Coffee ripeness monitoring is a key indicator for defining the moment of starting the harvest, especially because the coffee quality is related to the fruit ripeness degree. The most used method to ...define the start of harvesting is by visual inspection, which is time-consuming, labor-intensive, and does not provide information on the entire area. There is a lack of new techniques or alternative methodologies to provide faster measurements that can support harvest planning. Based on that, this study aimed at developing a vegetation index (VI) for coffee ripeness monitoring using aerial imagery. For this, an experiment was set up in five arabica coffee fields in Minas Gerais State, Brazil. During the coffee ripeness stage, four flights were carried out to acquire spectral information on the crop canopy using two quadcopters, one equipped with a five-band multispectral camera and another with an RGB (Red, Green, Blue) camera. Prior to the flights, manual counts of the percentage of unripe fruits were carried out using irregular sampling grids on each day for validation purposes. After image acquisition, the coffee ripeness index (CRI) and other five VIs were obtained. The CRI was developed combining reflectance from the red band and from a ground-based red target placed on the study area. The effectiveness of the CRI was compared under different analyses with traditional VIs. The CRI showed a higher sensitivity to discriminate coffee plants ready for harvest from not-ready for harvest in all coffee fields. Furthermore, the highest R2 and lowest RMSE values for estimating the coffee ripeness were also presented by the CRI (R2: 0.70; 12.42%), whereas the other VIs showed R2 and RMSE values ranging from 0.22 to 0.67 and from 13.28 to 16.50, respectively. Finally, the study demonstrated that the time-consuming fieldwork can be replaced by the methodology based on VIs.
The selection of late blight resistant genotypes of tomato requires many evaluations on the field. The use of machine learning models to assess late blight severity based on images from multispectral ...cameras onboard of unmanned aerial vehicles (UAVs) can bring efficiency and quickly in evaluations. In this work, we use remote sensing and machine learning techniques to assess late blight resistance of tomato lines grown in open field conditions. Seventy-six tomato lines, including two resistant lines and one susceptible line, were used to quantify late blight severity. Plants were arranged according to a replicated check design in a total of 132 experimental plots. Tomato plants were artificially inoculated with a Phytophthora infestans zoospore suspension. Multispectral images were obtained using an unmanned aerial vehicle. We calculated vegetation indexes (VI) using the images, which were the basis for building the Random Forest models used to predict disease severity. Two methodologies were used to predict late blight severity: Methodology 1, which used only the images from the last day of evaluation, and Methodology 2, which used the images from four days of evaluation. For Methodology 1 and 2, determination coefficients of 0.81 and 0.93 were obtained for the test set, respectively. Methodology 2 was used to predict late blight severity of the 132 field plots. Tomato plots were sorted from lowest to highest predicted severity. Resistant plots were ranked first indicating consistency of prediction. We therefore recommend Methodology 2 as a fast and practical way to predicted late blight severity in breeding populations.
Machine Learning (ML) algorithms have been used as an alternative to conventional and geostatistical methods in digital mapping of soil attributes. An advantage of ML algorithms is their flexibility ...to use various layers of information as covariates. However, ML algorithms come in many variations that can make their application by end users difficult. To fill this gap, a Smart-Map plugin, which complements Geographic Information System QGIS Version 3, was developed using modern artificial intelligence (AI) tools. To generate interpolated maps, Ordinary Kriging (OK) and the Support Vector Machine (SVM) algorithm were implemented. The SVM model can use vector and raster layers available in QGIS as covariates at the time of interpolation. Covariates in the SVM model were selected based on spatial correlation measured by Moran’s Index (I’Moran). To evaluate the performance of the Smart-Map plugin, a case study was conducted with data of soil attributes collected in an area of 75 ha, located in the central region of the state of Goiás, Brazil. Performance comparisons between OK and SVM were performed for sampling grids with 38, 75, and 112 sampled points. R2 and RMSE were used to evaluate the performance of the methods. SVM was found superior to OK in the prediction of soil chemical attributes at the three sample densities tested and was therefore recommended for prediction of soil attributes. In this case study, soil attributes with R2 values ranging from 0.05 to 0.83 and RMSE ranging from 0.07 to 12.01 were predicted by the methods tested.
