Tea Yield Research Articles

A review on the application of computer vision and machine learning in the tea industry

Tea is rich in polyphenols, vitamins, and protein, which is good for health and tastes great. As a result, tea is very popular and has become the second most popular beverage in the world after water. For this reason, it is essential to improve the yield and quality of tea. In this paper, we review the application of computer vision and machine learning in the tea industry in the last decade, covering three crucial stages: cultivation, harvesting, and processing of tea. We found that many advanced artificial intelligence algorithms and sensor technologies have been used in tea, resulting in some vision-based tea harvesting equipment and disease detection methods. However, these applications focus on the identification of tea buds, the detection of several common diseases, and the classification of tea products. Clearly, the current applications have limitations and are insufficient for the intelligent and sustainable development of the tea field. The current fruitful developments in technologies related to UAVs, vision navigation, soft robotics, and sensors have the potential to provide new opportunities for vision-based tea harvesting machines, intelligent tea garden management, and multimodal-based tea processing monitoring. Therefore, research and development combining computer vision and machine learning is undoubtedly a future trend in the tea industry.

Tea Leaf Disease Classification using Domain Adaptation Method

Tea trees are extremely vulnerable to diseases and insect pests in the growth process, which seriously affects the yield and quality of tea leaves. This requires the identification and treatment of infected tea leaves in time. With the rapid development of artificial intelligence and computer vision, there is a new trend to identify the images by computer. However, there often exists environmental changes such as illumination intensity, sample angles and background in the leaf images collected in natural scenes, which results in the difference of data distribution. This makes the traditional deep learning method unable to solve the problem of cross-domain classification very well, thus seriously affecting the accuracy of classification. This study mainly focuses on the cross-domain task with unaligned data distribution. A domain-adaptive based method was proposed to realize the cross-domain classification of tea leaf diseases. The experimental results verify the effectiveness of the presented method and provide new thoughts for the cross-domain classification problem in agriculture.

A microbial consortium enhances tea yield and quality in a field study

There is a close relationship between belowground microorganisms and aboveground plant growth, and soil microbiomes contribute to plant growth, health and production. However, the influence of application microbial agents on soil microbial community structure and production of tea (Camellia sinensis) plantations remains unclear. To test this, under a microbial consortiuma (TCM) and water treatments, a conjoint analysis transcriptome and metabolome profiling was carried out in tea shoots harvested, focused on the carbon/nitrogen metabolism, plant immune response, and a analysis of the structure of the rhizosphere microbial community was carried out in rhizosphere soil harvested. Compared to the water treatment, the tea shoot dry weight, nutrients content (nitrogen and phosphorus) increased (p < 0.001), and promoted the nitrogen metabolism and weaked the carbon metabolism in some extent (p < 0.01), TCM inoculation also triggered direct or indirect defense effects in tea shoots by upregulating key genes in the jasmonic acid, ethylene, and salicylic acid pathways. The abundance of carbon/nitrogen cycles and predatory myxobacterial species were significantly enriched and were positively correlated with C. sinensis productivity and nutrition in the rhizosphere soil. The TCM consortium application positively affected carbon/nitrogen allocation, defense-growth synergy of C. sinensis by altering indigenous rhizobacterial communities (p < 0.01) and enhanced nutrient use efficiency. The TCM is a safe tea inoculant that could be used to enhance the quality and yield, showing a potential use as biofertilizers in C. sinensis cultivation.

Revealing the Inhibition of Tea Cultivar ‘Ziyan’ Root Growth Caused by High Nitrogen Based on Physiological, Metabolite, and Transcriptome Analyses

