The overuse of pesticides has led to resistance in phytopathogens, posing significant threats to global food security and environmental health. Antimicrobial peptides (AMPs), small molecules produced by various organisms as part of their innate immune defense, exhibit broad-spectrum antimicrobial activity with a lower risk of resistance development. These properties make AMPs promising candidates for sustainable agricultural practices. However, challenges such as high production costs, instability, and potential toxicity to plant cells have hindered their widespread application. This review provides a comprehensive overview of the discovery, classification, and antimicrobial mechanisms of AMPs, focusing on their roles in plant protection. It also explores strategies for identifying and optimizing AMPs, including structural modifications, targeted delivery systems, and production methods using plant- and microbe-based expression systems. Additionally, the review highlights the potential of transgenic approaches to enhance crop resistance by expressing AMP genes in plants. Despite the challenges, AMPs offer a transformative opportunity for modern agriculture, providing innovative solutions to combat plant diseases while reducing reliance on conventional pesticides. Continued research and technological advancements are essential to fully realize the potential of AMPs in sustainable plant protection.

Chilling injury (CI) during postharvest storage seriously impairs bananas’ quality and marketability. This study systematically investigated CI mechanisms through physicochemical, microstructural, and optical analyses and innovatively developed a hyperspectral imaging (HSI)-based approach for early CI detection. Bananas stored at suboptimal (7 °C) and optimal (13 °C) conditions exhibited distinct physicochemical changes. CI progression was related to increased browning symptoms, an abnormal moisture redistribution (reduced pulp moisture content), and delayed softening. Microstructural analysis revealed membrane destabilization, cellular lysis, intercellular cavity formation, and inhibited starch hydrolysis under chilling stress. Hyperspectral microscope imaging (HMI) captured chilling-induced spectral variations (400–1000 nm), enabling the t-SNE-based clustering of CI-affected tissues. Machine learning models using first derivative (1-st)-processed spectra achieved a high accuracy. Both PLS-DA and RF had a 99% calibration accuracy and 98.5% prediction accuracy for CI classification. Notably, HSI detected spectral signatures of early CI (2 days post-chilling treatment) before visible symptoms, achieving a 100% identification accuracy with an optimized PLS-DA combined with 1-st processing. This study provides a theoretical basis for studying fruit CI mechanisms and a novel nondestructive optical method for early CI monitoring in postharvest supply chains.