Spanish Journal of Agricultural Research
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2067 research outputs found
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Development and evaluation of a machine vision-based cotton fertilizer applicator:
No full textAim of study: To develop and assess a cotton fertilizer applicator integrated with a Machine Vision Based Embedded System (MVES) to achieve precise and site-specific fertilization.
Area of study: The investigation was performed in the Indian Institute of Technology, Kharagpur.
Material and methods: The MVES included a cotton detection system with a web camera, processor (computer), and python-based algorithm, and a fertilizer metering control unit with a stepper motor, motor driver, power supply, and microcontroller. The python-based algorithm in the computer predicts the presence (or absence) of cotton plants, whenever an input image is received from the camera. Upon cotton detection, it transforms into a Boolean signal sent to the microcontroller via PySerial communication, which instructs the motor to rotate the metering unit. Motor adjusts the speed of metering unit based on machine speed measured through a hall sensor, ensuring site-specific delivery of metered fertilizer A developed lab setup tested the MVES, experimentally examining performance indicators.
Main results: The MVES obtained a MAPE of 5.71% & 8.5%, MAD 0.74 g/plant & 1.12 g/plant for urea and DAP (di-ammonium phosphate), respectively. ANOVA revealed no statistically significant effect of forward speed on the discharge fertilizer amount (p>0.05). For urea, discharge rates ranged from 1.03 g/s (at 10 rpm, 25% exposure length of metering unit) to 40.65 g/s (at 100 rpm, 100% exposure). DAP ranged from 1.43 to 47.66 g/s under similar conditions.
Research highlights: The delivered application dosage conformed the recommended dosage. The developed MVES was reliable, had a quick response, and worked properly
Use of a mixture design to optimize dietary macronutrients for large turbot (Scophthalmus maximus Linnaeus, 1758)
No full textAim of study: Studies on the dietary needs of turbot fish (Scophthalmus maximus Linnaeus, 1758) have largely focused on the juvenile stage; however, there are not many on the larger (300–500 g) species. The purpose of this experiment was to determine the ideal dietary levels of protein, fat, and carbohydrate for large turbot.
Area of study: Demre, Antalya, Türkiye.
Material and methods: A three-component mixture design model was created to adjust the quantities of dietary protein between 45.6% and 63.4%, carbohydrates between 4.9% and 30.5%, and fat between 5.6% and 17.7%. The components of the model were fish meal (FM), fish oil (FO), and wheat flour (W). Fish initially weighing 301.6±0.1 g on average were fed 14 different diets for 10 weeks. The ideal dietary macronutrient levels were estimated by examining the prediction profiler at the highest desirability based on the variables that were selected to maximize final weight, daily growth coefficient, protein efficiency ratio, nitrogen and energy retentions, and minimize feed conversion ratio, nitrogen and carbon losses.
Main results: The optimal diet formulation yielded the highest desirability of 0.87 for all selected responses and resulted in dietary inclusion levels of FM, W and FO as 63.6%, 20.8%, and 9.4%, respectively. The proposed optimal nutrient concentrations for large turbot (growing from 300 to 500 g) are 54% protein, approximately 17% lipid, and 15.8% carbohydrate on dry matter basis.
Research highlights: The mixture design successfully allowed us to estimate the optimum levels of dietary protein, lipid and carbohydrate for large turbot.
 
Detección de plantas de azafrán infestadas por ácaros mediante imágenes aéreas y un clasificador de aprendizaje automático
No full textAim of study: To evaluate and develop a machine learning code that uses aerial images in visible and near infrared (NIR) spectra to detect mite-infested Saffron (Crocus sativus L.) plants through processing the spectral indices to classify healthy and diseased plants. This leads to the identification of the concentration points of the bulb mites and the estimation of the percentage of infestation in the field.
Area of study: Khorasan-Razavi province, Torbat-Heydarieh, Iran.
Material and methods: Five fields were randomly selected and their red-green-blue (RGB), as a typical visible spectral image, and NIR images were taken in two consecutive years. Seven spectral vegetation indices for NIR images including NIR-band, Red-band, normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), difference red-nir ratio (DRN) and infrared percentage vegetation index (IPVI); and twelve indices for RGB images inlcuding red-band, green-band, blue-band, visible-band difference vegetation index (VDVI), visible atmospheric resistant index (VARI), triangular greenness index (TGI), normalized difference greenness index (NDGI), normalized green blue difference index (NGBDI), modified green red vegetation index (MGRVI), red green blue vegetation index (RGBVI), vegetative index (VEG) and excess of green index (EXG), were extracted and analysed. In order to detect affected plants, two support vector machine (SVM) classifiers with radial basis function (RBF) kernels were used separately for NIR and RGB images.