Nitrogen (N) is a key regulator in the growth of tea plants and the synthesis of amino acids (AAs) and related secondary metabolites, thus affecting the yield and quality of tea leaves. Increased N fertilization significantly improves the yield and quality of tea plants, but applying excess nitrogen wastes resources and causes pollution problems. Herein, we employed morphological, physiological, metabolomic, and RNA-seq methods to study the response of ‘Ziyan’ roots to high N. High N supply induced an increase in arginine (Arg), asparagine (Asn), and glutamine (Gln) in roots and simultaneously decreased sucrose, polyphenols, and caffeine contents. High N reduced the length, volume, number, and activity of the roots by 10.63%, 25.00%, 26.95%, and 14.50%, respectively, which inhibited ‘Ziyan’ root growth, probably by disturbing the regulation of carbon and nitrogen metabolism in the tea plant. According to the Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analysis, AAs, flavonoids, and flavonol-related pathways were relatively active after supplying high N. In addition, the transcriptome analysis identified NRT1/PTR and GOGAT as key genes, and the transcription factors (e.g., AP2/ERF, MYB, and WRKY) and auxins were actively involved in the high N stress response of ‘Ziyan’ roots. These findings will help us understand the adaptive mechanism of high N supply in tea tree roots and provide a reference for guiding the fertilization of ‘Ziyan’ tea plants.

TSBA-YOLO: An Improved Tea Diseases Detection Model Based on Attention Mechanisms and Feature Fusion

Tea diseases have a significant impact on the yield and quality of tea during the growth of tea trees. The shape and scale of tea diseases are variable, and the tea disease targets are usually small, with the intelligent detection processes of tea diseases also easily disturbed by the complex background of the growing region. In addition, some tea diseases are concentrated in the entire area of the leaves, needing to be inferred from global information. Common target detection models are difficult to solve these problems. Therefore, we proposed an improved tea disease detection model called TSBA-YOLO. We use the dataset of tea diseases collected at the Maoshan Tea Factory in China. The self-attention mechanism was used to enhance the ability of the model to obtain global information on tea diseases. The BiFPN feature fusion network and adaptively spatial feature fusion (ASFF) technology were used to improve the multiscale feature fusion of tea diseases and enhance the ability of the model to resist complex background interference. We integrated the Shuffle Attention mechanism to solve the problem of difficult identifications of small-target tea diseases. In addition, we used data-enhancement methods and transfer learning to expand the dataset and relocate the parameters learned from other plant disease datasets to enhance tea diseases detection. Finally, SIoU was used to further improve the accuracy of the regression. The experimental results show that the proposed model is good at solving a series of problems encountered in the intelligent recognition of tea diseases. The detection accuracy is ahead of the mainstream target detection models, and the detection speed reaches the real-time level.

Adverse effects of shading on the tea yield and the restorative effects of exogenously applied brassinolide

Shading during cultivation leads to increased tea quality, but its effect on tea yield remains unclear. In this study, shading decreased the tea plant yield because it resulted in the production of thin leaves and shortened internodes as well as a decreased photosynthetic rate. The exogenous application of brassinolide (BL), which is a novel phytohormone, recovered some of the lost yield caused by shading by increasing the leaf area, promoting internode elongation, and increasing the photosynthetic rate. Moreover, BL did not affect the enhancement effect of shading on tea quality. The exogenous application of BL could increase the content of total amino acid (AA), geraniol, chlorophyll and carotenoid, and reduce polyphenols-to-amino acids ratio (P/A) in green tea processed from shaded tea leaves. Our findings have clarified the effects of shading on tea plants and explored measures to address shading-induced yield losses without affecting the enhancement of quality, which have potential implications for limiting shading-induced yield losses in tea plants.

Effects of Shellfish and Organic Fertilizer Amendments on Soil Nutrients and Tea Yield and Quality.

Soil acidification in tea plantations leads to an excessive heavy metal content in tea, decreasing its yield and quality. How to apply shellfish and organic fertilizers to improve soil and ensure the safe production of tea is still not clear. A two-year field experiment was conducted in tea plantations in which the soil was characterized by a pH of 4.16 and concentrations of lead (Pb) (85.28 mg/kg) and cadmium (Cd) (0.43 mg/kg) exceeding the standard. We used shellfish amendments (750, 1500, 2250 kg/ha) and organic fertilizers (3750, 7500 kg/ha) to amend the soils. The experimental results showed that compared with the treatment without any amendment (CK), the soil pH increased by 0.46 on average; the soil available nitrogen, phosphorus, and potassium contents increased by 21.68%, 19.01%, and 17.51% respectively; and the soil available Pb, Cd, Cr, and As contents decreased by 24.64%, 24.36%, 20.83%, and 26.39%, respectively. In comparison to CK, the average yield of tea also increased by 90.94 kg/ha; tea polyphenols, free amino acids, caffeine, and water extract increased by 9.17%, 15.71%, 7.54%, and 5.27%, respectively; and the contents of Pb, Cd, As, and Cr in the tea decreased significantly (p < 0.05) by 29.44-61.38%, 21.43-61.38%, 10.43-25.22%, and 10.00-33.33%, respectively. The greatest effects on all parameters occurred with the largest amendment of both shellfish (2250 kg/ha) and organic fertilizer (7500 kg/ha) combined. This finding suggests that the optimized amendment of shellfish could be used as a technical measure to improve the health quality of both soil and tea in acidified tea plantations in the future.

