Sensor Comparison for Grass Growth Estimation (original) (raw)
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Sensors Applied to Digital Agriculture: A Review
REVISTA CIÊNCIA AGRONÔMICA
Sensors are the basis of digital agriculture; they provide data that allows the development of agricultural control and supervisory systems, and it helps analyze the performance of management practices. Further, sensors can be used to provide data for algorithms developed to automate the prescription of inputs. Among the sensors used in agriculture, those used to monitor soil, plants, and crop yield are reviewed in this work. In soil monitoring, the aim is to measure variables associated with the physical and chemical characteristics of soil to evaluate soil fertility and compaction. In plant monitoring, sensors are used to detect diseases and pests, weed infestation, and nutritional stress. Sensors present in the yield monitors of the harvesters allow the generation of yield maps. Finally, remote sensing techniques for predicting crop yields are analyzed owing to their potential applications in crop management.
International Journal of Computer & Software Engineering, 2016
Background: Technology is constantly evolving with respect to production agriculture. Precision farming technologies have been increasingly recognized for their potential ability for improving agricultural productivity, reducing production cost, and minimizing damage to the environment. Methods: The combined use of different sensors and the following analysis of the recorded data allowed to define an efficient technique for crop monitoring. Results: We obtained the volume reconstruction of several plants and the NDVI mapping by exploiting the proposed technique. Conclusion: Crop monitoring and yield forecasting play a major role in the agricultural context. Future work will be devoted to develop and use a customized index structure to increase the efficiency and to integrate in our knowledge base some geo referential data.
Studies on Using Optical Sensors in Agricultural Production
2019
The optical sensors for assessing the condition of crops are based on the analysis of the wavelength of the lightreflected by the vegetal mass, i.e. the measurement of the wavelength type and intensity of light reflected back to thesensors. In general, optical sensors are based on the ability of vegetation to reflect incident electromagnetic radiation,which allows for correlation between the qualitative and quantitative data characterizing agricultural crops. Sensors formeasuring crop reflectance can be graded by platform, such as: aerial or spacecraft (satellites, aircraft, unmanned aerialvehicles, balloons), field means (hand-held sensors, sensors mounted on agricultural machinery)
Trend Technologies for Robotic Fertilization Process in Row Crops
Frontiers in Robotics and AI, 2022
The development of new sensory and robotic technologies in recent years and the increase in the consumption of organic vegetables have allowed the generation of specific applications around precision agriculture seeking to satisfy market demand. This article analyzes the use and advantages of specific optical sensory systems for data acquisition and processing in precision agriculture for Robotic Fertilization process. The SUREVEG project evaluates the benefits of growing vegetables in rows, using different technological tools like sensors, embedded systems, and robots, for this purpose. A robotic platform has been developed consisting of Laser Sick AG LMS100 × 3, Multispectral, RGB sensors, and a robotic arm equipped with a fertilization system. Tests have been developed with the robotic platform in cabbage and red cabbage crops, information captured with the different sensors, allowed to reconstruct rows crops and extract information for fertilization with the robotic arm. The main advantages of each sensory have been analyzed with an quantitative comparison, based on information provided by each one; such as Normalized Difference Vegetation Index index, RGB Histograms, Point Cloud Clusters). Robot Operating System processes this information to generate trajectory planning with the robotic arm and apply the individual treatment in plants. Main results show that the vegetable characterization has been carried out with an efficiency of 93.1% using Point Cloud processing, while the vegetable detection has obtained an error of 4.6% through RGB images.
Journal of Applied Horticulture, 2015
Sensors have been used to detect tree sizes for agrochemical and fertilizer applications in grape vineyards. Rugged and reliable sensors are required to measure the size and quality of tree canopy volume for variable rate fertilizer application. Real time sensing is important as size of the tree changes with time due to biological factors and management practices. This study evaluated ultrasonic was established by moving targets perpendicular to the centerline on both sides. The maximum sensig range of sensors varied from 6 to 8 m with ultrasonic sensor having the highest range. The beam widths for ultrasonic sensors were found to be wide (maximum 950 mm) whereas optical sensor has a narrow maximum beam width of 70 mm. The laser sensor has a sharp beam and did not work well in outdoor environment with plant materials. Statistical analysis was also done for sensors and found that P value is lower than 0.001 and R 2
A Review on Crop Sensors for Agriculture
United International Journal for Research & Technology, 2020
There have been numerous investigations and exploration done on examining the samples that can be seen in the yield creation and foreseeing information of comparable nature. To survive the imperfections of conventional horticulture, for example, enormous work and labor prerequisite, no real time data accumulation, little observing zone, Wireless Sensor Network based exactness horticulture, expectation for expanding crop yield is finished utilizing data mining strategies. These hubs sense the natural boundaries like temperature, dampness, pH, NPK values. Examination is done on detected information which is caught from field what's more, put away in worker for additional examination. Hence, there is a need for an exceptional model that does expectation of the harvest yield however utilizes these farming boundaries to give results that help improving the harvest yield. The farmer or scientist can do effective cultivating utilizing this innovation.
Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS
Sensors
As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R2 of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R2 of 0.91. Ultrasonic sensors did not perform well due to our static measureme...
IRJET- Automation in Agriculture: An Application of Computer Vision Technology
IRJET, 2021
In this era of 21 st century, technology has significantly impacted our lives. There have been fascinating innovations and product releases using technology which have impacted almost every sector of industry. Agriculture is one of the fields which has been significantly impacted by the technological advancements. Modern agriculture is driven by the continuous improvements using the digital tools and data which has increased the processes involved in agriculture. This paper describes in detail the extensive application of computer vision in automating the processes of agriculture. Making the best usage of agriculture will help the farmers to greater extent to get the better quality of seeds, to use chemical fertilizers effectively and for proper irrigation process. Technology has changed the concept of farming thus making it more profitable, efficient, safer and simple. Computer Vision is one such field whose extensive application in agriculture can be largely beneficial for automation. The intelligent systems based on computer vision are becoming in agricultural operations thus increasing the productivity. The rapidly growing population will lead to gradual reduction in the cultivated land and this will increase the productivity pressures on agriculture. The traditional agricultural methods should now be equipped with the digital technologies and efforts have to made to make the processes involved in the agriculture some simpler and productive. Focus should be given on using communication and information technologies to improve the total yield and quality of the crops in agriculture. In this era of 21 st century, it is extremely important to be aware of the technological advancements happening around and applying these advancements for the betterment of human life is the need of the hour. This paper therefore, highlights one such application of technological advancement in the field of Agriculture.