Part one: Research:

Machine vision

Machine vision has become an integral part of innovation that is implemented by several agricultural companies to assist the farmers or crop producers to maintain accuracy and validity of the important information related to the farming operations. There are different types of machine vision technologies such as drones, machine learning, Robocop, etc. which are generally incorporated by the firms across the whole world for acquiring and storing relevant information about the crops and fields thereby improving the agricultural activities. As the name suggests, machine vision is the process of producing images or pictures of various industrial functionalities that are utilized by the respective professionals for improving the quality of organizational operations (Raveendra, et al., 2019). Through the help of machine vision, industrial automation can be considered as one of the most common and significant applications in the field of innovation. Though many industries have adopted this technology through various tools such as in healthcare industry, financial service industry, retail industry, automotive industry, etc., machine vision became predominant and well-known due to its immense application in the agricultural industry (Steger, et al., 2018). The emergence of machine vision paved the way for image generation and acquires relevant data and information about particular industrial operations in pictorial form.

It can be said that this technological approach has enabled the professionals of every industry to obtain an accurate picture regarding the market conditions, R&D functions, designs and infrastructure, product portfolio, etc. thereby planning for effective strategies for their future market growth (Daniel, 2017). Machine vision provides an exact assessment by inspecting upon the organizational activities in a proper manner. It is a key innovative tool that is used for influencing product line automation. With the adoption of such technology, the firms can conduct proper inspection and monitoring of the in-house operations by ensuring batch integrity in product development. The camera system should be utilized properly with effective lighting and optical lenses that can provide a clear vision of the desired components or elements (Pajares, et al., 2016). On one hand; it reduces the production cost and maintains high-quality products while on the other hand; the machine vision technology helps in eliminating industrial wastes thus increasing customer satisfaction. Moreover, different problems or difficulties related to the operations can be detected effectively which ultimately helps the companies to tackle them on an immediate basis.

Agricultural industry

The agricultural industry is one of the notable and important industries in the whole world as it contributes a large portion of revenue to the development of a country’s economy. This industry is responsible for producing the necessary food items for fulfilling the consumption purpose of the inhabitants of the countries. The agricultural industry is mainly comprised of those individuals or farmers who produce vegetables, fruits, dairy products, eggs, and different other products from forestry operations (Jones & Ejeta, 2016). The entire industry is dependent upon performing several significant activities like checking the fertility of the soil, harvesting crops, livestock feeding, animal gazing, etc. that help in ensuring proper growth and production of the crops in the market. It is thus of utmost importance for the farmers or agricultural producers to make sure that suitable strategies and tools are being adopted which can help them provide increased national income, greater food security to the consumers and an improved living standard.

Part 2: Brainstorming:

In the case of the agricultural industry, machine vision technology can help in assessing the challenges and future trends. Some of the features of the technology which can contribute to the disruption of the agricultural industry are:

With the help of machine vision, it will be possible to automate the process of harvesting and planting. This means that the technology will be able to assess the quality of the soil and the resources that would be important to use for proper growth of the crops (Smith et al., 2018). It will be possible to support the farmers for providing with the knowledge about the conditions of the land and the future predictions about the crops. The information would help them to prevent the losses in farming and the quality of the crops could be enhanced as well. Along with this, due to the automated process, it would be possible to categorize the crops according to their outputs and the types of resources that it would require for proper growth and production. Therefore; the overall process will become quicker to implement and the quality of the production can be increased significantly as well.
With the help of machine vision technology, it will be also possible to sort the crops according to their quality. This means it would be possible to understand which crops would be of good quality and which would be of bad quality. Therefore; the farmers would be able to understand the quality of each of the categories of crops without much effort from their side. This would help in proper quality management in the agricultural industry. Farmers will be able to supply high-quality crops in a better way. This will help in distinguishing the crops effectively and can bring high profits for the agricultural industry. Even it would be possible to identify the future trends in agriculture by which the farmers would be able to adapt with new resources for better maintenance and production of crops.
Livestock maintenance will be enhanced with the help of machine vision technology. This means that it will be possible to monitor the growth and activities of the animals which assist in the production of crops. For example, cows are used for harvesting the crops and therefore; with the use of the technology, it would be possible to understand the capabilities of the cows for proper harvesting. There would be possibilities of identifying the quality of harvesting and the productivity of these animals in the aspect of assisting in the harvesting (Matthews et al., 2016). This would certainly help in ensuring that the animals would be properly managed and changes could be made if those animals are found to be inactive or not effective for the assistance in farming.