Metabolomics analysis of the effect of acidification on rhizosphere soil microecosystem of tea tree.

Acidification can seriously affect the growth of tea trees and the yield and quality of tea leaves. In this study, we analyzed the effects of acidification on the physicochemical properties, microorganisms and metabolites of tea rhizosphere soils with different pH values, and the results showed that with the increase of soil pH, the organic matter content, cation exchange capacity, microbial biomass carbon, microbial biomass nitrogen, microbial respiration intensity, bacterial number and actinomyces number in tea rhizosphere soil all showed an increasing trend, while the fungi number decreased. The results of soil metabolite analysis showed that 2376, 2377 and 2359 metabolites were detected in tea rhizosphere soil with pH values of 3.29, 4.74 and 5.32, respectively, and the number of similar compounds reached 2331, accounting for more than 98%. The results of soil metabolite content analysis showed that with the increase of soil pH, the total contents of metabolite of tea rhizosphere soil increased significantly. The results of correlation analysis between physicochemical indexes of soil and microorganisms and soil metabolites showed that physicochemical indexes of soil and microorganisms were significantly correlated with 221 soil metabolites, among which 55 were significantly positively correlated and 166 were significantly negatively correlated. Based on correlation interaction network analysis, 59 characteristic compounds were obtained and divided into 22 categories, among which 7 categories compounds showed a significant increasing trend with the increase of soil pH, while the other 15 categories compounds showed the opposite trend. Based on the functional analysis of characteristic metabolites, this study found that with the increase of soil pH in tea rhizosphere, the diversity and number of soil microorganisms increased, and the cyclic ability of C and N of tea rhizosphere soil was enhanced, which in turn might lead to the enhancement of resistance of tea tree and promote the growth of tea tree.

A Novel LED Light Radiation Approach Enhances Growth in Green and Albino Tea Varieties.

Light, as an energy source, has been proven to strongly affect photosynthesis and, thus, can regulate the yield and quality of tea leaves (Camellia sinensis L.). However, few comprehensive studies have investigated the synergistic effects of light wavelengths on tea growth and development in green and albino varieties. Thus, the objective of this study was to investigate different ratios of red, blue and yellow light and their effects on tea plants' growth and quality. In this study, Zhongcha108 (green variety) and Zhongbai4 (albino variety) were exposed to lights of different wavelengths for a photoperiod of 5 months under the following seven treatments: white light simulated from the solar spectrum, which served as the control, and L1 (red 75%, blue 15% and yellow 10%), L2 (red 60%, blue 30% and yellow 10%), L3 (red 45%, far-red light 15%, blue 30% and yellow 10%), L4 (red 55%, blue 25% and yellow 20%), L5 (red 45%, blue 45% and yellow 10%) and L6 (red 30%, blue 60% and yellow 10%), respectively. We examined how different ratios of red light, blue light and yellow light affected tea growth by investigating the photosynthesis response curve, chlorophyll content, leaf structure, growth parameters and quality. Our results showed that far-red light interacted with red, blue and yellow light (L3 treatments) and significantly promoted leaf photosynthesis by 48.51% in the green variety, Zhongcha108, compared with the control treatments, and the length of the new shoots, number of new leaves, internode length, new leaf area, new shoots biomass and leaf thickness increased by 70.43%, 32.64%, 25.97%, 15.61%, 76.39% and 13.30%, respectively. Additionally, the polyphenol in the green variety, Zhongcha108, was significantly increased by 15.6% compared to that of the plants subjected to the control treatment. In addition, for the albino variety Zhongbai4, the highest ratio of red light (L1 treatment) remarkably enhanced leaf photosynthesis by 50.48% compared with the plants under the control treatment, resulting in the greatest new shoot length, number of new leaves, internode length, new leaf area, new shoot biomass, leaf thickness and polyphenol in the albino variety, Zhongbai4, compared to those of the control treatments, which increased by 50.48%, 26.11%, 69.29%, 31.61%, 42.86% and 10.09%, respectively. Our study provided these new light modes to serve as a new agricultural method for the production of green and albino varieties.