Main results: The average accuracy of the SVM classifier models were estimated to be 82.3% for NIR images and 91.4% for RGB images during the test phase. Also, the accuracy of the developed models when evaluated in the field with respect to the confusion matrix method was 75.6% and 80.3% for the classification models for NIR and RGB images, respectively.
Research highlights: RGB images were able to distinguish infested plants with better accuracy. Processing aerial images of lightweight drones could speed up the inspection of vast saffron fields.Objetivo del estudio: Evaluar y desarrollar un código de aprendizaje automático que utilice imágenes aéreas en los espectros visible e infrarrojo cercano (NIR) para detectar plantas de azafrán (Crocus sativus L.) infestadas por ácaros mediante el procesamiento de índices espectrales para clasificar plantas sanas y enfermas. Esto permite identificar los puntos de concentración de los ácaros del bulbo y estimar el porcentaje de infestación en el campo.
Área de estudio: Provincia de Jorasán-Razaví, Torbat-Heydarieh, Irán.
Materiales y métodos: Cinco campos fueron seleccionados al azar, y se tomaron sus imágenes en rojo-verde-azul (RGB), como una imagen espectral visible típica, e imágenes en infrarrojo cercano (NIR) en dos años consecutivos. Se extrajeron y analizaron siete índices de vegetación espectrales para las imágenes NIR, que incluyeron NIR-band, redband, normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), difference Red-NIR ratio (DRN) and infrared percentage vegetation index (IPVI); y doce índices para las imágenes visibles RGB, que incluyeron red-band, green-band, blue-band, visible-band difference vegetation index (VDVI), visible atmospheric resistant index (VARI), triangular greenness index (TGI), normalized difference greenness index (NDGI), normalized green blue difference index (NGBDI), modified green red vegetation index (MGRVI), red green blue vegetation index (RGBVI), vegetative index (VEG) and excess of green index (EXG). Para detectar las plantas afectadas, se utilizaron dos clasificadores de Máquinas de Soporte Vectorial (SVM) con núcleos de Función de Base Radial (RBF) de forma separada para las imágenes NIR y RGB.
Resultados principales: La precisión promedio de los modelos clasificadores SVM se estimó en un 82.3% para las imágenes NIR y un 91.4% para las imágenes visibles durante la fase de prueba. Además, la precisión de los modelos desarrollados al ser evaluados en campo con respecto al método de matriz de confusión fue del 75.6% y 80.3% para los modelos de clasificación de imágenes NIR y RGB, respectivamente.
Aspectos destacados de la investigación: Las imágenes RGB lograron distinguir plantas infestadas con mejor precisión. El procesamiento de imágenes aéreas de drones de bajo peso podría acelerar la inspección de grandes campos de azafrán
Sustainability indicators for farming systems in Pampa biome of Brazil: a methodological approach NEXUS-MESMIS
No full textAim of study: To develop and measure sustainability indicators for the water-food-energy nexus in the Ibirapuitã river basin production systems in the Brazilian Pampa biome. The research seeks to contribute to the area of agriculture and sustainability along two lines: a) develop a methodology of sustainability indicators that can be applied to farming systems globally; and b) increase understanding of the interrelationship between water, food and energy and how it affects rural areas' sustainability.
Area of study: The study was conducted in the Ibirapuitã river basin in the Brazilian Pampa biome.
Material and methods: The construction of the indicators was based on the MESMIS methodology (Framework for the Evaluation of Management Systems incorporating Sustainability Indicators). In research, 121 farming systems were sampled. The sustainability indexes of the indicators between and within each dimension were analyzed using analysis of variance (ANOVA) and Tukey's test.
Main results: A significant difference was found between the averages of the indices of the dimensions in the production systems of the basin (p<0.05). The water dimension presented the highest level of sustainability, classified as "ideal". The energy dimension presented an intermediate level of sustainability, classified as "acceptable”. Furthermore, the food dimension presented the lowest sustainability index among the nexus, classified as "alert". These indexes contribute to identifying the main action points for improving the systems, being an essential tool for local rural extension.
