In the past, agricultural production has relied on traditional approaches to enhancing farm production. For instance, farmers in the past used conventional approach such as the migration of birds to determine the climate conditions that were approaching. According to Zhang and Pierce (2013), farmers made planting decisions based on their experiences relating to the climatic conditions. However, traditional approaches to farming have severely resulted to a decrease in output because of the different and varying conditions that are beyond the comprehension of human experiences. As such, sensors have been developed for application in crop and animal faring with the goal of seeking to maximize production. Use of sensors is mainly used for purposes of increase the level of intensive farming. Farmers who seek to increase their capacity normally are faced with the challenge of managing a big farm. As such, decision-making is not effective as the farmer lacks all the necessary information that may be required to address issues that arise in the farm. Thus, using technologies such as sensors, the farm manager can be able to make decisions based on a systematic analysis of issues. The resulting consequence is the increase in productivity and profits in both crop and animal farming.
Electronic sensors in farming are currently being used for a variety of applications. Crop farming especially for the specialty crops is currently facing challenges that are likely to affect its long-term viability (Lee, Alchanatis, Yang, Hirafuji, Moshou and Li, 2010). The use of sensors technology leads to creation of more job opportunities for specialized professions. Further, it results to more innovative production, which leads to a competitive crop market. Sensors are used to improve operations in crop farming using machine guidance systems, accurate application of pesticides and fertilizers and improving management of irrigation practices to reduce costs and enhance production.
Lee, Alchanatis, Yang, Hirafuji, Moshou and Li (2010) provide examples of some of sensor technologies used in farming. Firstly, this may include new technologies in controlling applications of chemicals and nutrients. The aim of such technologies is to reduce costs, improve efficiency, reduce environmental impact and provide better safety conditions for the workers (Lee, Alchanatis, Yang, Hirafuji, Moshou and Li, 2010). Secondly, non-contact sensors can be used to increase productivity and reduce labor costs. This involves use of robotic solutions in operations such pruning and harvesting. Thirdly, there is the use of autonomous navigation systems, which enhance operations such as harvesting and spraying. Fourthly, sensor technologies that enhance precision agriculture include soil sensing, mapping and prediction and irrigation control among others. Lastly, sensor technologies can also be used to monitor diseases and pests. According to Lee, Alchanatis, Yang, Hirafuji, Moshou and Li (2010), this may require the use of remote sensing or ground based systems.
In dairy farming, mechanization has largely involved the use of technologies such as the automated milking systems for purposes of reducing physical labor and labor costs (Rutten, Velthuis, Steeneveld and Hogeveen, 2013). Sensors used in dairy farming include milk color sensors, acceleration sensors, pH sensors and milk electrical conductivity sensors ((Rutten, Velthuis, Steeneveld and Hogeveen, 2013). Using sensors, fertility, locomotion and issues of mastitis can be investigated. Therefore, based on the use of sensors, famers are able to make informed decisions regarding the health management of the herds.
Sensor technologies in Farming
Remote Sensing Technologies
This involves the use of image based sensors that can provide information on climatic variability and temporal analysis of crop field areas. Yield maps are normally derived from the remote sensing imagery. The use of airborne multispectral imagery and high-resolution satellite imagery incorporate real-time monitoring. According to Pajeres (2011), remote sensing imagery can be used in the analysis and quantification of crop damage. Furthermore, the imagery can be used to provide critical information on crop and weed segmentation.
Sensing technologies in soil nutrients and soil chemistry analysis
Sensing technologies used in soil nutrient analysis include NIR and MIR spectroscopy, thermal imaging, spectral library, microwave and Raman spectroscopy (Lee, Alchanatis, Yang, Hirafuji, Moshou and Li, 2010). According to Adamchuk, Hummel, Morgan and Upadhyaya (2004), the use of NIR reflectance can be a good indicator of the soil organic matter content and soil moisture in the soil. Optical sensors are used to establish the nitrogen status of the crop (O'Driscoll, 2010).
The use of radiometric sensors applies to the determination of soil properties such as soil textures, sol thickness, and water tables, depth of soil horizons and differences that exists because of compaction of soil caused by plow pan development. According to Adamchuk, Hummel, Morgan and Upadhyaya (2004), an example is the ground penetrating radar (GPR). In the determination of a soil property such as soil strength, mechanical sensors need are used. The effect of compaction is to limit the growth of roots hence the crops may be unable to obtain the required water and soil nutrients. To determine the soil resistance, a standard cone penetrometer is used. However, using a penetrometer (even an automated one) can be time consuming hence the have developments to use of prototype systems for on-the go sensing of soil mechanical resistance. Measuring the soil resistance can be applied in the selection of appropriate machinery to use in reducing soil resistance.
