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Review

Research Advances in Underground Bamboo Shoot Detection Methods

by
Wen Li
,
Qiong Shao
,
Fan Guo
,
Fangyuan Bian
and
Huimin Yang
*
China National Bamboo Research Center, Key Laboratory of State Forestry and Grassland Administration on Bamboo Forest Ecology and Resource Utilization, Hangzhou 310012, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(5), 1116; https://doi.org/10.3390/agronomy15051116
Submission received: 25 March 2025 / Revised: 22 April 2025 / Accepted: 28 April 2025 / Published: 30 April 2025
(This article belongs to the Section Precision and Digital Agriculture)
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Abstract

:
Underground winter bamboo shoots, prized for their high nutritional value and economic significance, face harvesting challenges owing to inefficient manual methods and the lack of specialized detection technologies. This review systematically evaluates current detection approaches, including manual harvesting, microwave detection, resistivity methods, and biomimetic techniques. While manual methods remain dominant, they suffer from labor shortages, low efficiency, and high damage rates. Microwave-based technologies demonstrate high accuracy and good depths but are hindered by high costs and soil moisture interference. Resistivity methods show feasibility in controlled environments but struggle with field complexity and low resolution. Biomimetic approaches, though innovative, face limitations in odor sensitivity and real-time data processing. Key challenges include heterogeneous soil conditions, performance loss, and a lack of standardized protocols. To address these, an integrated intelligent framework is proposed: (1) three-dimensional modeling via multi-sensor fusion for subsurface mapping; (2) artificial intelligence (AI)-driven harvesting robots with adaptive excavation arms and obstacle avoidance; (3) standardized cultivation systems to optimize soil conditions; (4) convolution neural network–transformer hybrid models for visual-aided radar image analysis; and (5) aeroponic AI systems for controlled growth monitoring. These advancements aim to enhance detection accuracy, reduce labor dependency, and increase yields. Future research should prioritize edge-computing solutions, cost-effective sensor networks, and cross-disciplinary collaborations to bridge technical and practical gaps. The integration of intelligent technologies is poised to transform traditional bamboo forestry into automated, sustainable “smart forest farms”, addressing global supply demands while preserving ecological integrity.

1. Introduction

Bamboo (subfamily Bambusoideae, family Poaceae) comprises perennial, woody-stemmed grasses characterized by rapid growth, hollow segmented culms, and rhizome-dependent clonal propagation. Globally distributed across tropical, subtropical, and temperate zones, bamboo thrives predominantly in Asia, accounting for 75% of species diversity, while significant populations also exist in Africa and the Americas [1]. Ecologically, bamboo enhances carbon sequestration and soil stabilization, while economically, it serves as a renewable resource for construction, bioenergy, and food production [2,3,4]. The multifaceted value of bamboo has driven global cultivation efforts, with China emerging as one of the largest global producers of bamboo shoots [5]. In China, bamboo forests cover 7.5627 million hectares, 69.78% of which are Moso bamboo [6]. In traditional Asian cuisine, bamboo shoots, containing a variety of bioactive compounds with potential health-promoting properties, are available in various forms, including fresh, dried, and pickled bamboo [7]. Winter bamboo shoots sprout in late summer and early autumn. They are highly valued for their low-calorie, low-fat, high-fiber content, and rich nutritional profile, including vitamin A, iron, and zinc [8]. Compared to above-ground shoots, underground winter bamboo shoots have a higher nutritional value, tender texture, and can be consumed raw [9]. Moreover, aboveground shoots oxidize rapidly, losing vitamins and moisture, which compromises their taste and economic value. However, challenges in harvesting them leads to supply shortages and price surges [10].
Current underground plant detection technologies have achieved notable developments in root system mapping and soil structure analysis [11]. The methods are widely applied in agricultural and ecological studies for non-destructive root phenotyping, water content assessment, and nutrient distribution monitoring [12]. Although preliminary achievements have been made in the detection of large root systems such as trees, research and applications for winter bamboo shoots remain relatively scarce owing to challenges unique to bamboo ecosystems, including complex rhizome networks, high-heterogeneity soil textures, and interference from overlapping root signals. The detection and harvesting process remains labor-intensive, relying on the expertise of bamboo farmers. However, over 90% of harvesters are over 60 years old, and even experienced bamboo farmers can only harvest 80 kg daily [13]. Additionally, manual excavation damages 5–20% of shoots [14], further reducing their economic value. The efficient and timely harvesting of underground winter bamboo shoots offers substantial economic benefits and is urgently in need of exploration.
This study systematically reviews the current status, challenges, and developmental trends in winter bamboo shoot detection technologies. By integrating technological expertise from plant root detection research, the review evaluates the applicability of manual detection, microwave detection, resistivity methods, and biomimetic approaches, identifies existing technical bottlenecks, and proposes intelligent solutions. The significance of this study lies in providing theoretical foundations to overcome technical barriers in efficiently and precisely detecting and harvesting winter bamboo shoots. It aims to advance innovative applications of artificial intelligence (AI), multi-sensor integration, and robotic technologies in this field, thereby facilitating the transformation of the bamboo industry from traditional labor-intensive practices to intelligent and standardized operations. Through the establishment of three-dimensional (3D) modeling frameworks, development of intelligent harvesting equipment, standardization of cultivation systems, integration of visual features with intelligent imaging analysis, and aeroponic bamboo shoot cultivation with AI, this review highlights the need for intelligent positioning and automated harvesting systems to improve efficiency, increase yield, and enhance the quality of underground winter bamboo shoots, which is crucial for the development of the bamboo shoot industry.

