Agricultural Structures and Mechanization

In our globalized world, the need to produce quality and safe food has increased exponentially in recent decades to meet the growing demands of the world population. This expectation is being met by acting at multiple levels, but mainly through the introduction of new technologies in the agricultural and agri-food sectors. In this context, agricultural, livestock, agro-industrial buildings, and agrarian infrastructure are being built on the basis of a sophisticated design that integrates environmental, landscape, and occupational safety, new construction materials, new facilities, and mechanization with state-of-the-art automatic systems, using calculation models and computer programs. It is necessary to promote research and dissemination of results in the field of mechanization and agricultural structures, specifically with regard to farm building and rural landscape, land and water use and environment, power and machinery, information systems and precision farming, processing and post-harvest technology and logistics, energy and non-food production technology, systems engineering and management, and fruit and vegetable cultivation systems. This Special Issue focuses on the role that mechanization and agricultural structures play in the production of high-quality food and continuously over time. For this reason, it publishes highly interdisciplinary quality studies from disparate research fields including agriculture, engineering design, calculation and modeling, landscaping, environmentalism, and even ergonomics and occupational risk prevention

Precision Agriculture for Crop and Livestock Farming—Brief Review

In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.info:eu-repo/semantics/publishedVersio

An approach to compensation of dust effects on seed flow sensors

Optical seed mass flow sensors are widely used on seed drills and planters. An important challenge in these sensors is their malfunction in a dusty condition. Dust caused by soil and seeds may sit on the light elements and disrupt its function. In this study, an approach was developed to compensate this effect. A non-contact intelligent system with infrared diodes and a microcontroller with ARM architecture was built up to monitor the seed flow in the delivery tube of seed drills. At the hardware phase, a glass with a different radius of curvature was installed in front of the elements. The semi-cylindrical glass placement in front of the optical elements meant that the arrangement was sealed against dust. Besides, the fall of the seeds tangential to the glass during the sowing caused the glass to self-clean. However, the hardware configuration of the seed flow sensor with semi-cylindrical glass alone was not sufficient under adverse dusty conditions. A suitable algorithm was therefore developed and applied to compensate for the dust effect. In this case, instead of the level of output voltage, MS (mean of variances) of sensor outputs was calculated. The mass flow estimation model was obtained using multiple regression between the MS index of the seed flow sensor and digital scale data. Experiments were carried out using different types of seeds in several repetitions. In all tests, the correlation coefficient of the mass flow estimation model was obtained above 0.9. The results revealed that this system works correctly and precisely in dusty field conditions without having to clean the sensing elements

Effect of drill machine operating speed on quality of sowing and biomass yield

ArticleThe paper is focused on the study and evaluation of quality of the seeding of seeds and its effect on the biomass yield. The aim was to evaluate the space arrangement of the seeds by using of polygon method on one field with the repetition for different forward speeds of the drill machine. For the evaluation there were used digital photographs, which were taken during repeated measurements of the each value of the forward speed after sprouting of crop. These images have been used in order to determine the shape and size of the surface area belonging to the plant. Own software TfPolyM was used for the image analysis. The shape of the polygons belonging to the individual plants was expressed by values of the shape factor Tf. This factor characterises the suitability the shape of polygon surface related to the individual plant. By comparing of the values of the shape factors for different forward speeds of the drill machine we can determine the optimal value of the forward speed from the point of seed placement uniformity in horizontal level. During harvest of the crop there was analysed the variability of the biomass yield in relation to values of the forward speed used during seeding. The most suitable values of shape factor Tf (0.8519) was recorded for speed of drill machine set on 12 km h -1 . For other tested speeds 8, 10, 15 km h -1 were recorded lower values of shape factor 0.7994, 0.8173 and 0.8449, respectively. In determination of biomass production for drill machine speed 12 km h -1 the greatest yield from 1 m2 was observed. Subsequently, for speeds 8 and 10 km h -1 was lower about 4.26% and 1.83%, respectively. For tested speeds of drill machinery 15 km h -1 and above was observed only a small descent of yields about 0.6%. Fluctuation in yields affected by working speed then demonstrates fluctuation in sowing rate. It was also observed that the working speed of sowing machinery also affect the amount of yield directly. However, in case of lowest yield of straw recorded it was observed even 20% decrease in yield of grains

