With AI4Science (AI-driven scientific discovery) emerging as a new paradigm for scientific discovery, artificial intelligence (AI) is reshaping plant science toward data-driven and intelligent frameworks. Roots, as the key organs responsible for water and nutrient acquisition, environmental sensing, and above-belowground interactions, are decisive for crop productivity and ecosystem functioning. However, due to their hidden growth in soil, structural complexity, and limited observability, our understanding of root structure and function has long lagged behind that of aboveground parts. Recent advances in artificial intelligence have provided new tools and pathways for decoding the “underground black box.” Leveraging AI as a core driving force to integrate multi-source root and environmental data, cutting-edge algorithms, and the broader knowledge base of root science has increasingly become a frontier direction in belowground research. In this context, we outline AI4Root, which summarizes the major applications and emerging trends of AI in root studies, encompassing data acquisition, structural modeling, mechanistic inference, and management decision support. AI-based image analysis enables automated recognition and quantification of root architectures, while modeling and data-fusion approaches reveal the multi-scale and multi-process interactions between roots and soil. AI also shows great potential in root phenotyping, architectural simulation, and deciphering root exudate-microbiome interactions, offering unprecedented tools to advance our understanding of root function. However, the application of AI in root research still faces several challenges, including the difficulty of acquiring high-quality field data, insufficient integration across experimental scales, and limited capacity to model the complexity of root-soil-environment interactions. Looking ahead, the deep integration of multi-source data, cross-disciplinary algorithmic advancements, and the coordinated development of digital agriculture platforms are expected to propel root science into a new era of intelligent research, offering innovative pathways for crop improvement and the advancement of smart agriculture.

Global climate change and intensive human activities have led to increasing degradation of arable land due to ionic stress, such as salinization and heavy metal contamination, posing a significant threat to food security. The rapid development of artificial intelligence (AI) technology offers a new approach for germplasm innovation for stress adaptation. This review systematically summarizes key advances and applications of AI in germplasm innovation for ionic stress tolerance. In terms of phenomics, multi-dimensional phenotyping systems integrate technologies including RGB imaging, spectral sensing, fluorescence imaging, thermal infrared imaging, and XRF. These are coupled with machine learning, deep learning, and multi-source data fusion to enable early, accurate, and non-invasive identification of ionic stress responses. In smart breeding, machine learning accelerates gene discovery, while pangenomics elucidates genetic diversity. AI-driven prediction of multi-gene regulatory networks and genomic selection significantly enhance breeding efficiency. However, challenges such as insufficient data standardization and sharing, limited prediction accuracy of genotype-environment interaction (G×E) models, and high technology promotion costs still constrain large-scale application. Future efforts focused on data integration, algorithm optimization, and technology popularization are expected to allow AI to play a greater role in the remediation of degraded farmland and in achieving sustainable agricultural development.

With global warming and population growth accelerating, crop breeding must advance towards greater precision, efficiency and sustainability. Riding the wave of rapid AI development, AI-driven Smart Breeding has emerged as the cutting edge of plant breeding. Leveraging technologies such as high-throughput phenotyping through machine learning and deep learning, genome-wide association studies for genomic selection, and multi-omics big data analysis, it integrates and edits genetic information while linking phenotypes with genomes. This approach promises revolutionary changes in agricultural breeding, enhancing crop productivity, improving quality, and refining traits. Looking ahead, precision agriculture and personalised breeding represent key developmental trajectories. Strategies focusing on environmental adaptability and climate change mitigation will aid in addressing ecological challenges. The cutting-edge technologies of smart Breeding, significantly enhancing breeding efficiency and precision, will shape the future direction and progress of plant breeding.

Smart agriculture, as a core direction of modern agricultural development, is driving the transformation of agricultural production from experience-driven to data-driven, and from manual operations to intelligent and automated systems. This paper systematically reviews the applications of artificial intelligence in smart agriculture and the current status, trends, and challenges of smart cultivation, constructing a “sensing-decision-execution-management” closed-loop framework to elucidate its role and value across the entire agricultural process. The sensing layer leverages multi-source sensors, imaging technologies, and the Internet of Things to achieve precise monitoring of crops and environmental conditions; the decision layer integrates data-driven algorithms and crop growth models to enable intelligent prediction and optimization of fertilization, irrigation, and pest and disease management; the execution layer employs unmanned agricultural machinery, drones, and robots to perform high-precision autonomous operations; and the management layer utilizes cloud-edge collaboration and digital twins to realize visualized and sustainable farm management. Although smart agriculture is developing rapidly, challenges remain in data assimilation and standardization, and the level of equipment intelligence and environmental adaptability still needs improvement. Moreover, with the massive acquisition and utilization of agricultural data, issues of data security and privacy protection have become increasingly prominent, posing significant challenges to the sustainable development of smart agriculture. The paper highlights that the core goal of smart cultivation is to increase yield, improve quality, reduce costs, and promote green development, while further advancing multi-source sensing integration, intelligent decision-making loops, autonomous operations, and low-carbon farming. The findings provide a reference for the theoretical framework of smart agriculture and offer technical support for the practical implementation of agricultural IoT, AI, and digital agriculture, playing a significant role in promoting efficient, intelligent, and sustainable agricultural production.

