Theofanis A. Gemtos

Farm machinery management information system

Graphical abstractDisplay Omitted Outline concerns for the use of information generated and pertaining to the tractor and the farm implements.Specify the pathways for the organization of data generated by the tractor ISOBUS protocol.Facilitate further development of required sensors, communication technology, and information processing capabilities. Management of farming operations is currently rapidly changing toward a systems perspective integrating the surroundings in terms of environmental impact, public entities and documentation of quality and growing conditions. The latest developments in Information and Communication Technologies (ICT) and the prevailing lack of interoperability between agricultural tractors, implements and on-board computers has led to the development of ISO 11783 (ISOBUS) international standard for securing a more effective communication between these entities. Precision agriculture requires an increasing amount of information in order to be sufficiently managed and the abilities of the ISOBUS protocol is a significant step toward this goal as it will provide a wealth of automated data acquisition for improving the management of crop production. However, there is an urgent need to organize and specify the pathways of this large amount of information as prerequisites for subsequently turning it into knowledge and decision support. The aim of this study was to analyze and design a future farm machinery management information system to handle tractor and implement data together with the interactions with their surroundings. Soft systems methodology was used to analyze the human activities and to identify user requirements in relation to the use of farm machinery and the management of the information generated and pertaining to the tractor and the farm implements. The empirical data to extract this information was gathered from 30 targeted interviews with tractor operators and farm managers located in Greece and Denmark, and pertaining to questions about the optimal use of farm machinery data and tractor-implement performance. A rich picture of the whole system was developed and from that a conceptual model that infers to daily operations with the tractor, implement and the interactions with the surroundings. The conceptual models were developed for both conventional farm machinery and agricultural robots. The conceptual model function will serve as a blueprint for further development of required sensors, communication technology, and information processing capabilities. The developed conceptual models were tested and validated with 15 farm managers from the initial reviewing panel in order to reveal supplemental additions and concerns.

Soft Computing Technique of Fuzzy Cognitive Maps to Connect Yield Defining Parameters with Yield in Cotton Crop Production in Central Greece as a Basis for a Decision Support System for Precision Agriculture Application

This work investigates the yield and yield variability prediction in cotton crop. Cotton crop management is a complex process with interacting parameters like soil, crop and weather factors. The soft computing technique of fuzzy cognitive maps (FCMs) was used for modeling and representing experts’ knowledge. FCM, as a fusion of fuzzy logic and cognitive map theories, is capable of dealing with uncertain descriptions like human reasoning. It is a challenging approach for decision making especially in complex environments. The yield management in cotton production is a complex process with sufficient interacting parameters and FCMs are suitable for this kind of problem. The developed FCM model consists of nodes that represent the main factors affecting cotton production linked by directed edges that show the cause-effect relationships between factors and cotton yield. Furthermore, weather factors and conditions were taken into consideration in this approach by categorizing springs as dry–wet and warm-cool. The methodology was evaluated for approximately 360 cases measured over 2001, 2003 and 2006 in a 5 ha cotton field. The results were compared with some benchmarking machine learning algorithms, which were tested for the same data set, with encouraging results. The main advantage of FCM is the simple structure and the easy handling of complex data.

Management information system for spatial analysis of tractor- implement draft forces

With the recent increases in fuel costs, producers are searching for ways to minimize costs and to increase productivity. The ability to monitor and collect data about tractor/implement performance can benefit farm machinery management decisions and lead to fuel savings. The draft force required to pull an implement is of great importance, since it determines fuel consumption and the tractor power required. The aim of this study was to design and develop a Farm Management Information System (FMIS) to handle draft forces data and generate spatial performance maps. This analysis could be useful to analyze tillage operations in order to maintain soil quality and reduce energy consumption.

Wireless Sensor Network for Precision Agriculture

Irregular or insufficient rainfall can be a serious limitation to the final yield, causing low yields and even crop failure. Moreover the total amount of water is assessed during the growing stages by the plants¢ characteristics. It is possible, however, to find out how much soil moisture by using sensors This can help farmers in decision making Furthermore the combination of soil moisture sensors with management zones may lead to increase of the final yield. The aim of this research was to calibrate and install the WATERMARK sensors in a commercial vineyard according to the Precision Agriculture practices. The results of the research proved that the WATERMARK sensors can assess the soil moisture with high accuracy (R2 = 0.85).