Fresh Yield Estimation of Spring Tea via Spectral Differences in UAV Hyperspectral Images from Unpicked and Picked Canopies

At present, spring tea yield is mainly estimated through a manual sampling survey. Obtaining yield information is time consuming and laborious for the whole spring tea industry, especially at the regional scale. Remote sensing yield estimation is a popular method used in large-scale grain crop fields, and few studies on the estimation of spring tea yield from remote sensing data have been reported. This is a similar spectrum of fresh tea yield components to that of the tea tree canopy. In this study, two types of unmanned aerial vehicle (UAV) hyperspectral images from the unpicked and picked Anji white tea tree canopies are collected, and research on the estimation of the spring tea fresh yield is performed using the differences identified in the single and combined chlorophyll spectral indices (CSIs) or leaf area spectral indices (LASIs) while also considering the changes in the green coverage of the tea tree canopy by way of a linear or piecewise linear function. The results are as follows: (1) in the linear model with a single index variable (LMSV), the accuracy of spring tea fresh yield models based on the selected CSIs was better than that based on the selected LASIs as a whole, in which the model based on the curvature index (CUR) was the best with regard to the accuracy metrics; (2) compared to the LMSVs, the accuracy performance of the piecewise linear model with the same index variables (PLMSVs) was obviously improved, with an encouraging root mean square error (RMSE) and validation determination coefficient (VR2); and (3) in the piecewise model with the combined index variables (PLMCVs), its evaluation metrics are also improved, in which the best performance of them was the CUR&amp;CUR model with a RMSE (124.602 g) and VR2 (0.625). It showed that the use of PLMSVs or PLMCVs for fresh tea yield estimation could reduce the vegetation index saturation of the tea tree canopy. These results show that the spectral difference discovered through hyperspectral remote sensing can provide the potential capability of estimating the fresh yield of spring tea on a large scale.

Effects of Tea Plant Varieties with High- and Low-Nutrient Efficiency on Nutrients in Degraded Soil.

Tea plants are widely planted in tropical and subtropical regions globally, especially in southern China. The high leaching and strong soil acidity in these areas, in addition to human factors (e.g., tea picking and inappropriate fertilization methods) aggravate the lack of nutrients in tea garden soil. Therefore, improving degraded tea-growing soil is urgently required. Although the influence of biological factors (e.g., tea plant variety) on soil nutrients has been explored in the existing literature, there are few studies on the inhibition of soil nutrient degradation using different tea plant varieties. In this study, two tea plant varieties with different nutrient efficiencies (high-nutrient-efficiency variety: Longjing43 (LJ43); low-nutrient-efficiency variety: Liyou002 (LY002)) were studied. Under a one-side fertilization mode of two rows and two plants, the tea plant growth status, soil pH, and available nutrients in the soil profiles were analyzed, aiming to reveal the improvement of degraded soil using different tea varieties. The results showed that (1) differences in the phenotypic features of growth (such as dry tea yield, chlorophyll, leaf nitrogen (N), phosphorus (P), and potassium (K) content) between the fertilization belts in LJ43 (LJ43-near and LJ43-far) were lower than those in LY002. (2) RDA results showed that the crucial soil nutrient factors which determine the features of tea plants included available P, slowly available K, and available K. Moreover, acidification was more serious near the fertilization belt. The pH of the soil near LJ43 was higher than that near LY002, indicating an improvement in soil acidification. (3) Soil nutrient heterogeneity between fertilization belts in LJ43 (LJ43-near and LJ43-far) was lower than in LY002. In conclusion, the long-term one-side fertilization mode of two rows and two plants usually causes spatial heterogeneities in soil nutrients and aggravates soil acidification. However, LJ43 can reduce the nutrient heterogeneities and soil acidification, which is probably due to the preferential development of secondary roots. These results are helpful in understanding the influence of tea plant variety on improving soil nutrients and provide a relevant scientific reference for breeding high-quality tea varieties, improving the state of degraded soil and maintaining soil health.