Crop Disease Detection sensing Technologies
Optical properties of leaves may detected using thermography. Diseases tend to affect the optical properties of a crop. The disease detection systems are normally based on the spectral measurements in the different wavebands. For instance, healthy crops will appear green while the diseased crops will normally depict a red color. According to Lee, Alchanatis, Yang, Hirafuji, Moshou and Li (2010), aerial photography used in conjunction with spatial analysis has been applied in the detection of numerous diseases such as sugar beet and potato blight.
Problems with the Use of Sensors
The use of sensors in agriculture has become very common in the past decade due to technological innovations that allow for use of advanced methods and techniques to ease some agricultural process a good example is the use of aerial vehicles to spray pesticides and other chemicals on crops. The aerial vehicles make use of satellite-guided sensors to move around the piece of land that is to be sprayed with chemicals. The vehicles are automated such that the farmer, or any other person for that reason, does not control them manually. The challenge arises when these aerial vehicles lose control and spray over a wider tract of land than is required or if the farmer changes his mind midway into the process. Furthermore, they lack the discrimination that is sometimes crucial especially if some crops within that tract of land ought not to be sprayed. In such instances, they result in severe wastages and hence economic wastes.
Other irrigation systems make use of moisture sensors to detect the level of water in the soil and hence activate the irrigation system. This enables effective irrigation of the crops with minimal wastages since the system is alienated whenever there is sufficient soil moisture due to rainfall or otherwise. However, such irrigation systems may prove faulty since the failure of the sensors to detect the presence or absence of water due to malfunctioning, short-circuiting or otherwise results in an overall failure of the whole irrigation system. This may cause the crops to dry up. Furthermore, farmers have found serious challenges in tilling their land without interfering with the location of the sensors in the soil. This means practices such weeding are curtailed. Consequently, the weeds have to be controlled only via chemical means since tillage would damage the soil sensors. This results in a vicious cycle since the soil becomes polluted with excess chemicals. Some of these chemicals may undermine the crops’ healthy growth and hence production.
The use of aerial vehicles in spraying pesticides and farm chemicals is especially disadvantageous since it is prone to losses due to the effect of wind. Many chemicals fail to land on the plants due to drift. Many plants, especially those covered by taller ones, fail to get any chemicals sprayed on them. This results in redundancies and reduced crop yield. The chemicals cover only the outer edges of the crops. This allows for pests and pathogens to hide on the inner side and continue causing harm to the crops. Structures such as greenhouses rely on thermometers and humidity sensors to adjust the temperature and airflow automatically. Failure of these sensors to accurately detect these conditions and the deviation from the optimum conditions could result in utter crop failure and huge losses.
The use of farm machinery to harvest crops relies on multiple sensors, both in counting and arranging the produce. The sensors are supposed to be sensitive enough to ensure minimal damage to the crops while accurate enough to ensure no wastes occur. However, famers find it difficult to find machinery that keeps an accurate database while carrying out the combined harvesting. Futuristic as this may be, it offers great platform for redundancies to be minimized (Zhang & Pierce, 2013).
The use of sensors in farms continues to offer numerous benefits to farmers and to agriculture as a whole. Despite the challenges and problems that come with the use of sensors their credibility continues to grow as innovations are prompted by these challenges. The farmers of the future may have a very different agricultural experience as compared to their predecessors due to the advancements that are to come, expensive as they will be. Sensors play a major role in improving the production of most farms. However, for the use of sensors in agriculture to be economically logical, it must involve large-scale or intensive farming. This enable the farmers to enjoy economies of scale when purchasing the sensors and also a considerable reduction I the physical labor that would otherwise have been hired in place of machines and other automations.
Adamchuk, V. I., Hummel, J. W., Morgan, M. T., & Upadhyaya, S. K. (2004). On-the-go soil sensors for precision agriculture.Computers & Electronics In Agriculture, 44(1), 71-91.
Lee, W. S., Alchanatis, V. V., Yang, C. C., Hirafuji, M. M., Moshou, D. D., & Li, C. C. (2010). Sensing technologies for precision specialty crop production. Computers & Electronics In Agriculture, 74(1), 2-33
O'Driscoll, C. (2010). Extra precision agriculture. Chemistry & Industry, (14), 18-21.
Pajares, G. (2011). Advances in Sensors Applied to Agriculture and Forestry. Sensors
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Rutten, C. J., Velthuis, A. J., Steeneveld, W. W., & Hogeveen, H. H. (2013). Invited review:
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Zhang, Q., & Pierce, F. J. (2013). Agricultural automation: fundamentals and practices. Boca
Raton: CRC Press, Taylor & Francis Group. (http://books.google.co.ke/books?id=qYvThP8PoqkC&pg=PA44&dq=use+of+sensors+in+farm+production&hl=en&sa=X&ei=xEJZU8zOIqa10wXN2IHQAQ&redir_esc=y#v=onepage&q=use%20of%20sensors%20in%20farm%20production&f=false)