The Influence of Cotton Variety in the Calibration Factor of a Cotton Yield Monitor

A three years study on precision fanning in cotton in central Greece showed serious infield variability of yield and soil physical and chemical properties. Yield mapping constitutes the starting and ending point of the whole process chain in a precision farming system. Yield mapping was performed for three consecutive years in the same cotton fields of central Greece in a wide range of cotton varieties and field conditions with a yield monitor installed on a cotton picker. The yield sensors estimate the flow volume of cotton conveyed from the picking units in the basket through air ducts. The main control unit performs the transformation of the estimated cotton volume in cotton weight using a factor named Calibration Coefficient or Factor. Every time the picking conditions change, like going to a different field or change cotton variety, there is the need to pick a whole load basket and make an actual weighing to inform the main unit to adapt the calibration factor to the correct value. After three cotton yield mapping seasons (overall 200ha) it was observed that there is a straight relation of the value of calibration factor with the cotton variety. The present study shows these results that group the values of calibration factor with corresponding cotton varieties. The results would help in the calibration of the monitors.

Using laser scanner to map pruning wood in vineyards

Management zones in vineyards may be delineated by examining the spatial variability of various biophysical factors related to grapevine performance, such as vegetation indices. Among measurements of vine vigour, the counting and weighing of winter dormant canes at pruning is the most informative to indicate vine balance and is commonly performed manually by grape growers for management purposes. Therefore the mapping of dormant canes in winter could provide an alternative assessment of vine vigour within precision viticulture studies. Recently, laser scanners have been used to evaluate the geometry of tree canopies. In the present study, the potential of using laser scanner technology as an automated, easy and rapid way to perform mapping of the pruning wood across the vineyard was investigated. The results suggest that laser scanners offer great promise to characterize within-field variability in vine performance.

Application of variable rate fertilizer in a commercial apple orchard

The spatial variability of yield in an apple orchard is high due to the inherent soil variability and the impact of the environment on the trees. One practice of precision agriculture is the variable rate application (VRA) of inputs, which gives farmers the opportunity to manage field variability. The objective of the present study was to demonstrate how the use of variable rate fertilization in an apple orchard can change the farmer’s profit. For every other row, in a commercial apple orchard in central Greece, VRA was applied while the remaining rows were used as reference and received a uniform rate similar to the rate typically used by the farmer. The VRA rates of nitrogen were based on the literature which suggests that for every t/ha of apple yield, 2.45 kg/ha of N are removed from the soil in one growing season. The comparison of the results between VRA and reference treatments showed that the amount of fertilizer used in VRA treatments was reduced by 32.4% while the farmer’s profit increased by 21%. More years of research are required to give more reliable results.

The use of a laser scanner for measuring crop properties in three different crops in Central Greece

A sensing platform using a laser scanner was developed for data collection for the crop in the field. Crop height and biomass from three crops (sunflower, soybeans and winter wheat) were estimated. The sensing platform was mounted on a tractor, ran along the crop and measured the crop height and biomass. The relevant crop parameters were measured manually and compared to the values estimated based on instrument measurements. High correlation coefficients were generally observed and varied according to the growth stage of the crop. The correlation was both high for estimating crop height (r2=0.94 to r2=0.90) and for biomass mass (r2=0.879 to r2=0.83).

Development and Evaluation of a Fuzzy Inference System and a Neuro-Fuzzy Inference System for Grading Apple Quality

ABSTRACT In this research work, a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) were developed to classify apple total quality based on some fruit quality properties, i.e., fruit mass, flesh firmness, soluble solids content and skin color. The knowledge from experts was used to construct the FIS in order to be able to efficiently categorize the total quality. The historical data was used to construct an ANFIS model, which uses rules extracted from data to classify the apple total quality. The innovative points of this work are (i) a clear presentation of fruit quality after aggregating four quality parameters by developing a FIS, which is based on experts’ knowledge and next an ANFIS based on data, and (ii) the classification of apples based on the above quality parameters. The quality of apples was graded in five categories: excellent, good, medium, poor and very poor. The apples were also graded by agricultural experts. The FIS model was evaluated at the same orchard for data of three subsequent years (2005, 2006 and 2007) and it showed 83.54%, 92.73% and 96.36% respective average agreements with the results from the human expert, whereas the ANFIS provided a lower accuracy on prediction. The evaluation showed the superiority of the proposed expert-based approach using fuzzy sets and fuzzy logic.

Robotics and Sustainability in Soil Engineering

This chapter discusses sustainability issues in soil engineering and presents the potential of the use of autonomous tractors in agriculture. While farm machinery is becoming bigger for economies of scale, it causes significant problems in soil erosion and soil compaction. The use of smaller intelligent machines could reduce these problems and the specification requirements for these systems are presented.