Jinzhu Lu

Hyperspectral imaging (HSI), which integrates spatial and continuous spectral information, has shown considerable potential for nondestructive citrus quality assessment. However, HSI data inherently suffer from high dimensionality, strong inter-band correlation, and substantial redundancy. Consequently, extracting reliable quality evaluation results from such complex spectral information relies heavily on effective algorithm design. This review summarizes recent advances in data-analysis algorithms for HSI-based nondestructive citrus quality assessment, focusing on three major tasks: maturity assessment; disease, pest damage, and bruising detection; and internal physicochemical and nutritional attribute prediction. Representative approaches, including chemometrics, machine learning, deep learning, transfer learning, and multimodal fusion, are reviewed and compared from the perspective of task-specific challenges. Existing studies indicate that maturity assessment has developed relatively mature algorithmic pathways, whereas disease and bruise detection require effective enhancement of weak abnormal signals and robust suppression of environmental and structural interference. Internal physicochemical and nutritional attribute prediction, especially for titratable acidity (TA) and vitamin C (VC), remains challenging because of weak spectral responses, complex nonlinear relationships, and limited cross-scenario stability. Future research should emphasize standardized datasets, informative wavelength selection, lightweight model design, interpretable learning, and multi-task collaborative modeling. This review provides a systematic reference for algorithm design and system optimization in HSI-based citrus quality assessment.

Automatic grading in factory production is a key component for improving production efficiency and ensuring product quality. As one of the most widely consumed edible mushrooms in the world, the cap diameter of Agaricus bisporus is a key factor affecting the market selling price. In addition to the high labor cost, manual grading is subjective and prone to human assessment errors. Therefore, this study proposes a video‐based fast real‐time machine vision evaluation system for size grading of Agaricus bisporus mushrooms. The designed grading system consists of a conveyor belt to achieve transportation of Agaricus bisporus and an overhead color camera to perform size quality assessment. Considering deployment on edge devices, we have optimized the YOLOv5 model for lightweight performance by adopting ShuffleNetV2 as the backbone network, significantly improving the model's lightweight performance and processing speed. At the same time, we investigated the balance between transport speed and recognition accuracy to ensure that the system maintains a high recognition rate while producing efficiently. The results show that the size of the improved model is reduced by 86.1%, FPS is increased by 9.8%, and mAP is 97.1%. The experimental field results show that the grading can still be performed at a high speed with a transmission speed of 130.17 mm/s. The grading speed can reach 1066.67 mushrooms/min with an accuracy of 97.87%, and it also has strong adaptability and stability in low resolution and low light conditions. The video‐based model can detect Agaricus bisporus mushrooms that are always in motion, which is more suitable for practical detection than a single image‐based detection model.

Rapid and accurate assessment of the citrus ripening stage in the field is important for determining harvest timing and improving industrial economic efficiency; however, the lack of effective nondestructive detection methods in the current orchard leads to flaws in ripening stage assessment, which affects harvesting decisions. To solve this problem, this study utilized hyperspectral technology to collect data from 22 fruit trees in an orchard (in the range of 400–1000 nm) and explored the effectiveness of five regions of interest selection methods (x-axis, y-axis, four-quadrant, threshold segmentation, and raw) for the delineation of the citrus ripening stage. The data quality was enhanced via wavelet transform (WT)-multiple scattering correction (MSC) preprocessing, and the effective wavelengths were extracted via the successive projections algorithm (SPA). On the basis of these wavelengths, backpropagation neural network (BP) and convolutional neural network (CNN) models were built for maturity prediction. The results show that the x-axis region of interest selection method outperforms the other methods, and the SPA-BP model based on this method performs best. An accuracy of 99.19% for the correction set and 100% for the prediction set was achieved when only 0.03% of the wavelength was used. This groundbreaking study highlights the significant potential of hyperspectral technology for in situ assessment of citrus ripening stages. Furthermore, it offers crucial technical support and serves as a valuable reference for the advancement of precision agriculture.

