Resumo em Inglês:

ABSTRACT The use of mechanistic plant growth models relies on the availability of high-quality inputs to reduce uncertainty in estimates. Measurements of photosynthetically active radiation inside a protected environment are either more expensive to obtain or dependent on assumptions regarding external measurements. This study aimed to reduce the influence of uncertainty in the measurements of low-cost lux meters by using a data assimilation strategy. We first determined, by simulation, the impact of different sensors on the estimates. We then used the Ensemble Kalman Filter to assimilate artificial observations of tomato growth in the Reduced-State Tomgro model, in simulations for which the solar radiation inputs were obtained from a low-cost lux meter. We compared the assimilated estimates to the simulations that used solar radiation obtained with a scientific-grade quantum sensor. For periods of larger radiation intensity, in which the differences in measurements from both instruments are larger, assimilation of observations with low errors lead to estimates that are closer to the ones obtained by scientific grade sensors. These results suggest that low-cost sensors could be used to obtain inputs for growth models in protected environments, provided there are also imperfect observations of the state.

Resumo em Inglês:

ABSTRACT This paper discusses the design of a safflower picking robot control system and focuses on a navigation control subsystem based on multisensor fusion. A navigation subsystem, an identification and positioning subsystem, a picking subsystem, and a levelling subsystem are designed. The hardware and software of the navigation subsystem are designed in detail, and a multisensor fusion positioning method based on extended Kalman fusion technology is proposed. The accuracy and stability levels of different combined navigation methods are compared. To test the effectiveness and accuracy of the proposed method, an outdoor test is carried out. The test results show that the outdoor fusion positioning accuracy of the robot is less than 8 cm, and when the satellite signal is lost, the navigation control subsystem can still provide high positioning accuracy. The final positioning result obtained using the integrated positioning method of the wheel odometer + IMU + DGNSS is approximately 52% higher than that of the odometer, approximately 29% higher than that of the wheel odometer + IMU, and approximately 11% higher than that of the IMU + DGNSS.

Resumo em Inglês:

ABSTRACT In China, low levels of accuracy in predicting when pineapple crops will reach maturity can result from environmental variation such as light changes, fruit overlap, and shading. Therefore, this study proposed the use of an improved RetinaNet algorithm (ECA-Retinanet) based on the ECA attention mechanism. The ECA attention mechanism was embedded into the classification subnet of RetinaNet to improve accuracy in detecting different levels of maturity in pineapples. A new pineapple dataset was collected comprising four different growth stages under mild and severe complex scenarios. The experimental results have shown that the mAP (Mean Average Precision) and F1 score (Balanced Score) of the ECA-Retinanet model were 97.69%, 94.75%, 93.2%, and 90% for identification in mild and severe complex scenarios. These values are 0.42%, 2%, 1.78%, and 1.5% higher than the original RetinaNet model which exceeds those of the six existing state-of-the-art detection models. The results have indicated that the proposed algorithm could be used for accurate identification of pineapple fruit and can detect fruit maturity using ground color images in the natural environment. The study findings provide a technical reference for automatic picking robots and early yield estimation.

Resumo em Inglês:

ABSTRACT The widespread adoption of sensor technology has made it a standard practice for obtaining precise and timely information during the harvest and post-harvest periods. One sensor that has gained popularity for post-harvest seed monitoring is the MQ-3, which identifies ethanol in the air as products undergo fermentation. However, these sensors typically require a stable operation. This study aimed to assess the stabilization time of an MQ-3 sensor when measuring ethanol levels in anaerobic bean seeds. We used six bean seed samples, each with an average moisture content of around 14%. We employed a completely randomized experimental design with nine repetitions for each sample. Every repetition consisted of 25 bean seeds placed in sealed flasks containing 70 mL of distilled water. This setup induced anoxic conditions within the flask, promoting anaerobic respiration in the seeds. After 24 hours, we exposed an air sample to the MQ-3 sensor and took readings at various time intervals (12-14, 19-21, 36-38, 68-70, 130-132, 192-194, 314-316, 616-618 seconds). The average stabilization time for the MQ-3 sensor while quantifying ethanol concentrations in the bean samples were approximately 23 seconds. The sensor demonstrated efficacy, convenience, and rapidity in assessing ethanol levels in anaerobic bean seeds.

