Monitoring fruit tree flowering information in the open world is more crucial than in the research-oriented environment for managing agricultural production to increase yield and quality. This work presents a transformer-based flowering period monitoring approach in an open world in order to better monitor the whole blooming time of modern standardized orchards utilizing IoT technologies. This study takes images of flowering apple trees captured at a distance in the open world as the research object, extends the dataset by introducing the Slicing Aided Hyper Inference (SAHI) algorithm, and establishes an S-YOLO apple flower detection model by substituting the YOLOX backbone network with Swin Transformer-tiny. The experimental results show that S-YOLO outperformed YOLOX-s in the detection accuracy of the four blooming states by 7.94%, 8.05%, 3.49%, and 6.96%. It also outperformed YOLOX-s by 10.00%, 9.10%, 13.10%, and 7.20% for mAP_ALL, mAP_S, mAP_M, and mAP_L, respectively. By increasing the width and depth of the network model, the accuracy of the larger S-YOLO was 88.18%, 88.95%, 89.50%, and 91.95% for each flowering state and 39.00%, 32.10%, 50.60%, and 64.30% for each type of mAP, respectively. The results show that the transformer-based method of monitoring the apple flower growth stage utilized S-YOLO to achieve the apple flower count, percentage analysis, peak flowering time determination, and flowering intensity quantification. The method can be applied to remotely monitor flowering information and estimate flowering intensity in modern standard orchards based on IoT technology, which is important for developing fruit digital production management technology and equipment and guiding orchard production management.

Obtaining complete and consistent spectral images of wheat ears in the visible areas of in situ wheat canopies poses a significant challenge due to the varying growth posture of wheat. Nevertheless, detecting the presence and degree of wheat Fusarium head blight (FHB) in situ is critical for formulating measures that ensure stable grain production and supply while promoting green development in agriculture. In this study, a spectral quantitative analysis model was developed to evaluate the infection degree of FHB in an in situ wheat canopy’s visible areas. To achieve this, a spectral acquisition method was used to evaluate the infection degree of FHB in a wheat canopy’s visible areas. Hyperspectral images were utilized to obtain spectral data from healthy and mildly, moderately, and severely infected wheat ear canopies. The spectral data were preprocessed, and characteristic wavelengths were extracted using twelve types of spectral preprocessing methods and four types of characteristic wavelength extraction methods. Subsequently, sixty-five spectral quantitative prediction models for the infection degree of FHB in the in situ wheat canopy visible areas were established using the PLSR method, based on the original spectral data, preprocessed spectral data, original spectral characteristic wavelengths extracted data, and preprocessed spectral characteristic wavelengths extracted data. Comparative analysis of the models indicated that the MMS + CARS + PLSR model exhibited the best prediction effect and could serve as the spectral quantitative analysis model for the evaluation of the infection degree of FHB in an in situ wheat canopy’s visible areas. The model extracted thirty-five characteristic wavelengths, with a modeling set coefficient of determination (R²) of 0.9490 and a root-mean-square error (RMSE) of 0.2384. The testing set of the coefficient of determination (R²) was 0.9312, with a root-mean-square error (RMSE) of 0.2588. The model can facilitate the spectral quantitative analysis of the infection degree of FHB in the in situ wheat canopy visible areas, thereby aiding in the implementation of China’s targeted poverty alleviation and agricultural power strategy.

This article discusses the salt stress in strawberry seedlings under greenhouse conditions in summer. Spectral acquisition equipment was used to obtain spectral data, and the ambient and leaf temperatures were combined to model and analyze the relative chlorophyll content in the strawberry seedling leaves. Four different salt gradients were employed to culture the strawberry seedings: S1 (0 mmol/L NaCl), S2 (50 mmol/L NaCl), S3 (100 mmol/L NaCl), and S4 (150 mmol/L NaCl). The results indicated that the spectral curves of the strawberry seedlings in groups S3 and S4 began to differentiate after day 3 (D3), and their average canopy temperature increased by 2.5 °C and 3.1 °C, respectively. The performance of traditional machine learning models integrating leaf temperature improved by more than 80%. Under each stress treatment, the one-dimensional ResNet model integrated with leaf temperature performed the best, with root mean square and mean absolute errors below 1.7 and 1.5, respectively. These results highlight the potential of incorporating temperature as an additional factor to improve the accuracy of plant stress assessments. By integrating temperature with spectral data, the model enhances the ability to monitor plant health dynamically and provides a more comprehensive understanding of how environmental factors influence plant physiology.

The aim of this study is to enhance the detection accuracy of rice chlorophyll content under complex backgrounds, optimize rice production management models, and improve the efficiency and quality of grain production. To achieve this, a spectral device for rice chlorophyll content detection with integrated background classification capability was developed. The device employed a MobileNetV4-Conv-Small-based model for rice background classification, enabling the categorization of clear, muddy, and green algae-covered backgrounds. The classification results showed that the model performed best under the green algae-covered background, with all performance metrics exceeding 97%. The muddy background followed, with metrics surpassing 94%, while the clear background proved more challenging, though the metrics still exceeded 93%. By combining preprocessing techniques with convolutional neural networks (CNNs), distinct rice chlorophyll content detection models were developed for each background type. For clear backgrounds, the optimal model was FD + CNN, with R², RMSE, and RPD values of 0.975, 5.191, and 6.318, respectively. For muddy backgrounds, the optimal model was SS + CNN, with R², RMSE, and RPD values of 0.627, 18.249, and 1.638, respectively. For green algae-covered backgrounds, the SS + CNN model also achieved the best results, with R², RMSE, and RPD values of 0.719, 16.417, and 1.885, respectively. Field experiments confirmed that the device achieved a rice background classification accuracy of 94.67% and a relative error compliance rate of 84.00% for chlorophyll content detection. These results demonstrate the feasibility of integrating background classification and chlorophyll content detection for rice cultivation.