The low degree of intelligence and standardization of tea bud picking, as well as laborious and time-consuming manual harvesting, bring significant challenges to the sustainable development of the high-quality tea industry. There is an urgent need to investigate the critical technologies of intelligent picking robots for tea. The complexity of the model requires high hardware computing resources, which limits the deployment of the tea bud detection model in tea-picking robots. Therefore, in this study, we propose the YOLOV5M-SBSD tea bud lightweight detection model to address the above issues. The Fuding white tea bud image dataset was established by collecting Fuding white tea images; then the lightweight network ShuffleNetV2 was used to replace the YOLOV5 backbone network; the up-sampling algorithm of YOLOV5 was optimized by using CARAFE modular structure, which increases the sensory field of the network while maintaining the lightweight; then BiFPN was used to achieve more efficient multi-scale feature fusion; and the introduction of the parameter-free attention SimAm to enhance the feature extraction ability of the model while not adding extra computation. The improved model was denoted as YOLOV5M-SBSD and compared and analyzed with other mainstream target detection models. Then, the YOLOV5M-SBSD recognition model is experimented on with the tea bud dataset, and the tea buds are recognized using YOLOV5M-SBSD. The experimental results show that the recognition accuracy of tea buds is 88.7%, the recall rate is 86.9%, and the average accuracy is 93.1%, which is 0.5% higher than the original YOLOV5M algorithm’s accuracy, the average accuracy is 0.2% higher, the Size is reduced by 82.89%, and the Params, and GFlops are reduced by 83.7% and 85.6%, respectively. The improved algorithm has higher detection accuracy while reducing the amount of computation and parameters. Also, it reduces the dependence on hardware, provides a reference for deploying the tea bud target detection model in the natural environment of the tea garden, and has specific theoretical and practical significance for the identification and localization of the intelligent picking robot of tea buds.

The potato industry is crucial to the development of the agricultural economy, and mechanized seeding is an effective aid to its modernization. Due to the metering devices having different sizes and structures, and even control, operation of sudden instability and other characteristics, and the complexity and variability of the farmland environment and other reasons, the mechanical sowing process may be affected by internal and external random and unpredictable factors, thus causing seed potato leakage, reseeding, and other phenomena and resulting in a decline in the quality of potato sowing brought about by the cascade effect: the quality and yield decline, precision sowing has become the modern potato production in the problem of modern potato production needs to be solved. This paper reviews the potato planter around the seeding quality of single seeding, plant spacing, depth and other directions of device optimization and digital sensing technology innovation involved, which single seeding direction from a variety of metering devices and the use of other sowing methods of potato planter detection, replanting, metering and other parts of the entry, and the introduction of the other field crops crop related research to provide a more comprehensive perspective. The advantages and disadvantages of the related research were compared and the existing problems were analyzed, and the future development direction of potato precision sowing technology was prospected.

Spectral technology is a scientific method used to study and analyze substances. In recent years, the role of spectral technology in the non-destructive testing (NDT) of fruits has become increasingly important, and it is expected that its application in the NDT of fruits will be promoted in the coming years. However, there are still challenges in terms of dataset collection methods. This article aims to enhance the effectiveness of spectral technology in NDT of citrus and other fruits and to apply this technology in orchard environments. Firstly, the principles of spectral imaging systems and chemometric methods in spectral analysis are summarized. In addition, while collecting fruit samples, selecting an experimental environment is crucial for the study of maturity classification and pest detection. Subsequently, this article elaborates on the methods for selecting regions of interest (ROIs) for fruits in this field, considering both quantitative and qualitative perspectives. Finally, the impact of sample size and feature size selection on the experimental process is discussed, and the advantages and limitations of the current research are analyzed. Therefore, future research should focus on addressing the challenges of spectroscopy techniques in the non-destructive inspection of citrus and other fruits to improve the accuracy and stability of the inspection process. At the same time, achieving the collection of spectral data of citrus samples in orchard environments, efficiently selecting regions of interest, scientifically selecting sample and feature quantities, and optimizing the entire dataset collection process are critical future research directions. Such efforts will help to improve the application efficiency of spectral technology in the fruit industry and provide broad opportunities for further research.