2. Existing Technologies for Winter Bamboo Shoot Detection

The peak market period for winter bamboo shoots is from December to January, with prices reaching up to 80 yuan/kg, particularly around the Spring Festival. Their economic value is highly dependent on timely harvesting as they are collected before emerging from the ground to maintain optimal quality. Winter bamboo shoots grow at depths of 0–20 cm, and their detection and harvesting currently rely on experienced bamboo farmers. However, owing to the complex underground soil composition and a lack of specialized research and equipment, no dedicated detection tools are available. Drawing from research on plant root detection, this paper reviews manual detection methods and explores the potential of microwave detection, resistivity methods, and biomimetic approaches for winter bamboo shoot identification.

2.1. Manual Detection Method

Manual harvesting depends on farmers’ knowledge of bamboo forest environments and extensive experience. In all-round plowing harvesting, bamboo farmers plow the entire area deep into the bamboo forest to a depth of 40 cm. During the plowing process, it is crucial to avoid damaging the bamboo rhizomes, lateral roots, and buds of the rhizomes. Following the excavation, the pits should be refilled. In rhizome-tracing harvesting, bamboo farmers choose 3–4-year-old mother bamboo that can produce shoots, which typically feature dark green bamboo leaves speckled with yellow spots [15]. Farmers lightly dig around the roots using a hoe until they find yellow or brown rhizomes and then trace the rhizomes to locate shoots. In pit excavation harvesting, bamboo farmers observe whether obvious bamboo shoot tips are breaking through the soil for direct extraction [16]. Second, they examine whether the ground surface is loose or cracked. If the soil is soft when stepped on, then it indicates that winter bamboo shoots are likely to be found. They gently dig the soil surface for winter bamboo shoots.
Although effective, manual harvesting faces significant challenges. The aging labor force accounts for 19.8% of bamboo shoot harvesters [17], and younger generations are increasingly reluctant to learn the labor-intensive process owing to economic constraints and the seasonal nature of harvesting. Moreover, manual methods are inefficient, prone to errors, and can damage both the soil and shoots, rendering them unsuitable for modern sustainable agricultural practices.

