Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Oolong tea cultivars categorization and germination period classification based on multispectral information

Recognizing and identifying tea plant (Camellia sinensis) cultivar plays a significant role in tea planting and germplasm resource management, particularly for oolong tea. There is a wide range of high-quality oolong tea with diverse varieties of tea plants that are suitable for oolong tea production. The conventional method for identifying and confirming tea cultivars involves visual assessment. Machine learning and computer vision-based automatic classification methods offer efficient and non-invasive alternatives for rapid categorization. Despite advancements in technology, the identification and classification of tea cultivars still pose a complex challenge. This paper utilized machine learning approaches for classifying 18 oolong tea cultivars based on 27 multispectral characteristics. Then the SVM classification model was executed using three optimization algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The results revealed that the SVM model optimized by GWO achieved the best performance, with an average discrimination rate of 99.91%, 93.30% and 92.63% for the training set, test set and validation set, respectively. In addition, based on the multispectral information (h, s, r, b, L, Asm, Var, Hom, Dis, σ, S, G, RVI, DVI, VOG), the germination period of oolong tea cultivars can be completely evaluated by Fisher discriminant analysis. The study indicated that the practical protection of tea plants through automated and precise classification of oolong tea cultivars and germination periods is feasible by utilizing multispectral imaging system.

Identifying and Counting Tobacco Plants in Fragmented Terrains Based on Unmanned Aerial Vehicle Images in Beipanjiang, China

Refined tobacco plant information extraction is the basis of efficient yield estimation. Tobacco planting in mountainous plateau areas in China is characterized by scattered distribution, uneven growth, and mixed/intercropping crops. Thus, it is difficult to accurately extract information on the tobacco plants. The study area is Beipanjiang topographic fracture area in China, using the smart phantom 4 Pro v2.0 quadrotor unmanned aerial vehicle to collect the images of tobacco planting area in the study area. By screening the visible light band, Excess Green Index, Normalized Green Red Difference Vegetation Index, and Excess Green Minus Excess Red Index were used to obtain the best color index calculation method for tobacco plants. Low-pass filtering was used to enhance tobacco plant information and suppress noise from weeds, corn plants, and rocks. Combined with field measurements of tobacco plant data, the computer interactive interpretation method performed gray-level segmentation on the enhanced image and extracted tobacco plant information. This method is suitable for identifying tobacco plants in mountainous plateau areas. The detection rates of the test and verification areas were 96.61% and 97.69%, and the completeness was 95.66% and 96.53%, respectively. This study can provide fine data support for refined tobacco plantation management in the terrain broken area with large exposed rock area and irregular planting land.