Precision Agriculture Farming Research Articles

A comparison between two specialized dairy cattle farms in the upper Po Valley. Precision agriculture as a strategy to improve sustainability

The current tools that allow the application of precision agriculture in livestock systems are undergoing growing diffusion. Given the significant share of anthropogenic greenhouse gas emissions attributable to the livestock sector, this study investigates the potential contribution of precision agriculture technologies to animal farming sustainability, particularly when considering alternative energy sources.The research compared two intensive dairy farms in the upper Po Valley (conventional farm - CF - and precision agriculture farm - PAF) using the life cycle assessment approach, considering as declared unit 1 kg of fat protein corrected milk, and aiming to (i) assess the main environmental impact categories for precision and conventional farming; (ii) compare different allocation modes (reference, mass, economic). In the reference mode all the burdens and all the environmental impacts of the farming system are allocated to the main product, which is the milk. The chosen impact categories were: global warming potential, acidification and eutrophication potentials, non-renewable energy, agricultural land occupation and fossil depletion.Based on the results, in the case of reference allocation, PAF impacts are lower than those from CF for all the impact categories except for global warming potential. When considering mass and economic allocations, the impacts of PAF and CF show the same behaviour for the various impact categories; however, the calculated equivalents place PAF at an increased sustainability level than CF.Therefore, precision agriculture technologies in animal farming are viable cues for the sustainability of the zootechnical sector and are worth being deepened in future studies focusing on the outcomes of zootechnical automation.

Review of Sensor Network-Based Irrigation Systems Using IoT and Remote Sensing

The motivation for this review paper came from the developing countries where the economy is mostly dependent on agriculture and climate conditions. Based on current conditions and historical records, profitability in production farming depends on making a right and timely operational decision. Precision farming is a systematic program designed to maximize the productivity of agriculture by carefully tailoring the soil and crop management to meet the specific requirements in each field while preserving environmental quality. This review paper highlights the development of an automated irrigation system with portable wireless sensor networks and decision support methods to remotely measure the environmental parameters in an agriculture field. Radio satellite, mobile phones, sensors, internet-based communication, and microcontroller capture the ecological parameters such as soil moisture, temperature, humidity, and light intensity. The knowledge gained from the sensors is transferred directly to the cloud server by using IoT technology. Users from anywhere in the world can display them through an internet-enabled device. Development of sensor-based application in modern agriculture makes it cost-effective and potentially productive and increases the efficiency through precision agriculture farming. Different limitations have been reported in the previously reviewed publications like the shortage of power in the field that can be solved by using a solar panel that recharges the battery at the same time using electricity. Bluetooth application in the agriculture sector is mainly improved by design system optimization. Problems related to transmission and radio range frequency can be solved by using a power class upgraded antenna.

Weed Detection Using Fractal-Based Low Cost Commodity Hardware Raspberry Pi

Conventional weed control system is usually used by spraying herbicides uniformly throughout the land. Excessive use of herbicides on an ongoing basis can produce chemical waste that is harmful to plants and soil. The application of precision agriculture farming in the detection process in order to control weeds using Computer Vision On Farm becomes interesting, but it still has some problems due to computer size and power consumption. Raspberry Pi is one of the minicomputer with low price and low power consumption. Having computing like a desktop computer with the open source Linux operating system can be used for image processing and weed fractal dimension processing using OpenCV library and C programming. This research results the best fractal computation time when performing the image with dimension size of 128 x 128 pixels. It is about 7 milliseconds. Furthermore, the average speed ratio between personal computer and Raspberry Pi is 0.04 times faster. The use of Raspberry Pi is cost and power consumption efficient compared to personal computer.

Experiment to Test RTK GPS with Satellite “Internet to Tractor” for Precision Agriculture

This experiment tests the ability to provide Real Time Kinematic Global Positioning System corrections via a real time network (RTK GPSRTN) by utilizing a satellite Internet connections in the field to multiple tractors for precision agriculture. Precision agriculture technology improves sustainable production by providing more farmers with the ability to obtain highly accurate machine control in rough or varied terrain. This experiment tests the use of low cost single frequency L1-band RTK GPS in a RTN covering a wide area. Wired Internet is not available in the field for many farmers so cellular internet such as 3G/4G must be relied on for connectivity. Where 3G/4G have limited coverage, satellite Internet could be used. A mobile satellite Internet antenna can be placed on a support vehicle and moved to a site where multiple tractors are harvesting or planting. The Internet is then shared with precision agriculture farming equipment nearby. Having better logistical and fiscal access to machine control and the Internet could assist the farmer and the United States Department of Agriculture in efforts to sustain adequate crop yields, project future agriculture needs, and better protect crops from pests and diseases. This experiment provides information on how to configure “Internet to Tractor” for RTK GPS RTN concept with a discussion of the limitations and future product development.

PA—Precision Agriculture: Dynamic Weighing for Accurate Fertilizer Application and Monitoring

The mass flow of fertilizer spreaders must be calibrated for the different types of fertilizers used. To obtain accurate fertilizer application manual calibration of actual mass flow must be repeated frequently. Automatic calibration is possible by measurement of the actual mass flow, based on dynamic weighing of the spreader. This paper describes the design and test of a dynamic weighing system. Under field conditions, this system was able to provide force due to spreader weight readings that have a standard deviation of 20 N, over a measurement range of 6–20 kN, at a maximum time delay of 1 s.Time-stamped data logging of the spreader weight and the theoretical application rate combined with position information allows determination of the realized spatial application of fertilizer. A comparison can be made between realized and prescribed application rates. This information is necessary for evaluation of fertilizer management in a precision agriculture farming system and to accurately target fertilizer application to crop and soil status.

PA—Precision AgricultureDynamic Weighing for Accurate Fertilizer Application and Monitoring

The mass flow of fertilizer spreaders must be calibrated for the different types of fertilizers used. To obtain accurate fertilizer application manual calibration of actual mass flow must be repeated frequently. Automatic calibration is possible by measurement of the actual mass flow, based on dynamic weighing of the spreader. This paper describes the design and test of a dynamic weighing system. Under field conditions, this system was able to provide force due to spreader weight readings that have a standard deviation of 20 N, over a measurement range of 6–20 kN, at a maximum time delay of 1 s. Time-stamped data logging of the spreader weight and the theoretical application rate combined with position information allows determination of the realized spatial application of fertilizer. A comparison can be made between realized and prescribed application rates. This information is necessary for evaluation of fertilizer management in a precision agriculture farming system and to accurately target fertilizer application to crop and soil status.