The macaw palm has been domesticated due to its potential use in the production of biofuel, in addition to several co-products that can be generated from its oil and pulp. One of the current ...challenges in this area is the harvesting, as there are no specific machines for this operation. Therefore, it is necessary to determine the appropriate information regarding the physical properties of the plant, so that it is feasible to develop the technologies necessary for the commercial scale application of macaw palm, allowing it to contribute to the sustainable production of raw material for the biofuel industry and other co-products. The principle of mechanical vibration can be used to shed fruit from trees when ripe, and it can be a method used for harvesting. Thus, as proposed in this study, it was necessary to study the dynamic behavior of the fruit-rachilla system during vibration. Hence, the modal properties of the system were determined. A study on the dynamic behaviors was carried out using a deterministic finite element model, and the natural frequencies were obtained through a frequency-scanning test to evaluate the model. The mean relative error (MRE) between the measured and simulated natural frequencies was also used to evaluate the model. The natural frequencies, determined experimentally, varied from 26.21 to 33.45 Hz on average, whereas the simulated frequencies varied from 24.81 to 39.27 Hz. The overall MRE was 9.08%. Once the model was validated, a sensibility test was carried out, which showed that the density of fruit and the elasticity modulus are the parameters that most influence the natural frequencies of the fruit-rachilla system.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Delineation of site-specific nutrient management zones (MZ) provides a basis for practical and cost-effective management of spatial soil fertility in precision agriculture. Therefore, the objective ...of this study was the delineation of MZs in a soybean field using geostatistics, principal component analysis (PCA), and the fuzzy k-means algorithm. The study was carried out in a field with 204 ha located in São Desidério, western Bahia state, Brazil (12 ° 25 ' 12" S, 45 ° 29ʹ 46" W). To do so, samples of soil attributes (0-20 cm), soybean yield, electrical conductivity (EC) at 0.20 m (EC02), 0.50 m (EC05), 1.00 m (EC1), 2.00 m (EC2) soil profile depth, and the Normalized Difference Vegetation Index (NDVI) were obtained in 204 points (100 x 100 m grid). After soil sampling and laboratory analyzes, the data were submitted to descriptive statistics and a Spearman correlation analysis was performed to select those attributes related to soybean yield. Then, the spatial variability of these attributes was assessed and spatial distribution maps were constructed using geostatistical tools. Next, PCA and fuzzy k-means algorithm were then performed to delineate MZs. Finally, the agreement between the MZs maps obtained from the PCA and soybean yield was assessed using the Kappa index. Results showed that the optimal number of MZs was two, which resulted in a Kappa index of 0.61 (very good). Moreover, the analysis of variance indicated heterogeneity between all attributes analyzed in the MZs. Finally, the defined MZs provide a basis of information for site-specific nutrient management.
The study aimed to analyze the distribution and spatial autocorrelation of irrigation concerning the other productive components of the garlic crop. The productive components were distributed in ...thematic maps, and the spatial autocorrelation was estimated by the Moran index, which quantifies the autocorrelation degree. Results show that irrigation contributes to higher yield, with bulbs of larger diameter and heavier cloves. Plants under drought stress conditions tend to develop wider and longer leaves with a higher shoot dry matter. The bivariate analysis revealed that irrigation in garlic is closely related to all explanatory variables.
Some compaction states cause changes in soil structure, resulting in increased soil density and soil resistance to penetration (RP). The objective of this study was: a) to analyze the variability of ...the studied attributes of the plant and the soil; b) define the linear and spatial correlations between plant and soil attributes; and c) to identify the best attributes that correlate spatially with garlic yield (GY) and lateral shoot growth (LSG) for the elaboration of spatial variability maps. The attributes evaluated were GY, apparent soil electrical conductivity (EC), mechanical resistance to penetration (MRP), soil volumetric moisture (SVM), plant water potential (WP), and LSG. The reach values of spatial dependence to be considered in future studies using the same attributes should be between 8 m for apparent soil EC and 23 m for RP. From a spatial point of view, garlic LSG could be estimated by indirect cokriging with soil RP. Values greater than 3000 kPa of soil RP indicated the sites with the lowest GYs.