2.2. Microwave Detection Method

In microwave detection, underground objects are detected by emitting electromagnetic waves and receiving the reflected signals from various media [18,19,20]. The moisture content of bamboo forest soil is 15–34%. Contrastingly, the moisture content of winter bamboo shoots can reach 85% [21]. The difference in moisture content between the two leads to the existence of a dielectric constant gradient, which changes the amplitude and phase of the reflected signal. Based on these changes, information on underground winter bamboo shoots can be obtained. Figure 1 shows a schematic of the system.
Ground-penetrating radar (GPR) is a crucial application of microwave detection technology [22]. It is used in geological exploration [23], underground pipeline detection [24], and plant root detection [25]. In practical scenarios, selecting the radar antenna frequency is crucial because it directly influences the detection depth and resolution. The GPRMAX 2D V2.0 software was used to simulate forward models of winter bamboo shoots of different shapes, sizes, and burial depths [26]. The feasibility of using GPR for winter bamboo shoot detection was confirmed, and the characteristics of each model were analyzed using echo imaging. Figure 2 depicts a model diagram and scanning echo diagram. The model is set up for winter bamboo shoots with the same burial depth but varying sizes. The models from left to right (a, b, c) represent winter bamboo shoots with lengths of 15, 10, and 5 cm, respectively, all buried at a depth of 5 cm. Multiple echoes are reflected from the top and bottom interfaces of the winter bamboo shoots. Longer winter bamboo shoots exhibit greater interval distances between the hyperbolic signatures at their upper and lower boundaries. Consequently, the longitudinal dimension of winter bamboo shoots can be estimated by measuring the time interval between the two hyperbolic arrivals.
Although the GPR is theoretically suitable for winter bamboo shoot detection, its high cost and complex operational procedures limit its widespread adoption. Consequently, low-cost user-friendly detection devices are urgently needed. A winter bamboo shoot detector based on microwave reflection has been designed [27,28]. The cost of the detector is less than one-tenth that of GPR. Unlike radar systems that rely on imaging, the device detects bamboo shoots by measuring amplitude ratios and phase differences, greatly simplifying its operation. Figure 3 shows a prototype of the winter bamboo shoot detector using microware reflection. The device uses 0.6–0.8 GHz microwaves to detect bamboo shoots at depths of 11–22 cm with an accuracy of at least 70%. Takata, et al. [29] designed a more compact, lighter, and cost-effective resonant bamboo shoot detector. Their detector utilizes changes in the electrostatic capacitance between electrodes to detect bamboo shoots at depths up to 5 cm underground. Figure 4 shows the device components.
Although microwave technology effectively detects underground winter bamboo shoots without damaging the bamboo forest ecosystems, its practical implementation is influenced by multiple factors. First, high soil moisture content accelerates signal attenuation, significantly affecting the propagation and reflection of microwave signals. As the soil depth increases, the moisture content and dielectric constant increase. This reduces the difference between the dielectric constants of the soil and bamboo shoots, thereby impairing the detection accuracy. The performance of a winter bamboo shoot detector based on microwave technology was studied in soil with a water content of 1.5–44%. The detector is considered viable when the soil moisture content is less than 20%. Figure 5 shows the complete experimental system of the bamboo shoot detector. Second, the soil structure influences the detection effect. The clayey soil is dense, whereas the sandy soil is loose. Regional disparities in soil media make the selection of an appropriate detection frequency crucial. A single frequency cannot achieve universally efficient detection in all scenarios. Notwithstanding these challenges, microwave technology is an effective noninvasive detection method with considerable potential for application [30].

2.3. Resistivity Method

The resistivity method detects underground conductivity distribution by placing electrodes on the ground surface, injecting an electric current into the ground via these electrodes and measuring the potential difference between adjacent electrodes [32,33]. The resistivity method encompasses several techniques, such as electric impedance tomography (EIT) and parallel electric methods.
The electrical impedance imaging method applies an excitation current on the surface of the measured object and measures the boundary voltage to obtain the conductivity distribution inside the object, which is then reconstructed in combination with the reconstruction algorithm [34,35]. It is often used in biomedical imaging studies of the brain [36,37] and lungs [38,39]. However, owing to its low resolution, it is unsuitable for refined medical work; thus, it is not widely used. Recently, electrical impedance imaging has been applied for plant root detection. The location of the maple roots in the soil was studied using EIT [40]. By combining this with the Newton’s one-step error reconstructor algorithm, it was found that the positioning effect was best under a soil water content of 22% [41]. Figure 6 presents a comparison of the algorithms. This indicates that EIT is feasible to detect plant rhizomes. However, the study only targeted maple rhizomes in a single physiological state. To further study plant rhizomes in different physiological states, EIT was applied to rapeseed rhizomes to effectively detect their position and size [42]. The results showed that the detection effect of dry root stems was not as good as that of fresh roots. The conductivity of the rotten root stems resulting from water loss was not significantly different from that of the surrounding soil; therefore, it could not be effectively detected. Fresh winter bamboo shoots have a high underground water content and a regular geometric shape; therefore, it is theoretically feasible to apply EIT to detect winter bamboo shoots [43]. However, most root detection experiments based on EIT technology are complex and expensive, primarily use imported equipment, and are difficult to apply to bamboo forests. Therefore, simpler equipment needs to be developed. EIT was used to simulate winter bamboo shoot detection [44], and the author proposed a simplified discrimination method based on the measurement voltage characteristics. The method reduces the requirements of experimental hardware and software and can be used to detect the position of winter bamboo shoots. However, these studies were not true underground detections because the roots were placed in the soil for localization. The applicability of this technology to underground plants such as winter bamboo shoots requires further verification.
The parallel electrical method is based on the conductivity difference between the target object and surrounding soil [45]. By laying multiple measurement lines with each line containing multiple electrodes, the potential difference between adjacent electrodes is measured to obtain the distribution information of the underground conductivity. This technique is often applied in large-scale scenarios, such as geological exploration and engineering monitoring. For example, the parallel electric method was used to detect the spatial distribution of Bischofia polycarpa roots [46,47]. They arranged an observation system around Bischofia polycarpa for data collection and obtained a 3D resistivity image of tree roots through the joint inversion of multiple measurement lines. The line arrangement for the field experiments is shown in Figure 7. However, the field measurement line has a large layout extent (7.5 m wide and 9.5 m long). Moreover, the data collection process requires a special parallel electrometer and professional data analysis software, which makes the process more complicated. Therefore, the approach is unsuitable for the small-range detection of winter bamboo shoots, which require precise positioning. To overcome these limitations, experiments on winter bamboo shoots were conducted using parallel electrical methods with cross-shaped and circular electrode configurations with a parallel electrical method instrument [48]. The results demonstrated that the cross-shaped system performed better for shoots buried at an approximate depth of 20 cm. However, the method can make false detections. Figure 8 illustrates a schematic of the cross-shaped circular observation system.
In summary, resistivity methods are feasible to detect winter bamboo shoots underground. However, they have low accuracy, require expensive high-precision equipment, and have complex setups. Data processing and inversion imaging are time-consuming and inefficient, making it difficult to fulfill the high-throughput requirements for winter bamboo shoot detection. Therefore, enhancements in equipment integration and efficiency are required to render the method practically feasible. Currently, it is not directly applicable to detect winter bamboo shoots.

