Resumo em Inglês:

ABSTRACT This efficiency in the application of water is associated with the evolution of irrigation equipment and systems and can be illustrated by the drippers in the case of localized irrigation, the sprinklers in the case of sprinkler irrigation, and the low-pressure emitters used in a central pivot. Given the context presented, this study aimed to carry out a theoretical approach on the classification and use of emitters used in pressurized sprinkler irrigation systems, carrying out bibliographical research on the subject. The research is exploratory, where articles and books with related subjects were researched: “classification and use of emitters used in pressurized sprinkler irrigation systems”. The research was carried out via the Google Academic platform and in libraries. The type of emitter to be chosen depends on many factors that influence the efficiency of the system. Therefore, a well-designed project, with the correct choice of the type of emitter, maximizes the application efficiency, minimizes energy expenditure, and increases the producer's profitability.

Resumo em Inglês:

ABSTRACT To explore the downwash airflow characteristics of a multi-rotor UAV and its effect on droplet movement and deposition, a comparative experiment between static-state and hovering-state spraying was carried out with a DJI MG-1P eight-rotor plant protection UAV. The results showed that the overall strength of the downwash airflow decreased further below the UAV. The direction of the airflow directly under the UAV was almost vertically downward and first increased and then decreased in speed. Closer to the ground, the airflow was directed outward with angles in the vertical direction of 71.3° and 81.5°. In general, the downwash airflow velocity and direction on both sides of the UAV were nearly symmetrically distributed. Compared with the static-state spraying, the high-speed downwash airflow in the hovering-state spraying significantly increased droplet velocity and size. The downwash airflow increased the amount of spray deposition in the different measurement layers but reduced the uniformity of the deposition. For the section (L-B-F-J-M) perpendicular to the flight direction, the near ground deposition was the best for the hovering-state spraying, with an average deposition of 5.85 μL/cm2 and an RSD of 36.87%. This study can be a reference for the optimization of the downwash airflow and the improvement of the spray application uniformity of multi-rotor UAVs.

Resumo em Inglês:

ABSTRACT Operational speed influences the soil preparation quality, the first established according to the working set performance and its traction capacity. The experiment's objective is to determine the influence of working speed on energy and operational performance of an agricultural tractor with Full-Powershift transmission when performing a harrowing operation. We conducted the experiment using lines, in a randomized block design. It had four operational soil preparation speeds, with seven repetitions, totaling 28 experimental units. We measured the following parameters per worked area: slipping, engine rotation, specific and per hour fuel consumption, strength, power, and yield on the drawbar and operating speed. Additionally, we analyzed soil profilometry parameters concerning the mobilized area and working thickness. We also evaluated the variance of the collected data and, when significant, submitted it to a regression test. The data showed that higher operating speeds result in greater operational performance and reduction of the energy demand of the mechanized set under study. In addition, this increase doesn't have a beneficial effect on grid fluctuation, not affecting the quality of soil preparation.

Resumo em Inglês:

ABSTRACT The process of determining the granulometric sludge of soil is represented by the dominant method of dry sieving on sieves. The procedural complexity of this method and the inability to quickly assess soil structure directly in the field led to the search for alternative methods of determination. The article summarizes the features and effectiveness of using three image analysis software packages FijiImageJ2, WipFrag v.3.3.14.0 and BASEGRAIN v.2.2.0.4 in the procedure for determining the morphological parameters of soil aggregates. In the studies used the processing of digital images of both the soil surface and the layer of identified fractions after sieving using the publicly available manual of these programs. Individual plugin functions of the programs for its application for soil analysis were determined. The possibility of using the FijiImageJ2 program to assess the microrelief of the soil layer of the corresponding fractions was determined. The effectiveness of WipFrag v.3.3.14.0 and BASEGRAIN v.2.2.0.4 programs for processing images of a layer of soil aggregates in the interval 2–>10 mm fractions was introduced. For the same programs, the possibility of forming additional indicators of the size of soil aggregates by such parameters as Feret diameters and the separation length criterion (D) was established.

Resumo em Inglês:

ABSTRACT The fertile land in Heilongjiang Province of China is suitable for rice cultivation, but this area is susceptible to low temperature and chilling injury, which is prevented by planting rice varieties suitable for GP that is an important measure. However, selection based on rice traits is vulnerable to environmental influences and takes a long time, and selection based on molecular markers may result in progeny recombination and lack of reliability. Therefore, an efficient accurate and intelligent identification method for rice growth period is urgently needed. In this study, machine learning and deep learning methods in Python were used to analyze the Raman spectra of 6 rice varieties in three accumulated temperature region of Heilongjiang Province. 1) In machine learning, Principal Component Analysis (PCA) was adopted for feature extraction, in combination with Support Vector Machine (SVM) classification models suitable for nonlinear data sets for identification, the identification rate was 93.33% and the type of this experimental data set was determined to be discrete. 2) In deep learning, Continuous Wavelet Transform (CWT) methods was adopted for data preprocessing, combined with the Convolutional Neural Networks (CNN) model with its own feature extraction, with the highest accuracy of 94.82%, which was higher than the PCA+SVM identification model. 3) Based on the method mentioned in 2), in order to improve the feature extraction ability of the model as a whole, Convolutional Block Attention Module (CBAM) was used to improve the CNN identification model for the first time for one-dimensional data sets, and the highest identification rate was 98.28%, which was better than the PCA+SVM identification model. 3) In the verification test, Raman spectral information of 4 rice varieties was brought into the constructed CWT+CNN-CBAM identification model for identification, and the identification results were as high as 94.79%. The experimental results showed that the CWT visualization data processing method based on Raman technology combined with the CNN identification model of CBAM with improved feature extraction ability in deep learning achieved the best identification results, which could provide an efficient, accurate and intelligent method for the identification of different growth period of rice varieties in Heilongjiang Province.

