Monitoring anthocyanins is essential for assessing nutritional value and the growth status of plants. This study aimed to utilize hyperspectral technology to non-destructively monitor anthocyanin levels. Spectral data were preprocessed using standard normal variate (SNV) and first-derivative (FD) spectral processing. Feature wavelengths were selected using uninformative variable elimination (UVE) and UVE combined with competitive adaptive reweighted sampling (UVE + CARS). The optimal two-band vegetation index (VI2) and three-band vegetation index (VI3) were then calculated. Finally, dung beetle optimization (DBO), subtraction-average-based optimization (SABO), and the whale optimization algorithm (WOA) optimized the extreme learning machine (ELM) for modeling. The results indicated the following: (1) For the feature band selection methods, the UVE-CARS-SNV-DBO-ELM model achieved an R_m² of 0.8623, an RMSE_m of 0.0098, an R_v² of 0.8617, and an RMSE_v of 0.0095, resulting in an RPD of 2.7192, further demonstrating that UVE-CARS enhances feature band extraction based on UVE and indicating a strong model performance. (2) For the vegetation index, VI3 showed a better predictive accuracy than VI2. The VI3-WOA-ELM model achieved an R_m² of 0.8348, an RMSE_m of 0.0109 mg/g, an R_v² of 0.812, an RMSE_v of 0.011 mg/g, and an RPD of 2.3323, demonstrating good performance. (3) For the optimization algorithms, the DBO, SABO, and WOA all performed well in optimizing the ELM model. The R² of the DBO model increased by 5.8% to 27.82%, that of the SABO model by 2.92% to 26.84%, and that of the WOA model by 3.75% to 27.51%. These findings offer valuable insights for future anthocyanin monitoring using hyperspectral technology, highlighting the effectiveness of feature selection and optimization algorithms for accurate detection.

In order to solve the problem of accuracy and speed of disease identification in real-time spraying operation in maize field, an improved ResNet50 maize disease identification model was proposed. Firstly, this paper uses the Adam algorithm to optimize the model, adjusts the learning strategy through the inclined triangle learning rate, increases L2 regularization to reduce over fitting, and adopts exit strategy and ReLU incentive function. Then, the first convolution kernel of the ResNet50 model is modified into three 3 x 3 small convolution kernels. Finally, the ratio of training set to verification set is 3 : 1. Through experimental comparison, the recognition accuracy of the maize disease recognition model proposed in this paper is higher than that of other models. The image recognition accuracy in the data set is 98.52%, the image recognition accuracy in the farmland is 97.826%, and the average recognition speed is 204 ms, which meets the accuracy and speed requirements of maize field spraying operation and provides technical support for the research of maize field spraying equipment.

The information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral characteristics and random forests (RF). The method screens spectral characteristic variables based on the linear correlation analysis results of rice LKC and four transformed spectra (original reflectance (R), first derivative reflectance (FDR), continuum-removed reflectance (CRR), and normalized reflectance (NR)) of leaves and the PCA dimensionality reduction results of vegetation indices. Following a second screening of the correlated single band and vegetation index variables of the four transformed spectra, the RF is used to obtain the mixed variable (MV), and regression models are developed to achieve an accurate prediction of rice LKC. Additionally, the effect of potassium spectral sensitivity bands, indices, spectral transformation form, and different modeling methods on rice LKC prediction accuracy is assessed. The results showed that the mixed variable obtained with the second screening using the random forest feature selection method could effectively improve the prediction accuracy of rice LKC. The regression models based on the single band variables (BV) and the vegetation index variables (IV), FDR–RF and IV–RF, with R² values of 0.62301 and 0.7387 and RMSE values of 0.24174 and 0.15045, respectively, are the best models. In comparison to the previous two models, the MV–RF validation had a higher R² and a lower RMSE, reaching 0.77817 and 0.14913, respectively. It can be seen that the RF has a better processing ability for the MV that contains vegetation indices and IV than for the BV. Furthermore, the results of different variable screening and regression analyses also revealed that the single band’s range of 1402–1428 nm and 1871–1907 nm, as well as the vegetation indices constituted of reflectance 1799–1881 nm and 2276–2350 nm, are of great significance for predicting rice LKC. This conclusion can provide a reference for establishing a universal vegetation index related to potassium.

With the continuous advancement of agricultural Internet of Things (IoT) technologies, real-time monitoring of agricultural environments has become increasingly significant. This monitoring provides valuable information on pest and disease occurrences and corresponding ecological conditions. However, as the agricultural environments have extensive coverage, the number of monitoring IoT devices and the volume of information will rapidly increase. This leads to a surge in network traffic and computing demands. To address this issue, this article proposes an agricultural environmental monitoring IoT system that utilizes edge computing and deep learning technologies. It combines the Long-Range Wide Area Network (LoRaWAN) for long-range transmission with pest recognition and counting modules. By offloading workloads traditionally processed in the cloud to edge nodes, the proposed system effectively reduces transmission and cloud computing pressures for the agricultural monitoring IoT. Simulation experiments demonstrate stable LoRaWAN protocol-based data transmission at the edge, with an overall packet loss rate of less than 5%, meeting the transmission quality requirements. Moreover, this article investigates a pest recognition and counting method based on deep learning technology. Pest images, captured by monitoring nodes, are recognized and counted online using the TensorFlow framework. Experimental results indicate an accuracy of 89% in pest recognition. By digitally transmitting pest image recognition results to the cloud, the proposed system significantly alleviates transmission and cloud computing pressures for the monitoring IoT.

Although nitrogen deficiency and sucrose are linked to anthocyanin synthesis, the potential role of sucrose in regulating anthocyanin biosynthesis under low nitrogen conditions (LN) in purple lettuce (Lactuca sativa L.) remains unclear. We found that adding exogenous sucrose enhanced anthocyanin biosynthesis but significantly inhibited lettuce growth at high concentrations. Optimal results were obtained using 1 mmol/L sucrose in a low-nitrogen nutrient solution (LN + T1). Chlorophyll fluorescence imaging indicated that the addition of exogenous sucrose induced mild stress. Meanwhile, the activities of antioxidant enzymes (SOD, CAT, and POD) and antioxidant capacity were both enhanced. The mild stress activated the antioxidant system, thereby promoting the accumulation of anthocyanins induced by exogenous sucrose. LN + T1 (low nitrogen nutrient solution supplemented with 1 mmol/L sucrose) up-regulated enzyme genes in the biosynthetic pathway of anthocyanins, including phenylalanine ammonia-lyase (PAL), chalcone synthase (CHS), dihydroflavonol reductase (DFR), flavanone 3-hydroxylase (F3H), flavonoid 3′-hydroxylase (F3′H), flavone synthase II (FNSII), and anthocyanidin synthase (ANS). Additionally, various transcription factors such as AP2/ERF, MYB, bHLH, C2H2, NAC, C2C2, HB, MADS, bZIP, and WRKY were found to be up-regulated. This study elucidates the regulatory mechanism of anthocyanin metabolism in response to the addition of exogenous sucrose under low nitrogen conditions and provides a nutrient solution formula to enhance anthocyanin content in modern, high-quality agricultural cultivation.