2.4. Biomimetic Method

Biomimetic-based detection addresses technical challenges by emulating the structures, functions, and behaviors of living organisms and translating biological features into engineering design tenets [49]. Intelligent biomimetic robots that integrate biomimetics and AI have been extensively applied in logistics, manufacturing, and scientific research. For underground winter bamboo shoot detection, this approach primarily depends on biomimetic olfaction. However, associated research remains rather limited, presenting substantial room for further development.
Electronic nose and tongue technologies are the major applications of biomimetic olfaction [50,51], commonly used in meat and fruit detection [52,53]. A PEN 3.5 portable electronic nose was used to identify bamboo shoot types [54,55]. They demonstrated the odor differences among different bamboo shoot varieties and demonstrated the potential of electronic nose technology for bamboo shoot identification. To overcome the limitations of single-sensor information, a combined convolutional neural network (CNN)–transformer model integrating electronic tongue and nose data for rapid detection was proposed [56]. The capacity of the electronic nose technology was validated for plant odor recognition. However, related studies have predominantly focused on aboveground plants. Contrastingly, winter bamboo shoots are located underground. Thus, investigating whether odors can penetrate the soil for detection is essential. An odor detection system based on electronic nose technology was developed to effectively detect toxic gases in the soil and surrounding air [57]. It was proved that electronic noses can recognize volatile odors underground. However, the odor of winter bamboo shoots becomes extremely weak after they penetrate the soil. Moreover, the presence of other plant and animal odors in bamboo forests can interfere with detection, making it challenging to apply electronic nose technology for underground winter bamboo shoot detection. Another approach is to train animals for underground detection because electronic noses are inspired by animal olfaction. In Tuscany, Italy, trained dogs are used to search for truffles underground with a highly sensitive sense of smell. Owing to their well-developed olfactory systems, wild boars are proficient in finding winter bamboo shoots. Therefore, training boars and studying their foraging methods may be viable solutions.
Although electronic nose technology has been supported by existing research in plant odor recognition, few studies have examined winter bamboo shoots. Most related studies have focused on non-underground objects. Additionally, the data processing of the current electronic nose technology mostly relies on computers. However, the detection of underground winter bamboo shoots requires equipment with high real-time processing capabilities. Therefore, despite the potential of electronic nose technology in odor recognition, soil-induced barriers, environmental interference, and real-time performance requirements need to be overcome when applying the technology for underground winter bamboo shoot detection. Consequently, electronic nose technology cannot be readily applied in practical scenarios.

2.5. Summary of Methods

Currently, the harvesting of underground winter bamboo shoots depends primarily on manual methods. However, these methods are inefficient and likely to damage the soil, shoots, and rhizomes. Given the limited number of experienced bamboo farmers, the future of the winter bamboo shoot industry is likely to shift toward intelligent positioning and automated harvesting. Microwave detection, resistivity methods, and biomimetic methods are theoretically feasible but face challenges, such as the need for technological breakthroughs, high equipment costs, and complex data processing (Table 1). As a result of the test analysis, the performance comparisons presented in Table 2 further illustrate the advantages and limitations of each intelligence approach. Despite these difficulties, research and development of winter bamboo shoot detection equipment is ongoing. Recently, a winter bamboo shoot detection and excavation vehicle was developed to improve harvesting efficiency and reduce soil damage [58]. Additionally, an all-terrain detection device based on electromagnetic reflection was designed to address the detection difficulties in different terrains [59]. Although some progress has been made, intelligent equipment for harvesting winter bamboo shoots requires further improvement and optimization.

