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Pollination is essential for achieving high yields and enhancing the quality of kiwifruit cultivation, both of which significantly influence growers' interests and consumers' preferences. However, compared to studies on yield, there are fewer studies exploring the impact of pollination methods on the flavor of kiwifruit Actinidia chinensis Planchon. This study examined the effects of bee (Apis mellifera L.) pollination and artificial pollination on the yield and flavor of kiwifruit in the main producing areas of China. Compared with those pollinated artificially, bee‐pollinated kiwifruit exhibited a greater fruit set rate, heavier fruit weight, and greater number of seeds. Notably, the number of seeds was positively correlated with fruit weight in bee‐pollinated kiwifruit, whereas no such correlation was detected in artificially pollinated fruit. Bee pollination not only enhanced the yield but also improved the flavor of kiwifruit. Specifically, bee‐pollinated kiwifruit contained higher levels of sucrose and lower concentrations of glucose and fructose, while the acid content was less affected by pollination methods. Furthermore, significant differences were observed in the volatile organic compound (VOC) levels in kiwifruit subjected to different pollination treatments, with bee‐pollinated fruit exhibiting a superior flavor. Our findings provide new insights into the beneficial role of bee pollination in enhancing kiwifruit yield and quality, underscoring the crucial importance of bees in kiwifruit pollination.
Pollination is essential for achieving high yields and enhancing the quality of kiwifruit cultivation, both of which significantly influence growers' interests and consumers' preferences. However, compared to studies on yield, there are fewer studies exploring the impact of pollination methods on the flavor of kiwifruit Actinidia chinensis Planchon. This study examined the effects of bee (Apis mellifera L.) pollination and artificial pollination on the yield and flavor of kiwifruit in the main producing areas of China. Compared with those pollinated artificially, bee‐pollinated kiwifruit exhibited a greater fruit set rate, heavier fruit weight, and greater number of seeds. Notably, the number of seeds was positively correlated with fruit weight in bee‐pollinated kiwifruit, whereas no such correlation was detected in artificially pollinated fruit. Bee pollination not only enhanced the yield but also improved the flavor of kiwifruit. Specifically, bee‐pollinated kiwifruit contained higher levels of sucrose and lower concentrations of glucose and fructose, while the acid content was less affected by pollination methods. Furthermore, significant differences were observed in the volatile organic compound (VOC) levels in kiwifruit subjected to different pollination treatments, with bee‐pollinated fruit exhibiting a superior flavor. Our findings provide new insights into the beneficial role of bee pollination in enhancing kiwifruit yield and quality, underscoring the crucial importance of bees in kiwifruit pollination.
Thinning is an expensive and time-consuming management practice used in peach orchards to improve resource distribution among plants and improve production quality. Determining the right time and intensity for thinning is challenging and involves expertise. Furthermore, it generally consumes many hours of work, which makes, in some cases, unfeasible to analyze an entire orchard. For this reason, information that can assist in making making decisions about thinning can improve the cost–benefit ratio of the technique. To mitigate these problems, an electronic nose system, the e-nose, that explores the relationship between the smell of peach trees outdoors and the different growth phases was developed. Twenty-two composed volatile samples were collected from around peach trees (open environment) during its reproductive period (around 39 days) and five supervised machine learning classification algorithms (k-nearest neighbors (KNN), multilayer perceptron (MLP), random forest (RF), logistic regression (LR), and support vector machine (SMV)) were used to analyze the data, to evaluate the possibility of estimating phenological stages from odor environment. The result showed that all models achieved a balanced accuracy greater than 97.5%. As a secondary contribution, the importance of sensors was also analyzed for this application, and a combination of three sensors achieved a classification rate of 100% with the KNN classifier. The e-nose system was successful in distinguishing between petal drop, initial fruit formation, advanced fruit formation, and formed fruit. These results demonstrate the potential of using an electronic nose in a remote system to assist in decision-making in orchard practices as thinning.
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