Traditional plant phenotyping methods are characterized by low efficiency and high subjectivity, which severely constrain the pace of genetic improvement and the understanding of plant responses to environmental stresses. This review systematically evaluates multiple imaging techniques for addressing key biological questions concerning plant responses to drought, nutrient deficiency, and salt stress, with the aim of providing a technical roadmap for high-throughput, high-precision, and cost-effective phenotyping that can support smart breeding and precision agriculture.

More than 210 peer-reviewed studies published between 2015 and 2026 are analyzed, and the techniques are compared across three functional dimensions: internal structure imaging, physiological and biochemical imaging, and three-dimensional structural imaging. For each technique, the measurement principles, principal areas of applicability, and current limitations are systematically assessed. Optical imaging constitutes the primary modality for analysis of surface morphology and biochemical composition, stress diagnosis, and three-dimensional structural reconstruction. Non-optical imaging techniques offer unique and irreplaceable complementary value for internal structure analysis, water dynamics monitoring, and spatial visualization of metabolite distribution, although their application in plant phenotyping remains more limited than that of optical methods. The performance of deep learning and swarm intelligence algorithms in noise reduction, dimensionality reduction, image segmentation, feature extraction, and phenotype–environment association modeling is also evaluated. Multimodal data fusion reduces prediction errors relative to single-sensor approaches, reflecting the fact that no single imaging technique can simultaneously satisfy the dual requirements of high throughput and high precision. The technical evolution from single-modality measurement to multimodal fusion analysis, from laboratory settings to field applications, and from traditional machine learning to deep learning and swarm intelligence optimization is reviewed systematically from three perspectives: optical–non-optical data synergy, multimodal fusion architectures based on deep learning and swarm intelligence, and cross-level integration of phenomics and genomics.

Future advances should focus on three directions: the development of low-cost, lightweight imaging sensors suitable for high-throughput field phenotyping platforms while retaining high precision; the design of cross-scale registration algorithms capable of aligning data acquired by different sensors and at different spatial scales; and the construction of interpretable phenotype–genotype association models that integrate plant physiological data with deep learning and swarm intelligence algorithms. Standardized quantitative evaluation systems for assessing fusion effectiveness are also urgently required. Realization of these goals will furnish critical technical support for smart breeding and precision agriculture, and will ultimately accelerate understanding of plant responses to drought, nutrient stress, salt stress, and other key biological challenges.