Resumo em Inglês:

ABSTRACT In soybeans from different years, the infrared spectroscopy waveforms exhibit similarities, yet variations in aging time lead to significant differences in their fatty acid content. To rapidly discern the year of soybean production, this study employed ten feature extraction algorithms in conjunction with Convolutional Neural Networks (CNN) to establish models for classifying soybeans from different years (2019, 2020, and 2021). The research findings reveal the following: (1) The CNN models, after feature extraction, all demonstrated improved accuracy. Notably, the optimal models for both powdered and granulated states were the Kpca-CNN models, achieving an accuracy of 100%. The corresponding loss function values were 0.0002 and 0.0007, with processing times of 0.19 seconds and 0.20 seconds, respectively. (2) The modeling results of all models suggest that the classification accuracy of soybean powder spectral data is higher compared to soybean particle spectral data. (3) Validation of the optimal Kpca-CNN model confirmed its consistent accuracy of 100% when introducing new data or reducing the size of the training dataset. In conclusion, the fusion of near-infrared spectroscopy analysis and CNN technology is considered an effective method for classifying soybeans from different years. This method provides a practical solution for the rapid and precise determination of seed production years.

Resumo em Inglês:

ABSTRACT Broccoli is increasingly favored by consumers as it is rich in vitamins and many other bioactive compounds. However, existing large tractor-drawn vegetable harvesting machinery is more suitable for large farmland and farm cultivation patterns. Therefore, this paper develops tracked broccoli harvesting equipment for small farmland and tests the ability to harvest broccoli. The tracked broccoli harvester developed in this paper is capable of traveling on its own in the field, cutting the stalks and transporting the broccoli bulbs. This paper analyses the working process of the cutting device and designs the working parameters. Through the cutting bench experiment, the optimal working parameters of the cutting device were determined as 456 r/min of rotational speed, 8.312 mm of overlap, and 0.255 m/s of pusher speed. In the field test, the broccoli leakage rate was 4.8%, the cutting qualification rate was 81.6%, the transport qualification rate was 95.62%, the damage rate was 3.96%, and the total loss rate was 8.55%. This structural model is an important technical support for broccoli harvesting equipment in China.

Resumo em Inglês:

ABSTRACT Adjustments in the nitrogen fertilization recommendation in wheat fields are essential to promote an increase in nitrogen (N) use efficiency by the crop. The real-time estimation of the nutritional demand of plants considering the spatial variability is one of the efficient ways to perform this adjustment. This study aimed to develop a model for the indication of topdressing nitrogen fertilization as a function of nutritional N demand using NDVI. Data from field experiments conducted in four regions of the State of Rio Grande do Sul, Brazil, during the 2015, 2017, 2018, and 2019 agricultural years using cultivars with significant sowing areas in the state, were used to construct the model. The analysis of the relationship between the amounts of N accumulated in the shoot and NDVI values allowed establishing a single model for different cultivars. The proposed model is sensitive to capturing regional edaphoclimatic differences and variability in N dynamics in cultivated areas. A consistent relationship was found between NDVI values and biomass and N content, which supported the generation of the topdressing N indication model for wheat. Therefore, the proposed model has the potential to maximize N use and grain yield, in addition to optimizing the economic return of the crop.

Resumo em Inglês:

ABSTRACT The yield of crops has a significant impact on economic and social development. It is significant to ensure the healthy growth of crops. Leaves can represent the growth of crops. Crop health can be monitored by analyzing a sufficient number of leaf images. But advanced farming techniques make leaves less susceptible to pests and diseases. Therefore, it is difficult to collect enough leaves with pests and diseases for image analysis. To solve this problem, this research proposed a method based on few-shot learning to identify crop leaf images and judge crop health status. The main structure of the method is a siamese network. The structure of its two subnetworks is the convolution neural network with an attention module. Each subnetwork outputs a feature vector. Measuring the distance of two feature vectors in the feature space, the similarity is calculated. Then the categories of leaf pests and diseases are judged. The experiments in this research were carried out on apple and potato leaves. The accuracy of identifying their pests and diseases reached 98.03% and 97.34% respectively. The experiment proved that when the sample size is small. The method proposed can effectively identify crop leaf pests and diseases.

Resumo em Inglês:

ABSTRACT The aim of this study was to assess how microclimate variables and irrigation affect the growth of sugarcane. The experiment was conducted using the VAT90-212 cultivar in the Semi-arid region of Brazil. The microclimate was monitored and by quantifying the irrigation depth. Nine campaigns were carried out in the field to collect morphological and biomass data. The data were subjected to descriptive statistics, so as to generate the mean values and/or sum between the campaigns. Using the correlation coefficient, the extent, sign and significance of the relationship between the response and explanatory variables were evaluated. The direct or indirect effects of the explanatory variables on the response variables were identified by path analysis. There was a significant correlation between microclimate variables and the morphological and biomass variables of the sugarcane, with a strong contribution from the intercepted fraction of the photosynthetically active radiation, the wind speed and soil temperature. The negative correlation with irrigation suggests that excess water impaired the growth dynamics of the crop. It is concluded that the growth of sugarcane is closely related to its capacity to intercept radiation, to the wind, thermal regime of the soil and irrigation management.