Focusing on the core issue of crop phenotyping empowered by artificial intelligence (AI), this paper systematically summarizes the typical biological characteristics of various crop phenotypes and the adapted high-throughput phenotyping acquisition techniques from six dimensions, namely morphological structure, growth and development, physiology and biochemistry, yield and quality, stress and disease resistance, and molecular genetics. Aiming at the above six dimensions, it emphatically reviews the latest research findings and practical application progress of AI technologies in the automatic identification of plant phenotypic characteristics, the accurate extraction of multi-scale phenotypic parameters, and the modeling and fusion analysis of multi-source phenotypic data. Finally, it analyzes the key challenges and deficiencies faced by current AI-driven crop phenotyping research in terms of adaptability to complex field environments, fusion of multi-source heterogeneous data, and accurate genotype-to-phenotype (G2P) modeling. Furthermore, it prospects the future development trends of AI technologies in the fields of crop phenotyping acquisition, analysis and phenomics, as well as their application prospects and research directions in intelligent breeding and agricultural intelligent decision-making.

Horticultural plants are of great significance in cultural inheritance, economic development, and ecosystem sustainability. However, the advancement of horticultural practices is hindered by challenges such as climate change, labor shortages, and inefficient resource utilization. Plant information sensing technology provides innovative solutions to these challenges. Among these technologies, plant wearable sensors have emerged as promising tools for monitoring due to their high flexibility, extensibility, spatiotemporal resolution, and biocompatibility, gradually becoming essential technologies for acquiring data on plant. Nevertheless, a systematic review of wearable sensor applications in horticultural plant research remains lacking. This paper focuses on horticultural plant information detection and comprehensively examines plant wearable sensors' research and application status in the following areas: growth monitoring, moisture content detection, stem sap flow analysis, electrical signal acquisition and chemical substance detection.Finally, we summarize the current status of plant wearable sensors and propose future development directions, aiming to provide valuable references for information detection in horticultural plants.

Non-destructive detection of fruit maturity is crucial for reducing unnecessary losses during fruit production, transportation, and storage. Traditional methods for assessing fruit maturity include empirical judgment, destructive testing, and non-destructive testing. Empirical judgment and destructive testing often suffer from low efficiency, inadequate accuracy, or cause sample damage.In recent years, non-destructive detection technologies, such as near-infrared spectroscopy, hyperspectral imaging, machine vision, fluorescence sensing, acoustic vibration, electrical properties, tactile sensing, and electronic noses, have rapidly developed. These technologies can precisely evaluate fruit maturity by measuring key parameters closely related to maturity, such as color, hardness, sugar-acid ratio, and volatile gases.This study reviews the principles of these non-destructive technologies and their current applications. It categorizes common fruit characteristics based on the core parameters that influence maturity, such as respiratory jump, color gradient, texture sensitivity, and aroma markers. It also summarizes the detection methods commonly used for different types of fruit.Additionally, the study identifies common challenges in this field, including significant environmental interference, difficulty in simultaneous multi-parameter detection, and lack of standardized systems. Finally, the study discusses potential future developments, including the integration of miniaturized multimodal sensors, the standardization of detection data, and the deeper integration of artificial intelligence with sensor systems.

Against the dual backdrop of New Agricultural Science development and educational digital transformation, South China Agricultural University addresses core challenges in botany courses at agricultural and forestry universities, such as the disconnect between learning and application, insufficient knowledge internalization, and lack of student interest. With AI as a key enabling tool, the university has constructed a four-in-one support system of ‘resources-tools-scenarios-achievements’ and implemented a closed-loop teaching reform integrating theory-practice, science-education, and industry-education. By reconstructing hierarchical teaching content centered on ‘cell-tissue-organ-group-resource utilization’, building a multi-modal AI intelligent resource matrix, and implementing a ‘three-dimension, three-stage’ process-based assessment, the reform has achieved multi-dimensional improvement of students’ core competencies and qualities, enhanced teachers’ professional capabilities, and highlighted resource radiation effects. This reform provides a replicable paradigm for the digital transformation of botany courses in agricultural and forestry universities, helping to cultivate innovative and compound talents who embody the ‘Four Agriculture’ ethos (understanding agriculture, loving agriculture, strengthening agriculture, and revitalizing agriculture) and serve modern agriculture and rural revitalization.