To better understand cells, diseases, and how to treat them, researchers focus on protein-protein interactions (PPIs). Since there are many difficulties in food production due to the environment, studying interactomes can ease these problems and help reveal new facts about how crops interact with pathogens. To study PPIs in plants, researchers rely on yeast two-hybrid (Y2H) systems, affinity purification with mass spectrometry (AP-MS), bimolecular fluorescence complementation (BiFC), and computational prediction programmes. Yet, they have some problems, including a high error rate and the difficulty in obtaining similar results on repeating the same experiments. The areas of machine learning and deep learning within AI have helped in PPIs by boosting the accuracy, speed, and ability to work on large datasets. AI is being applied more often to plant protein-protein interaction networks and interactomes, helping people to better understand the details of biological systems that will determine the future prospects of agriculture and global food security.

Keywords: Plant Proteomics; Artificial Intelligence; Protein-Protein Interaction Networks; Host-Pathogen Interactions; Agricultural Biotechnology.