3. Existing Problems and Development Prospects

3.1. Existing Problems

Currently, practical winter bamboo shoot detection devices are lacking, and research on related technologies and harvesting robots faces several challenges. The first challenge is the complex bamboo forest environment. The underground soil contains diverse materials, including gravel, tree roots, and bamboo rhizomes, which can generate signals similar to those of bamboo shoots, leading to misjudgments. Additionally, winter bamboo shoot growth is influenced by factors such as soil type, moisture, and nutrients. Variations in altitude, climate, topography, and human activities (e.g., industrial pollution) result in significant differences in soil properties [60]. Above-ground vegetation, including weeds and fallen leaves, further complicates detection. The second challenge is the high cost and operational complexity of detection equipment. While GPR has demonstrated potential, its high cost often outweighs its benefits [61]. Moreover, operating such equipment is complex and requires professional training, and its bulkiness further limits its application in complex terrains, reducing detection efficiency. The third challenge is the complexity of developing specialized harvesting equipment [62]. Navigating and positioning harvesting vehicles in complex terrains, especially where Global Positioning System signals are unavailable, is difficult. Additionally, the harvesting process should be precise to avoid damaging bamboo shoots and mother bamboo, necessitating advanced robotic arm designs and control methods. Detection equipment should also be capable of differentiating winter bamboo shoots from other underground objects, requiring highly sensitive and accurate sensors and algorithms. The final challenge is low research attention. Few researchers focus on winter bamboo shoot detection and harvesting, and the industry lacks specialized talent, hindering technological advancement. While some research is emerging in bamboo-rich provinces, such as Zhejiang, overall attention remains low, slowing progress in this field.
Existing bamboo shoot detection methods offer foundational insights but lack critical comparative metrics and real-world validation. Manual detection achieves not low accuracy but is labor-intensive and prone to human error, while microwave radar reaches much higher accuracy for shallow rhizomes but struggles in saline soils due to signal attenuation. Resistivity sensors face interference from root systems in organic soils, and biomimetic technologies require extensive training data and struggle with cross-species generalization. Field validations reveal significant gaps: the accuracy of laboratory-optimized microwave systems is dropped much in heterogeneous terrains, and biomimetic technologies overfit synthetic data, underperforming in real forests. Challenges like canopy occlusion and soil variability further limit practicality. Future work must prioritize edge-optimized AI and hybrid sensor fusion to address these translational bottlenecks, alongside explainable AI frameworks to enhance farmer trust and ecological compliance.

3.2. Economic Feasibility of Intelligent Detector

The economic viability of intelligent bamboo shoot detector hinges on three pillars: return on investment (ROI), policy incentives, and accessibility for smallholders. From an ROI perspective, these systems reduce labor costs by automating shoot detection and health monitoring, resulting in 40–60% labor savings and 800–1200 CNY/ha annual savings, coupled with yield improvements of 20–30% via optimized harvesting. Initial deployment costs (≈15,000–30,000 CNY/ha) are typically offset within 3–5 years in high-yield regions. Policy supports further enhance feasibility: governments offer subsidies (e.g., 30% Internet of Things (IoT) equipment rebates) and low-interest loans, while insurance programs mitigate revenue risks. For smallholders, cooperative procurement models (e.g., 40% cost-sharing for sensor networks) and shared-use platforms (e.g., mobile detection units) lower entry barriers. However, challenges persist, including limited rural internet coverage, technical training gaps, and maintenance costs. Solutions such as leasing models (200–500 CNY/month/ha) and vernacular AI interfaces address these barriers to a certain extent. Strategic investments in rural digital infrastructure and cross-sector partnerships will be critical to democratize access and ensure sustainable adoption.

3.3. Development Prospects

Intelligent innovation has emerged as a hallmark of modern forestry development [63], and it plays a pivotal role in promoting economic prosperity, enhancing ecosystem functions, and safeguarding the environment. With breakthroughs in new-generation AI technology, smart forestry has emerged as a new developmental direction. Although AI has been extensively applied in agriculture for activities such as picking, weeding, and fertilization [64], its application in forestry, particularly for the detection of underground winter bamboo shoots, remains inadequately explored. The introduction of AI technology to enhance the harvesting efficiency of winter bamboo shoots is crucial. In the future, AI can be integrated with various sensors, GPRs, automatic harvesting robots, and BeiDou positioning technology to collect multidimensional information on winter bamboo shoots. By incorporating the practical experience of bamboo farmers, a 3D model system can be constructed. Through machine learning, the system can continuously optimize itself based on data, establish rule-based models and decision-making mechanisms, and improve the accuracy and efficiency of winter bamboo shoot detection. Moreover, intelligent winter bamboo shoot-harvesting robots can be developed to achieve precise and efficient excavation, thereby further enhancing the harvesting efficiency and resource utilization rate. When combined with standardized bamboo planting, it can ensure the stability of the bamboo forest environment and healthy growth of winter bamboo shoots.
The efficacy of bamboo shoot detection systems is profoundly influenced by terrain and climatic variations, driving innovations in adaptive technologies and multi-sensor integration. In tropical lowlands, high humidity and soil acidification reduce microwave radar penetration depth, necessitating hybrid systems that combine shortwave infrared spectroscopy (SWIR) for moisture-compensated biomass mapping. Mountainous regions demand cold-resistant probes and solar–wind hybrid power solutions to counteract low temperatures and UV radiation. Flood-prone lowlands face salt crust interference, prompting salt-penetrating SWIR bands and AI-driven waterlogging alerts. In extreme climates, IoT vibration sensors can be employed to monitor rhizome water stress thresholds, while hurricane-affected Philippine groves require light detection and ranging-based 3D canopy reconstruction post-storm. These regional adaptations highlight a shift from single-method solutions to multimodal architectures that integrate climate-resilient hardware and adaptive algorithms.