Resumo em Inglês:

ABSTRACT To enhance speed and agility in interpreting physiological quality tests of seeds, The use of algorithms has emerged. This study aimed to identify suitable machine learning models to assist in the precise management of seed lot quality. Soybean lots from two companies were assessed using the Supplied Test Set, Cross-Validation (with 8, 10, and 12 folds), and Percentage Split (with 66% and 70%) methods. Variables analyzed through Tetrazolium tests included vigor, viability, mechanical damage, moisture damage, bed bug damage, and water content. Method performance was determined by Kappa, Precision, and ROC Area metrics. Classification Via Regression and J48 algorithms were employed. The technique utilizing 66% of data for training achieved 93.55% accuracy, with Precision and ROC Area reaching 94.50% for the J48 algorithm. Applying the cross-validation method with 10 folds resulted in 90.22% of correctly classified instances, with a ROC Area outcome like the previous method. Tetrazolium Vigor was the primary attribute used. However, these results are specific to this study's database, and careful planning is necessary to select the most effective application methods.

Resumo em Inglês:

ABSTRACT An electric orchard tractor with a power battery and transmission driven by dual motors was developed. The output shafts of the walking and power take-off (PTO) motors are connected by a wet clutch, which controls whether the two motors are coupled or independent. When the load of the walking or PTO motor exceeds its output torque, the two motors are driven by power coupled by the wet clutch to meet the power demand. According to the heavy-load working conditions of ploughing and rototilling, the power battery capacity and working duration targets of 15 kW and 4 hours/charge were set. A prototype 15 kW electric orchard tractor was manufactured and assembled, and its performance was assessed. A bench test showed that the tractor’s maximum PTO output power was 13.9 kW and a field rototilling test showed that its maximum continuous working time was 4.5 hours. Thus, the prototype electric orchard tractor met the design goals and requirements of for orchard operations.

Resumo em Inglês:

ABSTRACT Reducing water consumption by crops in semi-arid regions is an important factor for the sustainability of agriculture in these locations. In this sense, this study aims to evaluate the neuro-fuzzy inference method as a support for decision-making in irrigated coriander cultivation. The experiment was performed in two cultivation cycles in Pentecoste-CE, Brazil. The experiment was conducted in randomized blocks arranged in a split-plot design with five primary treatments, consisting of irrigation depths (50, 75, 100, 125, and 150% of the localized evapotranspiration, ETcloc), and five secondary treatments, consisting of different levels of bagana mulch (0, 25, 50, 75, and 100%, equivalent to 16 t ha−1). Neuro-fuzzy models with two input variables and eight output biometric variables were developed to evaluate growth (plant height, number of roots, and root length) and yield variables (productivity and shoot and root fresh and dry mass). In the first cycle, the best results occurred close to 55% ETcloc and between 40 and 50% of mulch; in the second cycle, water consumption returned results between 50 and 80% ETcloc. The fuzzy and multiple regression models showed MAE, MSE, and RMSE errors of 9, 22, and 10% lower, respectively. The neuro-fuzzy model might be a viable option for decision-making in irrigated crops, being able to optimize the use of natural resources and available water in semi-arid regions. The use of 55% of irrigation depth and a range of 40 to 50% of mulch can be a strategy for a higher water use efficiency.

Resumo em Inglês:

ABSTRACT To address the problem of the low accuracy and poor robustness of modeling methods for imbalanced data sets of pig behavior identification and classification, the three commonly used re-sampling methods of under-sampling, SMOTE and Borderline-SMOTE are compared, and an adaptive boundary data augmentation algorithm AD-BL-SMOTE is proposed. The activity of the pigs was measured using triaxial accelerometers, which were fixed on the backs of the pigs. A multilayer feed-forward neural network was trained and validated with 21 input features to classify four pig activities: lying, standing, walking, and exploring. The results showed that re-sampling methods are an effective way to improve the performance of pig behavior identification and classification. Moreover, AD-BL-SMOTE could yield greater improvements in classification performance than the other three methods for balancing the training data set. The overall major mean accuracy of lying, standing, walking, and exploring by pigs A, B and C was significantly improved by using AD-BL-SMOTE, reaching 91.8%, 93.0% and 96.0%, respectively.

