Drones Revolutionizing Agricultural Monitoring

The Impact of Unmanned Aerial System Technology on Agricultural Monitoring and Production

In recent years, unmanned aerial systems have been available for agricultural and environmental application. In agriculture, the use of drones basically boils down to four different segments: GPS map creation using on-board cameras, Crop field scanning with compact multispectral imaging sensors, livestock monitoring with camera-equipped drones with thermal imaging and heavy payload transportation (Margaritoff, 2018). The advantages of a drone in the air is the shift of perspective that it provides us with. By getting a birds view, we can always get to precisely looking down at the objects we intend, whether soil or biomass.

While techniques like sensors and field measurement have been used to measure soil properties like organic matter, moisture, salinity, pH and clay content, they have proven to be more accurate and reliable, energy and time consuming, laborious and expensive. It is also possible to calculate other soil attributes like pH from on-the-go spectrometers for in-situ measurements of reflectance spectra through the use of infrared reflectance spectroscopy. UAS has also made it possible to map soil texture and organic carbon using near-infrared measurements and also applying on-the-go measurements in a manner that is inexpensive (Zhang and Kovacs, 2012). The results have been increments in yields because farmers are capable of identifying problems before they even get to happen and as such increasing frequency and health awareness. This leads to improvements in yields and production and as such the higher yields.

Through establishing soil moisture content and the needs of different plants, farmers today have knowledge of applying inputs that are more precise like minerals. The more human beings know, the better they are placed to supplement the needs. While the importance of the physical properties of soil and its effects on plants has been well established, monitoring has been laborious, time-consuming and expensive (Colomina, and Molina, 2014). Through the use of remote sensing systems, farmers are today capable of getting laboratory quality information from every pixel in an image in a quicker manner. The effect of knowledge that is precise about SMC`s in agricultural fields have been able to demonstrate improvements in the field of education and is expected to continue doing so in different areas of agriculture. This has led to a base of agricultural practices that is more informative and detailed like artificial fertilisers, irrigation and herbicide and pest application which lead to agriculture that is more sustainable (Huang et al., 2013). Estimating soil moisture has been observed to have positive effects as it helps with the input decisions of farm operations and also has impacts on the environment that are positive like reduction in pesticides and fertiliser runoff from agricultural fields. Approaches that are user-friendly and more organised "toolbox" of inputs would definitely attract more consumers and also improve user-friendliness. Precision farming can be improved by networks of modern technology, collaborating with others and acquiring information that is key.

Applying hyper-resolution vineyard mapping based on multispectral, visible and thermal images has over the years been demonstrated. Estimation of crop nitrogen estimation, vegetation canopy mapping, crop and soil temperature while proving to be affordable have also proved to be feasible and precise. Successfully applying of UAS supported image capture has the potential of creating the required time frame for agricultural practice adjustment and that this remote sensing results could also possibly exceed those from control treatments that are traditional (Cano et al., 2017). With the increasing popularity of software's that are user-friendly and artificial neural networks, some have been offering these services in a manner that is commercial given the example of Drone play. UAS technology has also been helpful in controlling insects, weeds and plant diseases that are always devastating to farmers. Some of the advantages that exist with remote sensing of plant diseases and weeds are virtual maps that are generated instantly that show a fields status. Through the use of UAS, normal and variable rate pesticides applications are used.

Over time, UAV`s have proven to a useful platform for detecting yield variability and plant growth. For those farmers who so much wish to estimate their predicted harvest and the income that will result from it, crop yield is always a valuable indicator. The most efficient and effective way of doing this is definitely through the use of a UAS (Torres-Sánchez et al., 2017). The process followed so as to acquire these estimates consists of several steps, for example geo-referencing the images and procedurally classifying them into zones of homogenous spectral response through using classifying procedures that are unsupervised. There are several studies that have been conducted and it has been established that relative yield maps that depict fields` spatial variability are shown by green normalised difference index (Chang et al., 2017).

Through the use of UAS, people are today able to establish water stress and crop nutrient. This is achieved through the use of remote sensing indices like GNDVI, NDVI and NDWI. Visible and thermal imagery has also been used to estimate the level of water stress in grapevines that are irrigated. Water stress and crop nutrient are important indicators that tell farmers about the quality of crops and the health of plants. The use of UAS is able to save a lot of manpower by just lifting the perspective into a bird’s view and as such being able to cover more hectares of land in a manner that is more efficient (Gago et al., 2015).

In concluding, UAV`s rapid development has been contributing to better technologies that are also more improved that re-adapted into the agricultural sector. The appropriate use of SMC monitoring together with integrated geospatial sensor webs has the potential of substantially increasing farm operations efficiency. Agricultural field operations consist of tools and equipment's that are energy demanding and also heavy and as such introduction of light, energy effective and small technologies is definitely a step forward into agricultural practices that are more environmentally friendly and also sustainable (Haghighattalab et al., 2016). Improvements are however needed to develop UAS that is more adapted for the agricultural sector and with little intervention from humans.

References

Cano, E., Horton, R., Liljegren, C. and Bulanon, D.M., 2017. Comparison of small unmanned aerial vehicles performance using image processing. Journal of Imaging, 3(1), p.4.

Chang, A., Jung, J., Maeda, M.M. and Landivar, J., 2017. Crop height monitoring with digital imagery from Unmanned Aerial System (UAS). Computers and Electronics in Agriculture, 141, pp.232-237.

Colomina, I. and Molina, P., 2014. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, pp.79-97.

Gago, J., Douthe, C., Coopman, R., Gallego, P., Ribas-Carbo, M., Flexas, J., Escalona, J. and Medrano, H., 2015. UAVs challenge to assess water stress for sustainable agriculture. Agricultural water management, 153, pp.9-19.

Haghighattalab, A., Pérez, L.G., Mondal, S., Singh, D., Schinstock, D., Rutkoski, J., Ortiz-Monasterio, I., Singh, R.P., Goodin, D. and Poland, J., 2016. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods, 12(1), p.35.

Huang, Y., Thomson, S.J., Hoffmann, W.C., Lan, Y. and Fritz, B.K., 2013. Development and prospect of unmanned aerial vehicle technologies for agricultural production management. International Journal of Agricultural and Biological Engineering, 6(3), pp.1-10.

Torres-Sánchez, J., López-Granados, F., Serrano, N., Arquero, O. and Peña, J.M., 2015. High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PloS one, 10(6), p.e0130479.

Zhang, C. and Kovacs, J.M., 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precision agriculture, 13(6), pp.693-712.