Task-based agricultural mobile robots in arable farming: A review

In agriculture (in the context of this paper, the terms “agriculture” and “farming” refer to only the farming of crops and exclude the farming of animals), smart farming and automated agricultural technology have emerged as promising methodologies for increasing the crop productivity without sacrificing produce quality. The emergence of various robotics technologies has facilitated the application of these techniques in agricultural processes. However, incorporating this technology in farms has proven to be challenging because of the large variations in shape, size, rate and type of growth, type of produce, and environmental requirements for different types of crops. Agricultural processes are chains of systematic, repetitive, and time-dependent tasks. However, some agricultural processes differ based on the type of farming, namely permanent crop farming and arable farming. Permanent crop farming includes permanent crops or woody plants such as orchards and vineyards whereas arable farming includes temporary crops such as wheat and rice. Major operations in open arable farming include tilling, soil analysis, seeding, transplanting, crop scouting, pest control, weed removal and harvesting where robots can assist in performing all of these tasks. Each specific operation requires axillary devices and sensors with specific functions. This article reviews the latest advances in the application of mobile robots in these agricultural operations for open arable farming and provide an overview of the systems and techniques that are used. This article also discusses various challenges for future improvements in using reliable mobile robots for arable farmin

Monitoring Nitrogen Levels in the Cotton Canopy using Real-Time Active-Illumination Spectral Sensing

Managing nitrogen (N) fertilizer is fundamental to efficient cotton production. Traditional N management strategies often utilize N inefficiently through sub-optimal rate prescriptions and inappropriately timed applications. This leads to reduced production efficiency and increased environmental risk. Both deficiency and excess of N in cotton crop negatively affects lint yield and fiber quality. Thus, the aim is to monitor in-season cotton N levels in real-time at a growth stage where supplemental N can be applied. Research has shown high correlation of cotton leaf N concentrations with spectral reflectance of plants. The GreenSeeker® sensor is a ground-based active-light sensor developed to nondestructively evaluate N status in crops. However, the Normalized Difference Vegetation Index (NDVI) reported by the sensor is subject to influence by the soil background. The objective of this research was to develop an algorithm that improves a ground-based sensing system’s ability to discriminate between plant biomass and soil, allowing it to better estimate N status in cotton. Three cotton varieties, three seeding rates, and four N rates were established in a field experiment in Milan, TN. GreenSeeker readings and ultrasonic plant height data were collected and analyzed to investigate the influence of these crop management factors on NDVI. Strong positive correlation (r\u3e0.72) between NDVI and plant height was confirmed. Seeding rate affected NDVI throughout the season, confirming an effect of soil background noise on NDVI values. To aid in algorithm creation, NDVI data were collected from a subset of plots, the plant population was thinned, and re-sensed. Difference in NDVI of these populations was minimized when data below a threshold was removed prior to index calculation. Two algorithms were identified that reduced vegetation indices difference to within the published error of the sensor. The reduction of plant population effect on NDVI was validated by post-processing a larger data set using both algorithms

COVER CROPPING: SENSOR-BASED ESTIMATIONS OF BIOMASS YIELD AND NUTRIENT UPTAKE AND ITS IMPACT ON SUGARCANE PRODUCTIVITY

Sugarcane in Louisiana can be harvested for up to three years from one planting. Soil cultivation along sides of established beds is done for weed control and improve fertilizer use efficiency which increases the risk of soil degradation and yield decline. Planting cover crops (CC) is a soil conservation practice and an effective strategy to improve soil health and nutrient recycling. Limited work has been done on remote sensor-based evaluation of the potential nutrient benefits from cover crops and its effect on nutrient cycling on sugarcane systems. This study was conducted to evaluate the effect of two planting methods (broadcast and drilling) and three seeding rates (100%, 50%, and 25% of NRCS recommendation) of a mix of three legumes and two brassicas CC species and a control without CC, on sugarcane yield and quality parameters, and on soil nutrients levels. This study was also used for the acquisition of normalized difference vegetation index (NDVI), collected using GreenSeeker® and multispectral camera (MicaSense® - RedEdge-M) mounted on an unmanned aerial vehicle, to correlate with CC biomass and nutrient uptake. The NDVI readings and CC biomass clippings, using the quadrat frame method, were collected a week before CC termination. Tissue analysis was carried out by C:N dry combustion analyzer and nitric acid digestion-hydrogen peroxide for multi-element analysis. Cane yield was acquired with a chopper harvester and a dump billet wagon. Quality components were obtained by a SpectraCane® automated near infrared (NIR) analyzer for quality parameters. Soil inorganic nitrogen (N) content (NH4+ + NO3-) was quantified using KCl extraction procedure and flow injection analysis. Other soil nutrients content was determined based on Mehlich-3 extraction procedure followed by ICP. A strong positive correlation between the GreenSeeker NDVI (NDVI-GS) and aerial images derived NDVI (NDVI-AI) was obtained with a coefficient of determination (R2) value of 0.63. Adjustment of NDVI with, number of days, cumulative growing degree days, and number of days with positive growing degree days, from planting to sensing increased the R2 values up to 0.76, 0.76 and 0.73, respectively. The NDVI-GS obtain a stronger linear relationship with CC dry biomass and N content than NDVI-AI. Good positive correlations (0.48 \u3e R2 \u3e 0.12) were found between NDVI and some macronutrients (P and K) and micronutrients (Mn and Cu). Overall, there was no significant effect of planting method and seeding rate observed on cane yield and quality parameters. Moreover, there was no statistical difference on CC nutrient removal rate among the treatments (p\u3e0.05). For plant cane, the average cane and sugar yield across sites was 96 Mg ha-1 and 10794 kg ha-1, respectively. Lower yield was attained by the ratoon crops averaging only at 71 Mg ha-1 cane yield and 7197 kg ha-1 sugar yield. Remote sensing is a promising and viable technique to estimate CC biomass and nutrient uptake. Finally, this study corroborates the long-term effect of CC on nutrient management and their effect on cane yield and quality parameters

