Last generation instrument for agriculture multispectral data collection

In recent years, the acquisition and analysis of multispectral data are gaining a growing interest and importance in agriculture. On the other hand, new technologies are opening up for the possibility of developing and implementing sensors with relatively small size and featuring high technical performances. Thanks to low weights and high signal to noise ratios, such sensors can be transported by different type of means (terrestrial as well as aerial vehicles), giving new opportunities for assessment and monitoring of several crops at different growing stages or health conditions. The choice and specialization of individual bands within the electromagnetic spectrum ranging from the ultraviolet to the infrared, plays a fundamental role in the definition of the so-called vegetation indices (eg. NDVI, GNDVI, SAVI, and dozens of others), posing new questions and challenges in their effective implementation. The present paper firstly discusses the needs of low-distance based sensors for indices calculation, then focuses on development of a new multispectral instrument specially developed for agricultural multispectral analysis. Such instrument features high frequency and high resolution imaging through nine different sensors (1 RGB and 8 monochromes with relative band-pass filters, covering the 390 to 950 nm range). The instrument allows synchronized multiband imaging thanks to integrated global shutter technology, with a frame rate up to 5 Hz; exposure time can be as low as 1/5000 s. An applicative case study is eventually reported on an area featuring different materials (organic and non-organic), to show the new instrument potential. Last generation instrument for agriculture multispectral data collection. Available from: https://www.researchgate.net/publication/317596952_Last_generation_instrument_for_agriculture_multispectral_data_collection [accessed Jul 11, 2017]

Modelling of Harvesting Machines’ Technical Parameters and Prices

Technical and performance parameters of agricultural machines directly impact the operational efficiency and entire crop production. Sometimes, overestimation of technical and dimensional parameters of harvesting equipment is carried out with the intention of enhancing the operational efficiency, but this approach might turn out to negatively impact productivity due to unbalanced system design, and ultimately lead to financial losses. Therefore, a balanced preliminary estimation of technical parameters of equipment needs to be carried out before investment quantification, especially on the large capital-intensive machinery units, such as harvesting systems. In addition, availability of ready to use, simplified models for the price estimation from input technical parameters would reduce the complexity involved in this latter analysis. The current study is an attempt to provide tools to address these issues. A large dataset of combine and forage harvesters has been analyzed to investigate relevant parameter-to-parameter and parameter-to-price relations. The study of the available data allowed the determination of indicative models for the estimation of machine price, power, weight, tank capacity and working width. A significant correlation between power and price (R2 > 0.8) has been observed for two groups of harvesting machines. For combine harvesters, satisfactory correlations were found between power and weight, and power and tank capacity. A regression model for combine harvesters showed a satisfactory behavior at predicting the average working width that can be operated by a given power. On the other hand, for the forage harvesting group, the relation between these quantities has lower values; therefore, for better accuracy of the association, more sophisticated considerations should be incorporated, taking into account other parameters

Characterization of vine canopy through two dimensional imaging

In the last decade, reconstruction of plant dimensions and volume has gained a noticeable importance, in particular for the possibility of collecting data correlated to biomass, leaves area, etc. This is specifically of interest in the case of vineyards, where knowledge of variability can be not only a useful mean to evaluate the health condition of the vines and of the grapes, but also an important input to allow variable management practices. Different sensing technologies are available for shape reconstruction, as for instance ultrasonic sensors, laser scanners or depth cameras, however, their practical application is still limited by low quality information, high costs, or high speed data processing demand. For the present work two-dimensional imaging is proposed as a viable solution for shape reconstruction of canopies. The method takes advantage of low cost 2D commercial cameras, which can be installed on board the tractor allowing on the go collection of images of plants from the bottom. The paper describes the instrumentation set up and integration with a specific thresholding algorithm allowing segmentation of canopy profile. Tests were carried out on 20 different dates on a glera vineyard in an experimental farm in the North of Italy. The results were correlated with the number of leaves, leaf area index and canopy volume. High correlation was identified in the case of volume with coefficients of determination R\ub2 > 0.7 in most of the cases

Analysis of cost and performances of agricultural machinery: Reference model for sprayers