Recent progress and perspectives on physiological and molecular mechanisms underlying cold tolerance of tea plants.

Tea is one of the most consumed and widely planted beverage plant worldwide, which contains many important economic, healthy, and cultural values. Low temperature inflicts serious damage to tea yields and quality. To cope with cold stress, tea plants have evolved a cascade of physiological and molecular mechanisms to rescue the metabolic disorders in plant cells caused by the cold stress; this includes physiological, biochemical changes and molecular regulation of genes and associated pathways. Understanding the physiological and molecular mechanisms underlying how tea plants perceive and respond to cold stress is of great significance to breed new varieties with improved quality and stress resistance. In this review, we summarized the putative cold signal sensors and molecular regulation of the CBF cascade pathway in cold acclimation. We also broadly reviewed the functions and potential regulation networks of 128 cold-responsive gene families of tea plants reported in the literature, including those particularly regulated by light, phytohormone, and glycometabolism. We discussed exogenous treatments, including ABA, MeJA, melatonin, GABA, spermidine and airborne nerolidol that have been reported as effective ways to improve cold resistance in tea plants. We also present perspectives and possible challenges for functional genomic studies on cold tolerance of tea plants in the future.

Effect of potassium fertilizer on tea yield and quality: A meta-analysis

The global tea (Camellia sinensis L.) output has been in decline despite the continued increases in global tea consumption amount and cultivation area. Application of potassium (K) fertilizer is a common way to enhance tea yield and quality, given the widespread deficiency of soil-available K in tea plantations. However, the specific effects of K fertilizer application upon tea yield and quality and its differential effects among varied K fertilizer application amount, soil-available K amount and tea types remain unknown. In this study, 518 pairwise comparisons between tea productivity after non-application and application of K fertilizers were obtained through a literature review, and then analyzed. The results showed that K application had a significant positive effect on tea yield and overall quality, enhancing the amount of tea yield, the content of amino acid, tea polyphenol, water extract, and catechin by 7.83 %, 7.84 %, 26.79 %, 1.57 % and 6.47 %, respectively. Furthermore, a K application rate of 90–120 kg ha−1 maximized the tea yield and quality. However, K application showed varied effects on tea yield and quality in soils with different available K levels. In terms of tea types, the yield and quality of black and green tea were enhanced under K application. The results of a mixed-effects model indicated that the main factors affecting tea quality were the amount of K fertilizer applied and the soil-available K content, whereas the variation in tea yield results mainly from the tea types. Therefore, to develop high-quality tea plantations and address the contradiction between low tea yield and high tea consumption, efforts should be made to enhance the soil-available K content by optimizing the amount of K applied on tea plantations.

Sustainable green tea production through agroecological management and land conversion practices for restoring soil health, crop productivity and economic efficiency: Evidence from Northern Vietnam

AbstractTea is a very important cash crop in Vietnam as it provides crucial income and employment for farmers in poor rural areas. Unfortunately, the dominance of long‐term, conventional tea cultivation has caused severe soil health degradation and environmental pollution. At the same time, as tea production may provide a better net income compared with other annual crops such as rice and vegetables, farmers have been converting parts of their allocated land to cultivate tea plants. Little is known about the benefit of agroecological management as an alternative to conventional tea management practices, and thus, there is a need to understand how it can improve tea yields, quality and the livelihoods of the farmers. Conducted in Northern Vietnam from 2019 to 2022, this study examined the impacts of agroecological tea management practices on soil health indicators, tea yield and quality, and net income of tea farmers. We showed that agroecological management practices significantly enhanced soil organic matter by 0.8% and soil pH by 0.5 units on average. Conversely, conventional management based on chemical fertilizer applications, significantly increased soil total nitrogen by 0.15%–0.2%. No significant differences were observed between soil texture and other soil chemical characteristics. Soil biological parameters were also significantly higher in agroecological tea soil and root samples than in conventional tea plots. Average AMF frequency and intensity of the agroecological tea roots were 98% and 37%, respectively, compared with 73% and 15% of the conventional tea roots. Likewise, soil macrofauna and mesofauna abundance in the agroecological tea plantations was 76 individuals/m2and 101 individuals/100 g fresh soil on average, respectively, while that of conventional tea farms were 34 and 63 individuals/100 g fresh soil, respectively. Interestingly, a comparison between the converted and nonconverted lands did not show any significant effect of the conversion on soil physicochemical and biological characteristics, apart from tea root AMF colonization. Conventional tea management consistently resulted in higher tea yield and yield components, even though the differences were not always statistically significant. Despite lower tea yields, agroecological tea adopters earned around USD 8400 ha/year more than the farmers still practicing conventional management. This study shows that it is economically and environmentally more sustainable to produce organic tea than conventional tea, and our results should encourage more farmers to adopt such agroecological practices in Northern Vietnam.

