The issue of emitter clogging is a common phenomenon in drip fertigation systems, which causes uneven irrigation and fertilization. However, efficient and environmentally friendly methods are scarcely available for alleviating clogging. In the present study, we investigated the anti-fouling efficacy of three magnetic field strength levels (0, 0.4 T and 0.6 T) on emitter clogging in drip fertigation systems. Our results show that magnetized water treatment could effectively relieve emitter clogging and delay the occurrence time of clogging, which increased the average discharge variation rate (Dra) by 37.00–61.64% and decreased the dry weight (DW) by 53.00–69.29% compared with non-magnetized water treatments. X-rays were used to estimate the compositions of the main clogging substances, and the results show that phosphates were the dominant substances that induced emitter clogging. In addition, magnetized water treatment effectively reduced the contents of chemical and particulate fouling, as exhibited by a decrease in phosphates, silicate and quartz by 53.17–69.58%, 47.22–61.95% and 43.18–74.80%, respectively. In comparison, the higher strength of magnetized water treatment (0.6 T) was better in clogging control, which increased Dra and the Christiansen of uniformity (CU) by 24.64% and 43.96%, respectively, and the DW was reduced by 34.67% compared with that of 0.4 T. This study proves that magnetized water treatment is an effective, chemical-free treatment method with great potential for fouling control technology, and it is helpful for the further promotion of drip fertigation technology.

The light intensity is low in haze weather, and the facility is in a weak light environment for a long time. As a functional film, light conversion film (LCF) can improve the light conversion performance and is conducive to regulating the environment in the facility to promote crop growth. It can be seen from the test that the light transmittance of LCF under visible light conditions (400–780 nm) is 8.67% higher than that of ordinary film (OF), with stronger light transmittance. In the red–orange light band (600–700 nm), the LCF is 1.3% higher than that of the OF. Through the detection of irradiance, it was found that the irradiance was outdoor environment > LCF > OF in any weather. A two-year greenhouse experiment was conducted to study the effect of LCF on the whole growth process of facility agriculture (environment-soil-crop) under weak light. It is found that LCF reduces the air humidity by 0.47~2.83%; it has an obvious warming effect on the surface soil of greenhouse, and it is linearly correlated with temperature. In terms of crop growth, LCF significantly (p < 0.05) increased the photosynthetic rate at heading stage, and finally increased the yield, total soluble sugar and reduction-type Vitamin C by 8.97–39.53%, 9.22–30.14%, and 1.41–21.09%, respectively. In addition, considering the frequent haze weather in North China, the use of LCF can improve air temperature, CO₂ concentration, photosynthetically active radiation (PAR), and soil temperature, and it can effectively deal with the challenge of weak light. In conclusion, LCF can improve the facility environment and improve crop yield and quality, indicating that the implementation of LCF has potential benefits in solving crop yield reduction and quality decline in haze weather. In addition, as the main component of LCF, rare earth materials are a new type of clean energy, which can effectively promote the sustainable development of the agricultural ecosystem.

Machine vision has significant advantages in a wide range of agricultural applications; however, acquiring a large number of high-quality image resources is often challenging in actual agricultural production due to environmental and equipment conditions. Therefore, crop image augmentation techniques are particularly important in crop growth analysis. In this paper, greenhouse tomato plants were used as research subjects to collect images of their different fertility stages with flowers and fruits. Due to the different durations of each fertility period, there is a significant difference in the number of images collected. For this reason, this paper proposes a method for balanced amplification of significant feature information in images based on geometric position. Through the geometric position information of the target in the image, different segmentation strategies are used to process the image and supervised and unsupervised methods are applied to perform balanced augmentation of the image, which is combined with the YOLOv7 algorithm to verify the augmentation effect. In terms of the image dataset, the mixed image dataset (Mix) is supplemented with mobile phone images on top of in situ monitoring images, with precision increased from 70.33% to 82.81% and recall increased from 69.15% to 81.25%. In terms of image augmentation, after supervised balanced amplification, the detection accuracy is improved from 70.33% to 77.29%, which is suitable for supervised balanced amplification. For the mobile phone dataset (MP), after amplification, it was found that better results could be achieved without any amplification method. The detection accuracy of the mixed dataset with different data sources matching the appropriate amplification method increased slightly from 82.81% to 83.59%, and accurate detection could be achieved when the target was shaded by the plant, and in different environments and light conditions.

The east-west ridge orientation has recently become an important agronomic method to improve mechanization in solar greenhouses. However, these ridge orientation changes shape differences between different ridges in crop water consumption, and there is a lack of research on the regulation and adaptation of water consumption. Therefore, this study introduces a variable irrigation decision-making method based on the Internet of Things management and control system for an east-west ridge orientation. To replenish water on demand, this study seting the variable irrigation decision-making (VRI) methods and traditional average irrigation decision-making (URI) methods in the system, and lettuce cultivation experiments were conducted to verify the effectiveness of the model and system. The results show that the difference of accumulated photosynthetically active radiation is the most significant between different ridges of the east-west ridge orientation, and the coefficient of variation is 43.77 %, which can be used as an activating factor for VRI methods. The irrigation water consumption, yield, water-use efficiencies, and irrigation water utilization of lettuce at different levels of irrigation were 307.12 L/m², 5854.07 kg·ha^-1, 1391.47 kg·ha^-1·mm^-1, and 7.63 kg·cm^-3, respectively. Compared with the URI methods, the VRI method saved 10.02 % of water, increased yield by 9 %, and enhanced water use efficiency and irrigation water use efficiency by 12 % and 21.32 %, respectively. This study provides a new approach for improving crop production efficiency under an east-west ridge orientation.

This study aims to enhance maize water demand calculation. We calculate crop evapotranspiration (ETc) through mobile phone photography and meteorological parameters. In terms of crop coefficient (Kc) calculation, we utilize the mobile phone camera image driver to establish a real-time monitoring model of Kc based on plant canopy coverage (PGC) changes. The calculation of PGC is achieved by constructing a PGC classification network and a Convolutional Block Attention Module (CBAM)-U²Net is implemented by the segment network. For the reference crop evapotranspiration (ETo) calculation, we constructed a simplified ETo estimation model based on SVR, LSTM, Optuna LSTM, and GWO-SVM using a public meteorological data-driven program, and evaluated its performance. The results demonstrate that our method achieves high classification accuracy for the PGC 98.9% and segmentation accuracy for the CBAM-U²net-based segmentation network 95.68%. The Kc calculation model exhibits a root mean square error (RMSE) of 0.053. In terms of ETo estimation, the Optuna-LSTM model with four variables demonstrates the best estimation effect, with a correlation coefficient (R²) of 0.953. The final R² between the estimated ETc value and the true value is 0.918, with an RMSE of 0.014. This method can effectively estimate the water demand of maize.