The construction of standardized tea gardens is the main trend in the development of modern agriculture worldwide. As one of the most important economic crops, tea has increasingly stringent requirements placed on its planting capacity and quality. The application of machine vision technology has led to the gradual development of tea production moving towards intelligence and informatization. In recent years, research on tea production based on machine vision technology has received widespread attention, as it can greatly improve production efficiency and reduce labor costs. This article reviews the current application status of machine vision technology in tea pest monitoring, intelligent harvesting, quality evaluation, and classification, and analyzes and discusses specific challenges around machine vision technology in tea production; for example, this technology lacks a standard database and weather interference, training errors in the model, and differences in the hardware computing speed can become a challenge. Based on the current research and application of machine vision technology in various fields, this article looks ahead to the development prospects and future trends of machine vision technology in tea production applications, such as future research to further integrate multiple types of sensors, improvements in the quality and usability of datasets, optimized model algorithms for existing problems, dissemination of research results, and intelligent management of tea production through machine vision technology.

The construction of standardized citrus orchards is the main trend in the future development of modern agriculture worldwide. As the most widely used and mature technology in the agricultural field, machine vision has greatly promoted the industrial development model of the citrus industry. This paper summarizes the application of machine vision technology including citrus pest and disease detection, harvesting identification and localization, and fruit grading. We compare the advantages and disadvantages of relevant research, and analyze the existing problems and prospects for future research. Due to the complex and changeable in-field environment, robots may experience unpredictable interference in the recognition process, which leads to errors in target fruit localization. The lack of datasets also affects the accuracy and stability of the algorithm. While expanding the dataset, it is necessary to conduct further research on the algorithm. In addition, the existing research focuses on indoor monitoring methods, which are not practical for the changeable outdoors environment. Therefore, realizing the diversity of sample datasets, designing agricultural robots suitable for complex environments, developing high-quality image processing hardware and intelligent parallel algorithms, and increasing dynamic monitoring methods are the future research directions. Although machine vision has certain limitations, it is still a technology with strong potential for development.

Lettuce is one of the most widely planted leafy vegetables in plant factories. The lack of trace elements in nutrient solutions has caused huge losses to the lettuce industry. Non-obvious symptoms of trace element deficiency, the inconsistent size of the characteristic areas, and the difficulty of extraction in different growth stages are three key problems affecting lettuce deficiency symptom identification. In this study, a batch of cream lettuce (lactuca sativa) was planted in the plant factory, and its nutrient elements were artificially controlled. We collected images of the lettuce at different growth stages, including all nutrient elements and three nutrient-deficient groups (potassium deficiency, calcium deficiency, and magnesium deficiency), and performed feature extraction analysis on images of different defects. We used traditional algorithms (k-nearest neighbor, support vector machine, random forest) and lightweight deep-learning models (ShuffleNet, SqueezeNet, andMobileNetV2) for classification, and we compared different feature extraction methods (texture features, color features, scale-invariant feature transform features). The experiment shows that, under the optimal feature extraction method (color), the random-forest recognition results are the best, with an accuracy rate of 97.6%, a precision rate of 97.9%, a recall rate of 97.4%, and an F1 score of 97.6%. The accuracies of all three deep-learning models exceed 99.5%, among which ShuffleNet is the best, with the accuracy, precision, recall, and F1 score above 99.8%. It also uses fewer floating-point operations per second and less time. The proposed method can quickly identify the trace elements lacking in lettuce, and it can provide technical support for the visual recognition of the disease patrol robot in the plant factory.