3.3.1. 3D Model Construction

Data on the bamboo forest environment, including soil moisture, light conditions, aboveground obstacles, and bamboo growth characteristics, can be collected using various sensors and IoT technology. GPR can be employed to analyze the reflection wave data of underground winter bamboo shoots, thereby determining their distribution. Combined with the BeiDou technology, precise positioning can be achieved for automatic harvesting robots. A 3D model of the winter bamboo shoot environment that vividly depicts the location, depth, and growth environment of underground shoots can be constructed by integrating the practical experience of bamboo farmers [65]. The system can process and analyze the data via cloud servers to optimize decision-making processes and enhance detection accuracy.

3.3.2. Winter Bamboo Shoot Harvesting Robots

Harvesting robots have several advantages [66,67]. First, they can operate on hazardous terrains, such as steep areas or areas infested with snakes, and are well-suited for repetitive tasks, such as fertilization and harvesting, thus enabling continuous operation throughout the day. Harvesting robots should be equipped with high-precision BeiDou positioning systems, GPR, and various sensors to receive real-time data from the 3D model system platform. This will enable accurate positioning and excavation while avoiding damage to winter bamboo shoots and bamboo forest soil caused by inadvertent digging. The robot should be capable of automatically excavating, collecting, and transporting the harvest to reduce manual intervention, enhance work efficiency, and demonstrate good environmental adaptability. These enhancements ensure stable and efficient completion of winter bamboo shoot excavation tasks under various terrain and climate conditions.

3.3.3. Planting Standardization

Clear requirements should be established for land selection, preparation, fertilization, planting, and management. Land with a deep soil layer, loose and fertile texture, and good drainage is required for large-scale planting [68]. Weeds and stones should be removed during land preparation to ensure soil purity and aeration. Moreover, soil microorganisms can enhance soil fertility and carbon content by decomposing organic matter and facilitating the nutrient cycle [69,70]. Therefore, appropriate amounts of organic fertilizer, compound fertilizer, or chicken manure can be added to the soil. During planting, a healthy bamboo rhizome should be planted according to a fixed plant-row spacing. During the management phase, timely irrigation, weeding, soil loosening, and pest control should be performed to ensure the optimal growth of winter bamboo shoots.

3.3.4. Integration of Visual Features with Intelligent Imaging Analysis

Integrating visual features with neural network models, such as CNNs and intelligent imaging systems, offers a promising solution to locate bamboo rhizome zones and map bamboo shoot search hotspots. The approach leverages deep learning architectures, such as YOLOv5 and transformer-based models, to automate defect detection in bamboo strips, adapting principles from agricultural AI for rhizome path prediction [71]. U-Net architectures with ResNet-50 backbones are deployed for radar image segmentation. For example, a modified U-Net trained on multispectral radar data can detect subtle soil anomalies (e.g., moisture gradients) linked to rhizome activity, achieving higher precision in simulated bamboo forest trials [72]. By analyzing multispectral imagery and color anomalies, the system identifies potential shoot clusters via spatial feature sampling and query recycling mechanisms. Key advantages include cost efficiency (reducing reliance on costly ground-penetrating radar), non-invasive monitoring, and scalability for large-area surveys. Challenges such as canopy occlusion and model generalization across bamboo species (e.g., Phyllostachys vs. clumping bamboo) can be mitigated via transfer learning with cross-species datasets and attention mechanisms to focus on critical regions. Future work should prioritize edge computing for real-time processing and multimodal fusion to enhance precision. By embedding domain-specific AI architectures (CNNs, decision trees), addressing data limitations (synthetic augmentation, transfer learning), and formalizing traditional knowledge (rule engines, reinforcement learning), this framework strengthens the technical rigor and practical relevance of the present study. Future work should explore explainable AI to visualize model decisions and foster farmer trust.

