Heavy metal pollution in agriculture is a significant problem that endangers human health. Laser-induced breakdown spectroscopy (LIBS) is an emerging technique for material and elemental analysis, especially heavy metals, based on atomic emission spectroscopy. The LIBS technique has been widely used for rapid detection of heavy metals with its advantages of convenient operation, simultaneous detection of multi-elements, wide range of elements, and no requirement for the state and quantity of samples. However, the development of LIBS is limited by its detection sensitivity and limit of detection (LOD). Therefore, in order to improve the detection sensitivity and LOD of LIBS, it is necessary to enhance the LIBS signal to achieve the purpose of detecting heavy metal elements in agriculture. This review mainly introduces the basic instruments and principles of LIBS and summarizes the methods of enhanced LIBS signal detection of heavy metal elements in agriculture over the past 10 years. The three main approaches to enhancing LIBS are sample pretreatment, adding laser pulses, and using auxiliary devices. An enhanced LIBS signal may improve the LOD of heavy metal elements in agriculture and the sensitivity and stability of the LIBS technique. The enhanced LIBS technique will have a broader prospect in agricultural heavy metal monitoring and can provide technical support for developing heavy metal detection instruments.

Tree diseases endanger forestry and fruit tree plantations seriously worldwide in the past decades, leading to significant economic losses for the agricultural production sector. Rapid and accurate detection of tree diseases is crucial in tree protection. Despite molecular biological detection methods have prominent specificity, they are time-consuming and laborious, and are not suitable for large-scale detection of tree diseases. Spectroscopy with nondestructive, rapid, and high throughput characteristics has been applied to plant disease detection. Spectral detection systems are divided into three categories according to the spectrometer's carrying platform: portable hand-held spectrometer, airborne vehicle-mounted spectrometer, and large laboratory spectrometer. This review summarized three main spectral detection systems and their advantages and disadvantages in detecting various diseases of forestry and fruit trees: including detection of the single disease, multiple stress, and early disease using Visible/near-infrared, Raman, and hyperspectral imaging. Finally, spectroscopy detection technology applications of challenges were summarized, highlighting future trends.

Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.

Heavy metal lead (Pb) accumulation in agricultural soil has become a serious threat to human health through the food chain in recent decades. Wheat, being one of the most important crops, is a crucial source of food for humans. Pb in soil can pose a significant risk to the wheat grain, as it can be transported through the root and stem. This transportation not only increases the Pb concentration in wheat, but also threatens the wheat's overall food security. Therefore, it is imperative to investigate the effects of Pb stress on wheat seedlings. Double-pulse laser-induced breakdown spectroscopy (DP-LIBS) and surface-enhanced Raman spectroscopy (SERS) as promising enhanced spectroscopy techniques were coupled to estimate Pb stress in wheat seedlings. Wheat seedling samples under Pb stress at 6 concentration gradients (0(c.k.), 500, 1000, 2000, 3000, and 5000 mg L⁻¹) were cultivated with the same substrate and management. The SERS and DP-LIBS spectra of the root, stem and leaf were obtained by using spectrometers in an enhanced way. Principal component analysis (PCA) was employed for preliminary analysis of DP-LIBS and SERS data to investigate the clustering of samples, including substrates and the wheat seedling root and leaf, under different Pb concentration stresses. However, while PCA enabled the rough differentiation of the samples, the classification accuracy requires improvement. The pre-processed SERS, DP-LIBS, and fusion spectral data (LIBS-Raman) were analyzed using discriminant models such as least squares support vector machines (LS-SVM), multi-layer perceptron-artificial neural network (MLP-ANN), radial basis function-artificial neural network (RBF-ANN) and probabilistic neural networks (PNN). The accuracy of the model built by LIBS-Raman was significantly better than the model built based on SERS and DP-LIBS data alone. LIBS-Raman increased the accuracy of the discrimination model, which highlights the effectiveness of collaborative spectroscopic methods for heavy metal detection. This study investigated the migration of Pb in the substrate-root-leaf. At 0 concentration (c.k.), Pb was not detected in the substrate and wheat seedling root and leaf. At the same concentration, the level of Pb stress in the root was higher than that in the leaf. This might be attributed to the self-protection mechanism of wheat seedlings, which had a weak ability to transport Pb ions. This work provides theoretical support for collaborative spectral data, furnishes a reference for the development of a combined spectrometer, and offers a valuable method for monitoring heavy metal contamination in plants.

Soil is a vital resource for human survival. In particular, aluminum (Al) and iron (Fe) metal elements in soil play significant roles in stabilizing soil organic matter.

Autonomous navigation is key to improving efficiency and addressing labor shortages in the fruit industry. Semi-structured orchards, with straight tree rows, dense weeds, thick canopies, and varying light conditions, pose challenges for tree identification and navigation line extraction. Traditional 3D lidars suffer from a narrow vertical FoV, sparse point clouds, and high costs. Furthermore, most lidar-based tree-row-detection algorithms struggle to extract high-quality navigation lines in scenarios with thin trunks and dense foliage occlusion. To address these challenges, we developed a 3D perception system using a servo motor to control the rolling motion of a 2D lidar, constructing 3D point clouds with a wide vertical FoV and high resolution. In addition, a method for trunk feature point extraction and tree row line detection for autonomous navigation has been proposed, based on trunk geometric features and RANSAC. Outdoor tests demonstrate the system’s effectiveness. At speeds of 0.2 m/s and 0.5 m/s, the average distance errors are 0.023 m and 0.016 m, respectively, while the average angular errors are 0.272° and 0.146°. This low-cost solution overcomes traditional lidar-based navigation method limitations, making it promising for autonomous navigation in semi-structured orchards.