolving the issue of data scarcity in cities. The framework was trained and evaluated based on a heterogeneous dataset of urban crop images, inclusive of multifactorial stress indicators on variable conditions. ResNet101 + SSO reached an F1-score of 98.9, and ROC-AUC of 0.989, which is better than the traditional approaches (RandomForest: 92.3% F1; KNN 89.7% F1). It showed great accuracy in detecting both biotic and abiotic stress factors, which allows the timely detection of the broken urban farms. This solution promotes sustainable urban agriculture by minimizing the waste of crops by monitoring stress accurately and at scale. The model is developed to support smart city objectives of improving food security and resources sustainability, which is tailored to city settings with limited resources. The future planning of work will be to combine real-time data of IoT sensors and make the model applicable to various types of crops used in urban areas.