3.3.5. Aeroponic Bamboo Shoot Cultivation with AI

Cultivating bamboo shoots in controlled environments, such as aeroponic systems, revolutionizes traditional farming practices and creates unique opportunities to optimize and integrate bamboo shoot detection technologies. The high controllability of aeroponic systems—including precise regulation of temperature, humidity, light cycles, and nutrient solution formulations—enables the generation of standardized growth parameter datasets [73]. These datasets provide high-quality training samples for deep learning-based phenotyping detection models. For instance, multispectral imaging sensors integrated into aeroponic facilities can capture real-time 3D morphological dynamics of bamboo shoot root systems. When combined with CNN algorithms, the data facilitate the establishment of growth rate prediction models to identify optimal harvesting windows. Moreover, the reduced morphological variability of bamboo shoots in controlled environments minimizes interference from complex natural backgrounds on image recognition models, greatly improving detection accuracy. To address the unique nutrient management requirements of aeroponics, decision tree-based rule engines can correlate root image features with nutrient absorption efficiency, enabling early warnings of growth anomalies. Future advancements should focus on developing cross-modal data fusion frameworks to synergistically analyze aeroponic environmental sensor data with visual detection metrics. This integration can support the creation of closed-loop feedback systems, transitioning detection technologies from passive recognition to proactive regulation. Such innovations can provide comprehensive, full-cycle intelligent decision-making support for intelligent bamboo shoot detection and industrial-scale production.

4. Conclusions

This review presented the current state of underground winter bamboo shoot detection and harvesting, highlighted advancements in intelligent exploration methods, and evaluated their advantages and limitations. Moreover, it proposed solutions incorporating AI and automation technologies to enhance yield and quality. Specifically, five key strategies are recommended: (1) leveraging sensor and IoT technology with GPR to develop 3D subsurface models; (2) developing robotic harvesters equipped with BeiDou positioning and advanced sensors to minimize soil damage and improve efficiency; (3) promoting standardized planting, optimizing soil management, fertilization, irrigation, and pest control; (4) integrating visual features with intelligent imaging analysis to improve the detection accuracy; and (5) cultivating bamboo shoots by combining aeroponic technologies and AI creates unique opportunities to optimize and integrate bamboo shoot detection technologies. With advancements in AI and automation, the winter bamboo shoot industry is expected to transition toward modernization and intelligent forestry. “Unmanned forest farms” and “smart forest farms” may become the dominant modes of future forestry production.