Risk Assessment and Application of Tea Frost Hazard in Hangzhou City Based on the Random Forest Algorithm

Using traditional tea frost hazard risk assessment results as sample data, the four indicators of minimum temperature, altitude, tea planting area, and tea yield were selected to consider the risk of hazard-causing factors, the exposure of hazard-bearing bodies, and the vulnerability of hazard-bearing bodies. The random forest algorithm was used to construct the frost hazard risk assessment model of Hangzhou tea, and hazard risk assessment was carried out on tea with different cold resistances in Hangzhou. The model’s accuracy reached 93% after training, and the interpretation reached more than 0.937. According to the risk assessment results of tea with different cold resistance, the high-risk areas of weak cold resistance tea were the most, followed by medium cold resistance and the least strong cold resistance. Compared with the traditional method, the prediction result of the random forest model has a deviation of only 1.57%. Using the random forest model to replace the artificial setting of the weight factor in the traditional method has the advantages of simple operation, high time efficiency, and high result accuracy. The prediction results have been verified by the existing hazard data. The model conforms to the actual situation and has certain guiding for local agricultural production and early warning of hazards.

Computer vision based method for severity estimation of tea leaf blight in natural scene images

Tea leaf diseases seriously affect the yield and quality of tea. Early warning and severity estimation of the diseases can be used to guide tea farmers to spray pesticide reasonably. Tea leaves infected with leaf blight are usually damaged, deformed, and occluded. An insufficient number of disease image samples will lead to overfitting of the estimated model. Thus, existing methods based on machine learning can only estimate the severity of tea diseases in natural scene images with low accuracy. Aiming to solve these problems, this study proposes a computer vision based method for the severity estimation of tea leaf blight in RGB images obtained under natural scenes. In this method, the influence of complex backgrounds is reduced by segmenting diseased tea leaves and spots, the problems of partial occlusion, deformation and damage of diseased leaves are solved by area fitting, and the severity of tea leaf blight is accurately estimated by the gradient boosting machine. Compared with classical machine learning methods and conventional convolution neural network methods, the method presented in this study only needs a small number of manually labeled samples and has better accuracy and robustness for the severity estimation of tea leaf blight in natural scene images.

SPATIAL MACHINE LEARNING FOR MONITORING TEA LEAVES AND CROP YIELD ESTIMATION USING SENTINEL-2 IMAGERY, (A Case of Gunung Mas Plantation, Bogor)

Indonesia's tea production and export volume have fluctuated with a downward trend in the last five years, partly due to the increasingly competitive world tea quality. Crop yield estimation is part of the management of tea plucking, affecting tea quality and quantity. The constraint in estimating crop yields requires technology that can make the process more effective and efficient. Remote sensing technology and machine learning have been widely used in precision agriculture. Recently, big data processing, especially remote sensing data, machine learning, and deep learning have been carried out using a cloud computing platform. Therefore, we propose using GeoAI, a combination of Sentinel-2A imagery, machine learning, and Google Collaboratory, to predict ready for plucking tea leaves at optimal plucking time at Gunung Mas Plantation Bogor. We used selected bands of Sentinel-2A and extracted more features (i.e., NDVI) as a training set. Then we utilized the tea blocks boundary and tea plucking data to generate labels using Random Forest (RF) and Support Vector Machine (SVM). The classification results were further used to estimate the production of crop tea yield. The RF classifier is able to achieve overall accuracy at 51% and SVM at 54%. Meanwhile, accuracy at optimally aged tea blocks is able to achieve at 75.62% for RF and 52.88% for SVM. Thus, the SVM classifier is better in terms of overall accuracy. Meanwhile, the RF classifier is superior in predicting ready for plucking tea at optimally aged tea blocks.

