Deep Learning-Based Method for Irrigation Status Detection in Tomato Using Plant Leaves

he impact of climate change, arguably the global warming and resulting drought, is one of the most escalating agricultural challenges affecting crop productivity. Therefore, effective water management is critical in agricultural practices.he impact of climate change, arguably the global warming and resulting drought, is one of the most escalating agricultural challenges affecting crop productivity. Therefore, effective water management is critical in agricultural practices.T The analysis of plant leaves presents an opportunity to gauge irrigation status through automated solutions to encourage broader adoption among farmers. Currently, there is a notable absence of AI methods in the literature for detecting tomato plant irrigation status through leaf analysis. Addressing this gap, we propose a novel end-to-end deep learning (DL)-based method, inspired by the ResNet-50 model. Our model trims unnecessary blocks and reduces larger kernels, significantly streamlining the model to better fit with the leaf image dataset related to the tomato irrigation status. We evaluate our method using a newly developed dataset and find outstanding performance (Precision: 99.05%, Recall: 99.01%, F1-score: 99.01%, mean-average F1: 98.98%, weighted-average F1: 98.95%, Kappa: 98.61%, accuracy: 98.90%) while comparing with the pre-trained DL models. Additionally, our model has fewer parameters and lower floatingpoint operations (FLOPs), enhancing its efficiency and suggesting its potential for more cost-effective and productive irrigation management practices. Impact Statement-The proposed photograph-based leaf water stress detection technology provides the water quantity required in tomato plants. This establishes a foundation for the next step: developing the model as an application (either in mobile device or in server) for effective management of limited water resources. This application will enable users to check water stress and apply precise amount of water either manually or through the automation of existing irrigation systems such as drip, sprinkler, subsurface, or other systems. This approach saves water while optimizing the productivity. Additionally, the technology is affordable and convenient to use for small scale farmers. The proposed technology has the potential to be applied to a range of crops and geographies, provided the model is adapted to fit the specific crops and topographies.