Author Contributions

Conceptualization, F.G. and F.B.; funding acquisition, H.Y.; investigation, F.G.; writing—original draft, W.L.; writing—review and editing, Q.S., F.B. and H.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Fundamental Research Funds for the Non-profit Research Institution of Zhejiang Province (Research on key technologies for innovative development and high-value utilization of eco-friendly bamboo shoot and timber materials) and Zhejiang Provincial Natural Science Foundation (LTGN23C200001).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We are sincerely grateful to the anonymous reviewers and editors for their valuable suggestions to improve the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Schematic of microwave-based bamboo shoot detection. The horizontal line marks the ground, above it is air, and below the horizontal line is soil.
Figure 1. Schematic of microwave-based bamboo shoot detection. The horizontal line marks the ground, above it is air, and below the horizontal line is soil.
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Figure 2. Simulation model and simulated radar scan echo images of winter bamboo shoots. Models a, b, and c are the echo diagrams of winter bamboo shoots with a length of 15, 10, and 5 cm, respectively, and the burial depth is 5 cm. B-scan echoes of models a, b, and c estimate the longitudinal size of the winter bamboo shoot by the time interval between the two hyperbolic curves [26].
Figure 2. Simulation model and simulated radar scan echo images of winter bamboo shoots. Models a, b, and c are the echo diagrams of winter bamboo shoots with a length of 15, 10, and 5 cm, respectively, and the burial depth is 5 cm. B-scan echoes of models a, b, and c estimate the longitudinal size of the winter bamboo shoot by the time interval between the two hyperbolic curves [26].
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Figure 3. Photograph of the winter bamboo shoot detector device using microware reflection [28].
Figure 3. Photograph of the winter bamboo shoot detector device using microware reflection [28].
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Figure 4. Components of the bamboo shoot detector designed by Takata [29].
Figure 4. Components of the bamboo shoot detector designed by Takata [29].
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Figure 5. Experimental system of the bamboo shoot detector in soil with different water contents [31].
Figure 5. Experimental system of the bamboo shoot detector in soil with different water contents [31].
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Figure 6. Comparison between Newton’s one-step error reconstructor algorithm and primal dual-interior point method algorithm. The left, middle, and right images show the soil–root system, reconstructed image, and binarization map, respectively. ∆p indicates the change in the resistivity distribution of the soil–root system with respect to the soil, the dark red region (Δp ≥ 0.02) corresponds to the root, the light colored region (−0.01 ≤ ∆p < 0.02) corresponds to the soil, and the dark blue region (∆p < −0.01) corresponds to the soil, which is caused by boundary measurement errors [41].
Figure 6. Comparison between Newton’s one-step error reconstructor algorithm and primal dual-interior point method algorithm. The left, middle, and right images show the soil–root system, reconstructed image, and binarization map, respectively. ∆p indicates the change in the resistivity distribution of the soil–root system with respect to the soil, the dark red region (Δp ≥ 0.02) corresponds to the root, the light colored region (−0.01 ≤ ∆p < 0.02) corresponds to the soil, and the dark blue region (∆p < −0.01) corresponds to the soil, which is caused by boundary measurement errors [41].
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Figure 7. Photo of the experiment using parallel electrical method [47].
Figure 7. Photo of the experiment using parallel electrical method [47].
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Figure 8. Observation system layout diagram. (a) A total of 64 micro electrodes in the crisscross-shaped observation system, evenly distributed on the four measurement lines that constitute the crisscross shape, and an X–Y–Z three-dimensional coordinate system. (b) A total of 64 micro electrodes, with 16 electrodes arranged on the inner circular measuring line with radius R1 and 48 electrodes arranged on the outer circular measuring line with radius R2 [48].
Figure 8. Observation system layout diagram. (a) A total of 64 micro electrodes in the crisscross-shaped observation system, evenly distributed on the four measurement lines that constitute the crisscross shape, and an X–Y–Z three-dimensional coordinate system. (b) A total of 64 micro electrodes, with 16 electrodes arranged on the inner circular measuring line with radius R1 and 48 electrodes arranged on the outer circular measuring line with radius R2 [48].
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Table 1. Winter bamboo shoot detection methods and their advantages and disadvantages.
Table 1. Winter bamboo shoot detection methods and their advantages and disadvantages.
MethodDetection ApproachAdvantagesDisadvantagesDevice Status
Manual MethodsAll-Round Plowing HarvestingDeep-plow the bamboo forest by following the bamboo rhizomes.Simple operation; suitable for small-scale bamboo forests; integrates soil loosening and fertilization.Labor-intensive and time-consuming. Moreover, it is prone to damaging winter bamboo shoots and disrupting the soil structure.-
Rhizome-Tracing HarvestingLocate bamboo rhizomes via foliar density and color analysis, then guide excavation by locating rhizomes.Beneficial for preserving winter shoots and soil structure.Low efficiency.-
Pit-Excavation HarvestingObserve surface soil loosening or cracking to determine target burial position.Simple operation; minimal soil disturbance.Requires considerable experience. Easy to miss the bamboo shoots that are deeply buried or hidden.-
Intelligent MethodsMicrowave DetectionUnderground bamboo shoots are non-invasively detected by analyzing the reflected microwave signals.Fast and non-contact.Detection accuracy of underground bamboo shoots is affected by underground media and soil moisture content.Commercially available
ResistivityMeasure underground resistivity changes for target determination.Certain effects.Equipment is costly, destructive, and inefficient.Prototype
BiomimeticDetect winter bamboo shoots by their smell.Broad development prospects. Designing the equipment is challenging.Concept
Table 2. Performance comparison among three intelligent methods.
Table 2. Performance comparison among three intelligent methods.
MethodDetection AccuracyDepth PenetrationSoil Adaptability Cost-Effectiveness
Intelligent MethodsMicrowave Detection70–80%0–25 cmModerateHigh
Resistivity>60%0–35 cmModerateModerate
BiomimeticN/A0–5 cmPoorHigh
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Li, W.; Shao, Q.; Guo, F.; Bian, F.; Yang, H. Research Advances in Underground Bamboo Shoot Detection Methods. Agronomy 2025, 15, 1116. https://doi.org/10.3390/agronomy15051116

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Li W, Shao Q, Guo F, Bian F, Yang H. Research Advances in Underground Bamboo Shoot Detection Methods. Agronomy. 2025; 15(5):1116. https://doi.org/10.3390/agronomy15051116

Chicago/Turabian Style

Li, Wen, Qiong Shao, Fan Guo, Fangyuan Bian, and Huimin Yang. 2025. "Research Advances in Underground Bamboo Shoot Detection Methods" Agronomy 15, no. 5: 1116. https://doi.org/10.3390/agronomy15051116

APA Style

Li, W., Shao, Q., Guo, F., Bian, F., & Yang, H. (2025). Research Advances in Underground Bamboo Shoot Detection Methods. Agronomy, 15(5), 1116. https://doi.org/10.3390/agronomy15051116

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