A cow behavior monitoring system based on the Internet of Things (IoT) has been designed and implemented using tri-axial accelerometer, MSP430 microcontroller, wireless radio frequency (RF) module, and a laptop. The implemented system measured cow movement behavior and transmitted acceleration data to the laptop through the wireless RF module. Results were displayed on the laptop in a 2D graph, through which behavior patterns of cows were predicted. The measured data from the system were analyzed using the Multi-Back Propagation-Adaptive Boosting algorithm to determine the specific behavioral state of cows. The developed system can be used to increase classification performance of cow behavior by detecting acceleration data. Accuracy exceeded 90% for all the classified behavior categories, and the specificity of normal walking reached 96.98%. The sensitivity was good for all behavior patterns except standing up and lying down, with a maximum of 87.23% for standing. Overall, the IoT-based measurement system provides accurate and remote measurement of cow behavior, and the ensemble classification algorithm can effectively recognize various behavior patterns in dairy cows. Future research will improve the classification algorithm parameters and increase the number of enrolled cows. Once the functionality and reliability of the system have been confirmed on a large scale, commercialization may become possible.

Rapid acquisition of chlorophyll content in maize leaves is of great significance for timely monitoring of maize plant health and guiding field management. In order to accurately detect the relative chlorophyll content of summer maize and study the responsiveness of vegetation indices to SPAD (soil and plant analyzer development) values of summer maize at different spatial vertical scales, this paper established a prediction model for SPAD values of summer maize leaves at different spatial scales based on UAV multispectral images. The experiment collected multispectral image data from summer maize at the jointing stage and selected eight vegetation indices. By using the sparrow search optimized kernel limit learning machine (SSA-KELM), the prediction models for canopy leaf (CL) SPAD_CL and ear leaf (EL) SPAD_EL were established, and a linear fitting analysis was conducted combining the measured SPAD_CL values and SPAD_EL values on the ground. The results showed that for SPAD_CL, the R² of the linear fitting between the predicted values and measured values was 0.899, and the RMSE was 1.068. For SPAD_EL, the R² of linear fitting between the predicted values and the measured values was 0.837, and the RMSE was 0.89. Compared with the model established by the partial least squares method (PLSR), it is found that the sparrow search optimized kernel limit learning machine (SSA-KELM) has more precise prediction results with better stability and adaptability for small sample prediction. The research results can provide technical support for remote sensing monitoring of the chlorophyll content of summer maize at different spatial scales.

In order to improve the efficiency and quality of transplanting vegetables in dry land, based on the seedling technology, transplanting effect and analysis of the planting process, a potted vegetable seedlings transplanting machine was designed. It mainly comprised rotary disc type feeding mechanism, five-bar duckbill type planting mechanism (simulated duckbill mechanism, disc cam, connecting rod, crank, fork, cable) and power transmission system. Based on the physical parameters of the seedlings and design requirements, it was determined that the diameter of the duckbill was D=90 mm, the opening and closing angle was 25°, and the taper angle was 17°. A test bench with adjustable parameters was built by analyzing the structure of the planting mechanism and the motion of the working process. The digital speckle technique was used to optimize the parameters, so that the length of the crankshaft Ι was S1=100 mm, the length of the crankshaft ΙΙ was S2= 80 mm, the length of the connecting rod Ι was S3=140 mm, the length of the connecting rod ΙΙ was S4=260 mm, the length of rod to connecting the rack was S6=314 mm, and the height of planting track was H=450 mm. According to the above parameters and the control requirements of the duckbill mechanism, the cam stroke was determined to be S=15 mm. And the initial phase difference between the two cams was 180°. The experiment was carried out with pepper seedlings as transplanting objects. The results showed that when the planting frequency was 50-70 plants/min, the seedling upright rate was 93%-91.1%, the planting depth qualified rate was 96%-92%. The leakage rate was 0-0.26%, the variation coefficient of plant spacing was 0.37%-0.67%, the injury rate was 0%, and the mechanical damage degree of the mining surface was 2.43-3.77 mm/m2. The machine can effectively improve the quality of transplanting, which can meet the production needs and design requirements of the mechanism. Keywords: seedling transplanting, planting mechanism, digital speckle, parameter optimization, test analysis DOI: 10.25165/j.ijabe.20201301.5494 Citation: Jin X, Cheng Q, Zhao B, Ji J T, Li M Y. Design and test of 2ZYM-2 potted vegetable seedlings transplanting machine. Int J Agric & Biol Eng, 2020; 13(1): 101–110.

Aims: To screen heavy metal-tolerant strains from heavy metal-contaminated soil in mining areas and determine the tolerance of the strains to different heavy metals and their removal rates through experiments. Methods: Mercury-resistant strain LBA119 was isolated from mercury-contaminated soil samples in Luanchuan County, Henan Province, China. The strain was identified by Gram staining, physiological and biochemical tests, and 16S rDNA sequences. The LBA119 strain showed good resistance and removal rates to heavy metals such as Pb²⁺, Hg²⁺, Mn²⁺, Zn²⁺, and Cd²⁺ using tolerance tests under optimal growth conditions. The mercury-resistant strain LBA119 was applied to mercury-contaminated soil to determine the ability of the strain to remove mercury from the soil compared to mercury-contaminated soil without bacterial biomass. Results: Mercury-resistant strain LBA119 is a Gram-positive bacterium that appears as a short rod under scanning electron microscopy, with a single bacterium measuring approximately 0.8 × 1.3 μm. The strain was identified as a Bacillus by Gram staining, physiological and biochemical tests, and 16S rDNA sequence analysis. The strain was highly resistant to mercury, with a minimum inhibitory concentration (MIC) of 32 mg/L for mercury. Under a 10 mg/L mercury environment, the optimal inoculation amount, pH, temperature, and salt concentration of the LBA119 strain were 2%, 7, 30 °C, and 20 g/L, respectively. In the 10 mg/L Hg²⁺ LB medium, the total removal rate, volatilization rate, and adsorption rate at 36 h were 97.32%, 89.08%, and 8.24%, respectively. According to tolerance tests, the strain showed good resistance to Pb²⁺, Mn²⁺, Zn²⁺, Cd²⁺, and other heavy metals. When the initial mercury concentration was 50 mg/L and 100 mg/L, compared with the mercury-contaminated soil that contained an LB medium without bacterial biomass, LBA119 inoculation increased 15.54–37.67% after 30 days of culture. Conclusion: This strain shows high bioremediation potential for mercury-contaminated soil.