Laser-induced breakdown spectroscopy (LIBS) is a promising alternative to conventional methods in classifying citrus huanglongbing (HLB). Mature citrus fruits with similar features were picked and divided into healthy and HLB-asymptomatic groups. LIBS spectra and images were collected by focusing a laser on fresh fruit surfaces without sample preparation. The pH value and soluble solids content of juice as the indicators of acidity and sugar were detected, and the content of Ca, Zn, and K in peel and pulp was analyzed. The characteristic lines from LIBS spectra were extracted by continuous wavelet transform and principal component analysis (PCA). The t -test of these indicators displayed significant difference between the two groups. Fisher discriminant analysis and multilayer perception neural network (MLP) were applied to identify the disease. The classification accuracy reached 100% by PCA-MLP. The results show that LIBS can realize in situ detection of citrus HLB fruits.

To address the low efficiency and high flower bud damage rate in camellia-fruit-picking machines, a multichannel camellia-fruit-picking device was designed. Three factors and levels of the quadratic orthogonal rotation combination test method were used in the test. The experimental results show that the fruit picking and bud damage rates were affected most by the roller speed, followed by the distance between rollers, and, finally, the roller diameter. The optimal parameter combination was determined to be the roller speed of 130 mm/s, distance between rollers of 21 mm, and roller diameter of 30 mm, as verified by outdoor experiments. The average camellia-fruit-picking and bud damage rates were 95.68% and 3.92%, respectively. Compared with a rotating rubber roller picking device, the average flower bud damage rate of the multichannel camellia-fruit-picking device was reduced by 14.71 percentage points.

Objective At present，the planting of Camellia forests is not standardized，and the space of Camellia forest is narrow for harvesting machine.Therefore，a motor-drive Camellia fruit vibration harvesting device is designed. Method The biomechanics of the fruit and flower buds of the main variety of Camellia in Jiangxi Province，Changlin 40，were measured by means of ZDF-50 digital tension meter，and the mass of Camellia fruit and Camellia flower buds was measured using the LW300X electronic scale.Based on the difference in mass between the Camellia fruit and flower buds，which resulted in different forced vibration inertial forces，the harvesting principle of Camellia fruit was determined The key components of the picking device，such as the vibration motor，vibration mechanism，lateral branch clamping mechanism，and lifting mechanism，were selected and designed.Based on the mechanical characteristics of Camellia fruit vibration harvesting，the critical acceleration conditions for Camellia fruit vibration shedding was obtained.Using the results of Camellia fruit picking rate and flower bud damage rate as experimental indicators，and vibration frequency，vibration time，and clamping distance as factors，orthogonal experiments were conducted in the oil tea forest of Deyiyuan ecological agriculture development company in Jiangxi Province，The optimal parameter combination was obtained using a weighted method. Result The factors that affect the net harvesting rate of Camellia fruits are vibration time，clamping distance，and vibration frequency； The impact on the damage rate of flower buds，in descending order，is vibration frequency，vibration time，and clamping distance.When the vibration time is 5 s，the vibration frequency is 12 Hz，and the clamping distance is 8 mm，the net harvesting rate of Camellia fruit is 93.93%，and the bud damage rate is 4.34%. Conclusion The motor-drive harvesting device for Camellia fruit designed in this study can adapt to narrow oil tea forest operations，meet the technical requirements for Camellia fruit harvesting，which provide reference for the development of Camellia fruit harvesting machines.

Accurate detection of Camellia oleifera fruits in natural orchards is essential for automated harvesting and yield-related phenotyping but is challenged by small fruit size, dense distribution, foliage occlusion, and fruit–leaf similarity. To address these challenges, we developed CO-YOLO, an enhanced YOLOv11-based detector that integrates a spatial attention module (SAM) and coordinate attention (CA) to jointly enhance spatial discriminative feature extraction and positional encoding. Unlike approaches that employ attention mechanisms primarily to reweight features, CO-YOLO combines spatial attention with coordinate-aware feature representation to improve the identification and localization of partially occluded fruits while retaining the YOLOv11 detection framework. A field dataset of 674 raw RGB images was collected under different fruit developmental stages, weather conditions, illumination environments, and occlusion levels. Compared with YOLOv11, CO-YOLO improved precision, recall, mAP@0.50, mAP@0.75, mAP@0.50:0.95, and F1-score by 2.46, 3.17, 2.96, 3.64, 3.42, and 2.56 percentage points, respectively, with greater improvements under stringent localization criteria. Ablation experiments confirmed the complementary contributions of SAM and CA, while environmental evaluation showed that immature fruits, rainy conditions, and dense occlusion remained the main sources of performance degradation. Fruit-counting analysis further indicated improved consistency between automated and manual counts, although dense fruit clusters continued to introduce counting errors. Overall, CO-YOLO improves the detection of Camellia oleifera fruit under complex orchard conditions while preserving the efficiency of the YOLOv11 framework, and thus offers a practical visual perception approach for automated harvesting and yield-related phenotyping.