Ecosystem Service Synergies Promote Ecological Tea Gardens: A Case Study in Fuzhou, China

Exploring the trade-off/synergy among ecosystem services (ESs) of agroecosystems could provide effective support for improving agricultural resilience for sustainable development. The construction of ecological tea gardens is emerging, aims to achieve a win-win situation for the tea industry and ecological environment protection. However, the effect of ES trade-offs/synergies on tea production is still not clear. In this study, we selected Fuzhou city, China, as a case study and explored the relationship among tea production and ESs in 2010 and 2020. Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and Intelligent Urban Ecosystem Management System (IUEMS) models were used to assess the ecosystem (dis)services, which were tea production, water yield, soil retention, net primary productivity (NPP), climate regulation, soil erosion and carbon emissions. Then, the sum of trade-off/synergy coefficients of ESs (Cts) were defined to reveal the trade-off/synergy in tea gardens and areas except tea gardens (ETG areas). K-means clustering was used to assess the spatiotemporal change of traditional tea garden and ecological tea garden, reflecting the effect of ecological tea garden construction. The results showed that: (1) the high-value areas of tea production were mainly distributed in Lianjiang County, with yields up to 3.6 t/ha, and the low-value areas in Yongtai County, with yields from 0.1–1.0 t/ha. Other ESs showed spatial heterogeneity. (2) The trade-offs in ETG areas intensified from 2010 to 2020, with Cts decreasing from −0.28 to −0.73, and the synergy in tea garden was at risk of decline, with Cts decreasing from 4.46 to 1.02. (3) From 2010 to 2020, 96.72% of traditional tea gardens (Area I) were transformed into ecological tea gardens (Areas IV and V). (4) Further, we classified the tea garden into five zones based on tea yield, with Zone I as the low tea yield areas and Zone V as the highest. From Zone I to Zone V, the Cts increased from 2.6 to 7.5 in 2010, and from 1.9 to 6.5 in 2020, respectively. These results demonstrate the effectiveness of the construction of ecological tea gardens in Fuzhou and provide a reference for subsequent studies on the ESs of tea gardens and governance of ecological tea gardens.

UAV remote sensing detection of tea leaf blight based on DDMA-YOLO

Tea leaf blight (TLB) is a common disease that affects the yield and quality of tea. Timely and accurate detection and monitoring of TLB can help support the precise control of the disease. This study proposed an unmanned aerial vehicle (UAV) remote sensing method based on DDMA-YOLO for effectively detecting and monitoring TLB while reducing the workload and time consumption of this process. This method used the RCAN to reconstruct high-resolution tea images to solve the problem of insufficient resolution of UAV remote sensing images. In this method, Retinex was selected to enhance the image contrast and to reduce the influence of uneven illumination. The amount of training sample data was expanded to improve the model’s generalization performance. The DDMA-YOLO model was constructed to improve the accuracy of monitoring TLB. The DDMA-YOLO model was developed using the YOLOv5 network as the baseline and by adding a multiscale RFB module to the backbone to improve the extraction ability of the detailed features of diseased leaves and to reduce the problem of missed detection caused by small leaves. A dual-dimensional mixed attention (DDMA) was added to the Neck, which parallels coordinate attention with channel attention and spatial attention, integrates nonlocal attention information and local attention information, and reduces missed detection and false detection caused by dense blade distribution. The experimental results show that the proposed method was superior to the classic target detection methods Fast R-CNN, SSD, RetinaNet, YOLOv3, YOLOv4 and YOLOv5. Compared with the baseline network, the AP@0.5 of the proposed method increased by 3.8%, and the recall increased by 6.5%.

Solanum carolinense L.

<i>Solanum carolinense</i> L.