Commercial soil moisture sensors have been widely applied into the measurement of soil moisture content. However, the accuracy of such sensors varies due to the employed techniques and working conditions. In this study, the temperature impact on the soil moisture sensor reading was firstly analyzed. Next, a pioneer study on the data-driven calibration of soil moisture sensor was investigated considering the impacts of temperature. Different data-driven models including the multivariate adaptive regression splines and the Gaussian process regression were applied into the development of the calibration method. To verify the efficacy of the proposed methods, tests on four commercial soil moisture sensors were conducted; these sensors belong to the frequency domain reflection (FDR) type. The numerical results demonstrate that the proposed methods can greatly improve the measurement accuracy for the investigated sensors.

Saline water is a major resource for agricultural irrigation in arid-semi arid regions, especially when it is combined with drip irrigation. However, highly saline water can easily cause clogging of the emitters in drip irrigation systems, adversely affecting crop growth. Hence, a 2a processing tomatoes drip irrigation study was conducted in Hetao irrigation district. The chemical clogging of the emitters was analyzed using four drip irrigation models: RI1 (all fresh water irrigation), RI2 (saline water use in the flowering stage, fresh water in the fruiting stage), RI3 (fresh water use in the flowering stage, saline water in the fruiting stage), and RI4 (all saline water irrigation). The results revealed that the discharge ratio variation (Dra) and the Christiansen uniformity coefficient (CU) of RI4 decreased to 74.0% and 70.9%, respectively, which is considered as a clogged condition with poor irrigation uniformity. When compared to the all saline water irrigation model, the Dra and CU of fresh-saline alternating irrigation models (RI2 and RI3) were higher by 12.16% and 18.05%, respectively. Additionally, the dry weight (DW) of emitters fouling was less than that of RI4 by 16.30%. The Dra and CU showed linear relationships (R² > 0.79) for the different irrigation models. However, as the Dra declined, the more adverse influence on maintaining the high CU was found in RI4. Using irrigation models with alternating fresh-saline water were recommended to control chemical clogging in drip irrigation systems. Calcium carbonate (CaCO₃) was the dominant scale formed, which caused the emitters to clog when processing tomatoes were grown using a drip irrigation system with saline water.

Reference evapotranspiration (ET₀) is an important part of the water cycle and energy cycle during crop growth. Understanding the influencing factors and spatiotemporal variations of ET₀ can guide regional water-saving irrigation and regulate agricultural production. Data for daily meteorological observations of temperature, relative humidity, wind speed, and sunshine hours from 38 surface meteorological stations were used to analyze the spatiotemporal variations and trends in Shandong Province from 1980 to 2019. (1) The ET₀ from 1980 to 2019 was 1070.5 mm, and there was a significant downward trend in the climate tendency rate of −7.92/10 a. The climate of Shandong Province became warmer and drier. The average annual temperature showed a significant upward trend, while the average annual relative humidity and average annual sunshine hours showed significant downward trends. (2) The annual ET₀ ratio in spring, summer, autumn, and winter was 29%, 40%, 21%, and 10%, respectively. (3) A change in Shandong Province’s interannual ET₀ occurred in 2002, with a decrease of 130.74 mm since then. (4) The ET₀ was positively correlated with temperature, wind speed, and sunshine hours and negatively correlated with relative humidity. This study provides a scientific basis for the regulation and control of agricultural production in Shandong Province.

In this paper, a comparative study of the effectiveness of deep neural networks (DNNs) in the classification of pure and impure purees is conducted. Three different types of deep neural networks (DNNs)—the Gated Recurrent Unit (GRU), the Long Short Term Memory (LSTM), and the temporal convolutional network (TCN)—are employed for the detection of adulteration of strawberry purees. The Strawberry dataset, a time series spectroscopy dataset from the UCR time series classification repository, is utilized to evaluate the performance of different DNNs. Experimental results demonstrate that the TCN is able to obtain a higher classification accuracy than the GRU and LSTM. Moreover, the TCN achieves a new state-of-the-art classification accuracy on the Strawberry dataset. These results indicates the great potential of using the TCN for the detection of adulteration of fruit purees in the future.

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.