Resumo em Inglês:

ABSTRACT Management of agricultural production systems interferes with greenhouse gases (GHG) emissions, thereby altering physical, chemical, and biological attributes of soil; therefore, it is important to understand the relationship between soil attributes and GHG emissions. This study evaluated GHG emissions and their relationship with soil attributes in off-season soybean, maize, brachiaria and eucalyptus production systems. The experiment was carried out in Brejo, Maranhão, Brazil, with soybean ( Glycine max ), maize ( Zea mays ), brachiaria ( Urochloa ruzizienses ), and eucalyptus ( Eucalyptus grandis ). Fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) were evaluated using air samples analyzed by gas chromatography. Soil attributes were ammonium and nitrate contents, total organic carbon, moisture, pH, density, total porosity, and water-filled pore space. N2O flux was 287.1 µg m-2 h -1 for eucalyptus cultivation, while areas cultivated with soybeans, maize and brachiaria had influxes of 46.7, 7.2, and 13.17 µg m-2 h-1, respectively. In the off-season, the highest emissions of N2O and CO2 were measured in eucalyptus areas due to soil moisture and porosity conditions provided by accumulation of litter on the soil surface.

Resumo em Inglês:

ABSTRACT Materials from renewable sources have been increasingly used in construction to assist in sustainable development. Therefore, the use of timber becomes an environmentally advantageous solution, especially if used for long-term purposes, such as in structures. In this sense, this study aimed to evaluate the performance of glued-laminated timber frames reinforced in their main connections. The structures were made using timber from the Araucaria angustifolia species and polyurethane adhesive based on vegetable oils. The frames, on a reduced scale, had a pillar height and free span of 2 m and were reinforced in the connections between the roof beams and pillars. The rupture strength values of the structures averaged 38.02 kN, approximately 5.47 times the design load, and represented a 32% increase in strength compared to non-reinforced structures. The displacements did not reach the normative limit for the design load and presented a linear behavior for strengths higher than three times those of the design. The structures presented satisfactory structural behavior and had an improvement in resistance and rigidity caused by the addition of reinforcements.

Resumo em Inglês:

ABSTRACT Laboratory chemical analysis of leaf samples can be costly and time-consuming, making it impractical for assessing crop variability. To address this challenge, researchers have focused on developing non-invasive tools that aid nitrogen (N) management, maximizing profits, minimizing environmental impact, and meeting market demands. This study aimed to develop a computer vision-based classifier system for assessing the N status in bean crops. An experiment was conducted in a greenhouse, involving five treatments (0%, 50%, 100%, 150%, and 200% N of the recommended dose) with six replications, totaling 30 pots containing six seedlings of Phaseolus vulgaris L. beans in four different phenological phases (V4, R5, R6, and R7). Digital RGB images of the bean canopies were captured using a camera at four-week intervals (30, 37, 44, and 51 days after emergence - DAE). The images were manually labeled to create an image database based on N status. Four different computational N status classifiers were developed by training a Convolutional Neural Network (CNN), one for each DAE. The classifiers were evaluated using confusion matrix metrics (accuracy, precision, and recall), resulting in an overall accuracy of about 80% when evaluating nitrogen status at five levels. Improved results were achieved by grouping the saturation classes of the 150% and 200% treatments with the 100% class (>=100% class), yielding an accuracy of 97% for 30 and 44 DAE. Promising results aside, this method opens new possibilities for improvement and application to other treatments, electromagnetic spectrum bands, and crops.

Resumo em Inglês:

ABSTRACT The issue of reducing tillage resistance and lowering energy consumption has become increasingly relevant. Strip tillage has a positive impact on soil protection and energy consumption reduction. This study analysed the tillage resistance components of rotary blades and their relationship with operating parameters and developed a mathematical relationship between tillage resistance and energy consumption (power and fuel consumption). We summarised tillage resistance into three parts: air resistance, lateral rotational friction resistance (divided into horizontal and vertical resistance), and shear force. By theoretical analysis and coupled simulation tests 11 (DEM-CFD), we obtained an air resistance, lateral rotational friction, and shear force of 0.0883 N, 97.21 N, and 893.496 N, respectively, accounting for 0.01%, 9.81%, and 90.18% of tillage resistance. Meanwhile, we found that horizontal resistance decreased linearly with the increase of angular speed, which was the opposite of vertical resistance. Horizontal and vertical resistance increased linearly with the increase in contact area. Shear force increased significantly with angular speed. Fuel consumption in the field test and tillage resistance and power consumption in coupled simulation was formed to build a correspondence point, and a mathematical relationship between tillage resistance, torque, and power and fuel consumption was established.