ABSTRACT Soil sensors are alternatively used to reduce the costs of soil sampling and be able to perform analyses in a laboratory. However, using individual sensors can result in low accuracy because ...the measured variable may be related to more than one soil characteristics. This study aimed to develop a portable soil multisensor platform with sensors for apparent electrical conductivity, moisture, temperature, and penetration resistance of the soil. The multisensor platform was developed based on a BeagleBone Black (BBB) single-board computer. Electronic circuits have been developed for electrical conductivity and moisture sensors. A load cell was used in the soil-penetration resistance sensor. For validation, the ECa, soil moisture, and penetration resistance data obtained using the multisensor platform and commercial sensors were compared. A low-cost global navigation satellite system module was used to georeference sampling points. The cost of acquiring the components required to assemble the multisensor platform was US$ 361.94. The strong correlation between the data obtained with the multisensor platform and commercial sensors proves that the developed multisensor platform has acceptable accuracy. The spatial variability of the apparent soil electrical conductivity, moisture, temperature, and penetration resistance in a coffee plantation can be characterized by the generated maps from the data obtained by the four sensors.
Among the most common and serious tomato plant pests, leafminer flies (Liriomyza sativae) are considered one of the major tomato-plant-damaging pests worldwide. Detecting the infestation and ...quantifying the severity of these pests are essential for reducing their outbreaks through effective management and ensuring successful tomato production. Traditionally, detection and quantification are performed manually in the field. This is time-consuming and leads to inaccurate plant protection management practices owing to the subjectivity of the evaluation process. Therefore, the objective of this study was to develop a machine learning model for the detection and automatic estimation of the severity of tomato leaf symptoms of leafminer fly attacks. The dataset used in the present study comprised images of pest symptoms on tomato leaves acquired under field conditions. Manual annotation was performed to classify the acquired images into three groups: background, tomato leaf, and leaf symptoms from leafminer flies. Three models and four different backbones were compared for a multiclass semantic segmentation task using accuracy, precision, recall, and intersection over union metrics. A comparison of the segmentation results revealed that the U-Net model with the Inceptionv3 backbone achieved the best results. For estimation of symptom severity, the best model was FPN with the ResNet34 and DenseNet121 backbones, which exhibited lower root mean square error values. The computational models used proved promising mainly because of their capacity to automatically segment small objects in images captured in the field under challenging lighting conditions and with complex backgrounds.
The search for alternative energy sources has fomented the study of several crops. The macauba palm crop, for instance, has been highlighted because of its particular relevance in Brazil due to its ...wide distribution across Brazilian territory and its potential for yielding high amounts of oil per cultivated hectare. However, the species is still most commonly harvested via extractivism, which results in low yields. Therefore, we aimed to analyze the dynamic behavior of the fruit-rachilla system when subjected to mechanical vibration to gather baseline information for the subsequent development of macauba harvesting machines. The fruit-rachilla system of the species was modeled for different fruit maturation stages and plant accessions. Natural frequencies and modes of vibration were determined by the stochastic finite element method (FEM), adopting the specific mass and the modulus of elasticity of the system as random variables, which enabled us to compile a dataset of natural frequencies based on the variability of the system properties. The mean values of the natural frequencies obtained in the vibration assays were 26.02 Hz at the green maturation stage and 21.22 Hz at the ripe maturation stage. The mean values of natural frequencies found in the simulation by stochastic FEM, referring to the third mode of vibration, were 26.05 Hz at the green maturation stage and 21.23 Hz at the ripe maturation stage. We concluded that the natural frequencies of the macauba fruit-rachilla system on the basis of different plant accessions showed a decreasing behavior during fruit maturation. The modes of vibration characterized by pendulum displacement did not differ among plant accessions or between fruit maturation stages.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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