Research highlights: The study consolidated a methodology for measuring sustainability indicators based on farming systems' water, energy, and food production characteristics, capable of being replicated in other realities
Experts’ opinion on the sustainable use of nematicides in Mediterranean intensive horticulture
No full textAim of study: Root-knot nematodes are considered a common limiting factor to reaching premium quality and economically viable yields in horticultural crops. Soil disinfestation with agrochemical fumigants has been the main nematode control method until their recent ban due to environmental and social concerns. This paper explores farmers and agricultural advisors’ opinion and preferences on the sustainable use of available nematode control methods, considering sustainability as an integration of nematicidal effectiveness, reduction of environmental harmful effects and preservation of human health.
Area of study: This study has been carried out between farm advisors of intensive horticultural crop areas in Southern Spain.
Material and methods: Farm advisors’ opinion and preferences on the use of nematicides was evaluated following an opinion survey and the Analytic Hierarchy Process (AHP) method. The analysis done was exploratory.
Main results: Providing that current available control methods give enough nematicidal effectiveness to get a profitable yield, the group of farm advisors showed a great consciousness on the use of sustainable alternatives for nematode control in intensive horticultural crops, prioritizing biosolarization as the first option, followed by biopesticides and fumigant nematicides in third place. The use of ozone and non-fumigant nematicides with high toxicity profiles were considered the last options, but new generation nematicides with lower ecotoxicity profiles are also considered as an important tool in sustainable nematode management.
Research highlights: These results provide a prediction of farmers' responses to the sustainable use of nematicides promoted by the European Union when agrochemical fumigants are banned
Use of B–mode and Power Doppler ultrasonography of the uterus and preovulatory follicle to predict ovulation time in Holstein cows after heat synchronization
No full textAim of study: To evaluate the utility of B-mode and Power Doppler ultrasonography to predict ovulation time in Holstein cows by assessment of uterine and follicle measurements.
Area of study: Galicia, NW Spain
Material and methods: 33 Holstein cows were examined every 12 h until ovulation. Measurements for the ratio endometrium/myometrium (END/MYO), uterine lumen (UL), diameter of the dominant follicle (DF), and Power Doppler of the dominant follicle and corpus luteum were recorded. The times of onset of heat, maximum heat (MHA) and heat finalization were obtained from the database of monitoring devices. Blood samples were taken at each examination for progesterone (P4) determination. Data were analyzed using one-way ANOVA and Pearson’s χ2 tests.
Main results: For UL, time -6 (1.53 mm) with respect to ovulation (time 0) significantly differed from time -42 (5.70 mm). Concerning DF, significant differences were observed between time -6 (20.48 mm) and time -54 (16.60 mm). As for P4, significant differences were found between time -6 (0.34 ng/mL) and time -54 (1.03 ng/mL). Considering MHA, significant differences were observed for the UL between after and before/during groups; for DF, significant differences were found before and after MHA. As for heat, the UL significantly differed between after and before/during groups. Significant differences were found for the percentage of cows with Doppler signal in the ovulatory follicle and corpus luteum concerning MHA and heat factors.
Research highlights: The use of Power Doppler to predict ovulation time needs to be refined. The END/MYO and UL measurements could be useful to identify cows in heat, but inaccurate to determine ovulation
The effect of corn grain micronization on diet digestibility and blood biochemical parameters in weaned Holstein calves
No full textAim of study: To evaluate corn grain micronization for calves fed a grower diet.
Area of study: Padinska Skela – Belgrade, Serbia.
Material and methods: Thirty weaned Holstein dairy calves (65–74 days of age) were randomly assigned to one of two treatments with growers containing micronized (MCG) or untreated corn grain (UCG). The experimental period lasted for 60 days.