Resumo em Inglês:

ABSTRACT Clay soil results in higher crop yield and quality than sandy soil. However, irrigation causes clay soil to slump easily, increasing compactness and decreasing soil oxygen content. This study investigated the effects of dry seeding and wet emergence on the soil microenvironment and cotton growth promotion in Xinjiang silt loam fields. The experimental design included three aerated and three non-aerated treatments. The results showed that aerated irrigation decreased dry density of the 0–20 cm soil layer to different degrees, the field capacity increased to different degrees, and the dry density and field capacity of the 20-30 cm soil layer did not change among the different treatments. The dry density and field capacity of WP2 treatment changed the most, the dry density of 0-10 cm and 10-20 cm soil layer were respectively 1.28 g cm-3 and 1.27 g cm-3, and the field capacity were respectively 35.23% and 35.7%. Under the same irrigation quota, the soil water content of the aerated treatments was lower than that of the non-aerated treatments. Aerated irrigation inhibited the horizontal diffusion of water and facilitated downward water transport. The WP2 treatment had the highest peak soil temperature at depths of 10 and 20 cm, and the WP2 treatment had the highest numbers of bacteria, fungi, actinomycetes, urease, and catalase activities, seedling emergence, primary root length, plant height, and stem thickness.

Resumo em Inglês:

ABSTRACT Increasing population and demand for animal-derived products has raised the need for improved efficiency in managing and controlling animal production. Given this context, the project aimed to develop a device that aids decision-making in animal production. A hardware system was designed for instant measurement of thermal well-being levels, light intensity, and air gas concentration. This hardware integrated DHT11 sensors, an LDR photoresistor, and an MQ-135 sensor. To validate the system, a 30-day experimental study was conducted in an industrial pig farming setting. The collected data was sent to the Thingspeak server using the HTTP protocol. Data management, filtering, and organization were optimized using developed treatment algorithms. The system presented information on air humidity, temperature, ammonia concentration, CO2 levels, luminosity, and enthalpy through interactive images on a dashboard. In the case of a risk situation, the system automatically notified users with an "ALERT" message, facilitating prompt and efficient management response, and minimizing losses. The sensor calibration process yielded a high coefficient of determination (r2 = 0.98). Thus, the developed IoT device represents a viable solution, providing precise environmental conditions to support producers and enhance their efficiency and sustainability.

Resumo em Inglês:

ABSTRACT Sorghum ( Sorghum bicolor L. Moench) is a crop of paramount importance for Brazil, used in human and animal food. Considering the reduced number of studies on the influence of using a densimetric table in seed processing and its respective reprocessing to the equipment, this study aimed to evaluate the physiological quality of processed sorghum seeds reprocessed in a densimetric table and stored for three months. The treatments consisted of four collection points in the terminal part of the densimetric table, representing the processing material: upper part (P1), upper middle part (P2), middle part (P3), and lower part (D). Subsequently, the samples that were reprocessed in the table came from P3 and were divided into the upper part, which was followed to bagging (P3R1), and the lower part (P3R2), which followed for disposal, totaling six treatments. Storage was performed in a refrigerated environment, using a completely randomized design, with the means being compared by Tukey’s test at 5%. We concluded that the reprocessing of sorghum seeds in the densimetric table can improve the physical quality of a lot due to its standardization at the end of the stages, contributing to the improvement of the physiological quality and use of the material that would be discarded. The equipment performance allows the removal of seed fractions with lower apparent specific mass and promotes positive stratifications in the physiological quality of sorghum seed lots.

Resumo em Inglês:

ABSTRACT This study proposes automating an air conditioning system for drying and storing agricultural products. The main objective is to precisely control key parameters of the air, namely temperature, relative humidity, and air velocity, to optimize the drying process. The framework required for constructing and operating the automated air conditioner is explained in detail. Specifically, we describe the conditioning unit, the automation hardware, the control strategies employed to regulate temperature, relative humidity, and air velocity, and discuss the safety measures and human-machine interfaces of the system. Experimental validation of the proposed system is presented, along with results and discussions demonstrating its successful control of drying air parameters across a wide range of desired conditions.