A partial study of vertical distribution of conventional no-till seeders and spatial variability of seed depth placement of maize in the Alentejo region, Portugal

The requirements for a good stand in a no-till field are the same as those for conventional planting as well as added field and machinery management. Among the various factors that contribute towards producing a successful maize crop, seed depth placement is a key determinant. Although most no-till planters on the market work well under good soil and residue conditions, adjustments and even modifications are frequently needed when working with compacted or wet soils or with heavy residues. The main objective of this study, carried out in 2010, 2011 and 2012, was to evaluate the vertical distribution and spatial variability of seed depth placement in a maize crop under no-till conditions, using precision farming technologies and conventional no-till seeders. The results obtained indicate that the seed depth placement was affected by soil moisture content and forward speed. The seed depth placement was negatively correlated with soil resistance and seeding depth had a significant impact on mean emergence time and the percentage of emerged plants. Shallow average depth values and high coefficients of variation suggest a need for improvements in controlling the seeders’ sowing depth mechanism or more accurate calibration by operators in the field

Control system response for seed placement accuracy on row crop planters

Doctor of PhilosophyDepartment of Biological & Agricultural EngineeringAjay ShardaPlanting is one of the most critical field operations that can highly influence early season vigor, final plant density and ultimately potential crop yield. It is the opportunity to place seeds at a uniform depth and spacing providing them the ideal environment for proper growth and development. However, inherent field spatial variability could influence seed placement and requires proper implementation of planter settings to prevent shallow seeding depth, sidewall compaction and uneven spacing. The overall goal of this research is to evaluate the response of the planter and crop to downforce control system implementation across a wide range of machine and field operating conditions. Planting operations were performed in corn production fields using a Horsch row-crop planter with 12 row units equipped with a hydraulic downforce system capable of implementing fixed and active downforce settings. A custom-made data acquisition system was developed to record sensor data at 10 Hz sampling frequency. From this study, the following conclusions were drawn. First, soil texture and soil compaction due to tractor tires influenced real-time gauge wheel load (GWL). Implementing a fixed downforce setting with target GWL set at 35 kg showed that 25% of the total planting time GWL was less than 0 suggesting areas planted with uncertain seeding depth due to potential loss of ground contact of the gauge wheels. Likewise, fewer row units per section could provide lower variability in GWL indicating the need for an automatic section control to maintain target GWL within an acceptable range for all row units. Second, implementing an active downforce setting showed no significant difference between downforce A (63 kg) and downforce B (100 kg) on plant spacing, although downforce setting B resulted to higher plant spacing accuracy. Higher variability in spacing was observed when ground speed is over 12 kph. To achieve desired seeding depth, downforce greater than 100 kg is needed when ground speed is over 7.2 kph on no-till field and when ground speed is over 12 kph on strip-tilled field. Third, response of row units segregated in sections revealed that row unit acceleration on wing, track and non-track sections increases with speed. Strip-tilled soil exhibited lower row unit acceleration by 18% compared to no-till soil. Finally, a proof-of-concept sensing and measurement (SAM) system was developed to calculate seed spacing, depth and geo-location of corn. This system could provide real-time feedback on seed spacing and depth allowing appropriate downforce control system management for more consistent seed placement during planting. In summary, advances in planter technology paved the way for the addition of more row units across on the planter to increase planting productivity. With increasing width of planter toolbar, each row unit may need different downforce control to varying field and machine operating conditions. Appropriate downforce control management should be implemented to compensate for increased dynamics of planter row units across a highly variable field conditions to achieve the desired seed placement accuracy