Main results: The values of total tract apparent digestibility of dry matter (DM), organic matter (OM), crude protein (CP), and nonfiber carbohydrates (NFC) were higher for calves fed MCG versus those within the UCG treatment by 3.9% (p<0.05), 7.0% (p<0.01), 7.1% (p<0.01) and 7.5% (p<0.05), respectively, for the days 25–30 of the experimental period. In addition, the values of digestibility of OM, CP, and NFC were higher by 4.9% (p<0.05), 5.7% (p<0.05), and 6.0% (p<0.05), respectively, for the days 55–60 of the experimental period. The density of metabolizable energy, net energy for maintenance and gain in consumed dietary DM was higher (p<0.001) by 4.7, 5.5, and 7.2%, respectively for calves fed on the grower containing micronized corn grain (MCG), during the first digestibility period, and by 3.0, 3.6, and 4.6%, respectively, during the second digestibility period. Energy intake was lower (p<0.05) during the second digestibility period, for calves fed a diet with micronized corn. Blood urea N was affected (p<0.001) by dietary treatments. Lower values (10.2%) were observed for calves fed the grower containing MCG.
Research highlights: The micronization of corn grain is a useful tool for optimizing weaned calf production due to the improvement in the digestibility and energy content of the ration
Prediction of growth performance parameters in the growing and free-range finishing phases of the Iberian pig via meta-analysis
No full textAim of study: To describe and predict mathematically the growth parameters of Iberian pigs.
Area of study: Iberian dehesa agroforestry system. Southwest of Iberian Peninsula.
Material and methods: A quantitative and systematic review was carried out to find all studies with valid data of growth and finishing in the Iberian swine breed published up to May 2020. For the analysis of the data, a mathematical fitting model was obtained and a function was postulated to describe the relation between the variables age and body weight.
Main results: 112 publications were found, and after applying several quality filters, 18 with age and weight matched data were used. The database was composed of 76 different tests and 22,558 animals. The clasical growth phases were independently evaluated for data analysis.It was necessary to separate the finishing trials into three groups according to the starting age. Seven mathematical models were obtained for lactation,post-weaning, and montanera finishing. However, no valid test data were found during the growth and prefinishing phases. Besides that, a single model was obtained combining lactation and post-weaning, and another surface model including the variables age and weight to compare average daily weight gain in montanera finishing phase.
Research highlights: After systematic review of the studies that provide information on the growth of Iberian pigs, and a quantitative analysis, some mathematical linear and nonlinear models have been developed for the prediction of the production ratios at different phases
Development of an online Nigella sativa inspection system equipped with machine vision technology and artificial neural networks
No full textAim of study: Nigella sativa L. seeds usually are mixed with impurities, which affect its quality and influences consumer acceptance in both raw seeds and the oil market. In this study, an intelligent system based on the combination of machine vision (MV) and artificial neural networks (ANN) was developed to classify and clean N. sativa seeds and its impurities.
Area of study: Iran, Kurdistan province.
Material and methods: For accurate detections we developed a robust image processing algorithm including image acquisition, image enhancement, segmentation, and feature extraction steps. Correlation-based Feature Selection method was used to select the superior features. Three methods of linear discriminant analysis, support vector machines, and ANN were used to classify the data.
Main results: The statistical indices of sensitivity, specificity, and accuracy for N. sativa in the online phase were 90%, 98.93%, and 97.04%, respectively. The average of these measurements for the impurities class was 95.57%, 96.89%, and 96.58%, respectively.
Research highlights: The results demonstrated the feasibility of suggested machine learning and image processing approaches in the real-time cleaning of N. sativa. The image acquisition and processing process, including selection of the best lighting methods to reduce the shadows, noise elimination and segmentation, provided precise results. The final results indicated the effectiveness of proposed machine learning algorithm in feature extraction, feature dimensionality reduction, and classification approaches. This methodology can be recommended for detection, classification and automatic cleaning of other similar seeds
Factors that affect profitability in the Spanish pig farming industry
No full textAim of study: To identify factors that boost the financial profits of pig producers. These factors refer to the company, the industry and the territory where they are located. We also incorporated an environmental factor according to greenhouse gas emissions.
Area of study: Spain.
Material and methods: The data used came from a sample of 1,810 Spanish entities that provided unbalanced panel data for the 2003-2018 period.
Main results: In recent decades, the pig farming industry has undergone considerable development characterised by an increase in production, exports and in the productivity of pig farms. The study enabled us to detect the factors that most influence the profitability of pig producers, bearing in mind the possible existence of endogeneity problems between some of the variables analysed.
Research highlights: The results obtained have practical implications, insofar as they facilitate decision-making as regards the location and characteristics that farms must possess in order to obtain competitive profitability