Management of agricultural operations is currently rapidly changing caused by increased attention to the concepts of sustainable development and sustainable intensification. Enhancement of productivity and efficiency of agricultural machinery are the leading factors in sustainable agriculture. The complete application and exploitation of engineering advances require the revision of traditional agricultural machinery management process. The definition of the farm fleet (tractors and implements), as well as machinery planning and management, must consider different parameters, including not only the cost of the machines but also their dimensions, weight, working width, needed power, etc. All of this information related to an agricultural machine is eventually influencing the impact on productivity, on the return on the investment, and also on the environment. The present work is aimed at identifying the most relevant parameters which are influencing costs and performances of sprayers, including tank volume, maximum flow, needed power, weight and price. The different parameters are analysed in a correlation matrix, in order to allow identification of dependencies and to extract reference models. The study is based on linear and multiple linear regression analysis carried out on technical specifications of about 700 models of sprayers. Relevant correlations were highlighted between price and weight, between weight and tank capacity and in some cases between power and weight. Following such correlations, models have been proposed, which can be implemented in order to support the decision making phases

Definition of Reference Models for Power, Weight, Working Width, and Price for Seeding Machines

Machine functional parameters define fleet composition and management and, thus, play an important role in economic and environmental performance. Large availability of programming methods and decision support systems are available in the market, however, there is still a lack of applicative tools to forecast the perceived and necessary technical parameters and machinery price options to complete tasks. In the current research, most correlated functional parameters for four group of seeding machines were determined with the application of linear and multiple linear regression analyses. Power, weight, working width, number of rows, and list price were studied, and reference equations were developed for seed drills, precision, combined and no-tillage planters. Two statistical analyses models were, therefore, developed for each of the groups in order to allow evaluation and prediction of performance and cost, thus contributing to the selection process optimisation and perceived choice of the needed implement

Definition of Reference Models for Power, Weight, Working Width, and Price for Seeding Machines

Machine functional parameters define fleet composition and management and, thus, play an important role in economic and environmental performance. Large availability of programming methods and decision support systems are available in the market, however, there is still a lack of applicative tools to forecast the perceived and necessary technical parameters and machinery price options to complete tasks. In the current research, most correlated functional parameters for four group of seeding machines were determined with the application of linear and multiple linear regression analyses. Power, weight, working width, number of rows, and list price were studied, and reference equations were developed for seed drills, precision, combined and no-tillage planters. Two statistical analyses models were, therefore, developed for each of the groups in order to allow evaluation and prediction of performance and cost, thus contributing to the selection process optimisation and perceived choice of the needed implement

Definition of Reference Models for Power, Mass, Working Width, and Price for Tillage Implements

Farm machinery selection, operation and management directly impact crop cultivation processes and outputs. A priori quantification of technical and financial needs allows definition of proportionate distribution and management of available resources and simplification of selection process. Appropriate planning, association and adjustment of the power unit and implement are required for soil cultivation. Consideration of functional parameters of the implement, their proper estimation and operation directly impact the soil structure, productivity and return on investment. Thus, a modelling approach was implemented for the definition of possible parameter-price relations for tillage equipment. The performed analysis allowed us to investigate the main relevant parameters, quantify their impact, and elaborate forecasting models for price, power, mass and working width. The significant relevance of the technical parameters and adjustment issues were outlined for each tillage implement group. For harrows and cultivators, the dependencies between studied parameters expressed better predictive qualities, especially for price-mass relation (R² > 0.8). While for ploughs power and mass relation had a primary output (R² = 0.7). The prediction features of the models provided reliable results for the estimation of the indicative values of the price and parameters of the implements

Definition of Reference Models for Power, Mass, Working Width, and Price for Tillage Implements

Farm machinery selection, operation and management directly impact crop cultivation processes and outputs. A priori quantification of technical and financial needs allows definition of proportionate distribution and management of available resources and simplification of selection process. Appropriate planning, association and adjustment of the power unit and implement are required for soil cultivation. Consideration of functional parameters of the implement, their proper estimation and operation directly impact the soil structure, productivity and return on investment. Thus, a modelling approach was implemented for the definition of possible parameter-price relations for tillage equipment. The performed analysis allowed us to investigate the main relevant parameters, quantify their impact, and elaborate forecasting models for price, power, mass and working width. The significant relevance of the technical parameters and adjustment issues were outlined for each tillage implement group. For harrows and cultivators, the dependencies between studied parameters expressed better predictive qualities, especially for price-mass relation (R² > 0.8). While for ploughs power and mass relation had a primary output (R² = 0.7). The prediction features of the models provided reliable results for the estimation of the indicative values of the price and parameters of the implements