Therefore; in the agricultural industry, it would be possible to generate quality information which would help in enhanced management of crops. The three ideas that have been generated can be induced effectively so that the assistance from the machine vision technology can be increased. It would be possible to ensure that predictions for the harvesting and maintenance of the crops would be enhanced which would assist in increasing the quality of the crops.

Part 3: Regulations and ethics:

In the aspect of ensuring that proper regulations are followed with the use of technology, it will be also important to follow proper ethics. The ethics would be important to be followed by the stakeholders of the agricultural industry. Lawrence Lessig’s model will be followed for highlighting the regulations as well as ethics that should be followed with the use of the technology. There are some factors which can impact the successful implementation of technology in the agricultural industry are:

For the agriculture industry, it would be challenging to make use of the technology on the aspect of difficulties that farmers could face. It would be difficult for farmers to learn about the effective use of technology. Therefore; they could resist the implementation. This can happen due to lack of experiences and knowledge (Kamilaris & Prenafeta-Boldu, 2018). Moreover; it would be also difficult to train them on using the processes. This can mainly happen due to illiteracy and fear of uncertainty among these farmers.
If some of the farming companies use technology, then there could be resistance from the other companies. This can happen due to the impact of disruption. This means with the fear of disruption, some of the companies which are not capable of handling this technology could raise protest and can interrupt the implementation. This would fail using technology for farming.
Due to the use of technology, there could be some legal issues as well. Legal issues could come in the form of errors in prediction and maintenance. For example, if the system fails to identify the good and bad quality of crops, then it would be difficult for the farmers to maintain and assess the quality. If any errors occur and the quality of crops is not maintained, then it can result in huge legal issues against the farming companies. The result can be also considered as unethical and the reputation of the farming companies can go down significantly.
The social issue can come in the aspect of difficulties that the farmers can face. This means that in the third world countries, it would be highly difficult for the farmers to invest in the implementation of the technology (Zhao et al., 2016). Moreover; they do not have the required infrastructures that need to be used for the implementation of the technology. Most of the farming companies could be skeptical on investing for the technology and it could fail in proper use of the technology in the agriculture industry. Along with this, a large section of farmers would not be supportive of investing for the technology. There could be fear about the risks that can occur due to the implementation of this technology.

All these issues need to be considered before proper implementation and should be eliminated with proper research and communication.

Part four: Disruption:

Description of the process

In the agriculture industry, machine vision has opened up several new and innovative opportunities for the firms in the market through which their successful and profitable operations can be ascertained (Meng, et al., 2015). Nowadays, most of the agricultural companies have been adopting different technologies for facilitating the machine vision for performing the operations effectively. The sector faces the immense necessity for attaining clear images of the fields and monitors the growth of the crops. With the rapid development of technologies, agricultural companies have implemented the machine vision technology for improving the execution of agricultural activities. Proper tracking and assessing the crop production process, harvesting of seeds, predicting the quality of the crops, the fertility of the soils, climatic conditions, livestock production, etc. can be easily done with the help of machine vision in agriculture (Naik & Patel, 2017). Moreover, animal behaviors can also be assessed along with species breeding and species recognition. Attention in the present day has been laid towards implementing several non-destructive and modern methods for enhancing the quality of overall agricultural operations. Farmers initially used their assumptions and experiences to assess the different factors influencing production growth. However, this tends to cause problems for them to carry out efficient agricultural operations. Thus, machine vision systems serve as the major innovation tool for the farmers and crop producers to ensure a proper and systematic way of performing farming activities.

Description of the components affected by machine vision

The use of machine learning, for instance, is an effectual invention in the technological field which has upgraded the agricultural functions to a considerable extent. This process helps in generating the hyperspectral and multispectral images for the inspection of modern food and crops. Different agricultural components can be affected by the implementation of machine vision technology such as monitoring the agricultural operations, surveying the quality of land and crops, predicting climatic and weather changes and availability of resources for undertaking farming activities (Sarkar, 2017). Through the help of machine vision such as machine learning technology, the farmers can obtain a clear picture about the above-highlighted components as these are very much necessary for understanding the extent to which a crop can be grown easily. The images processed by the technology adoption assist the crop producers to control the floods and droughts thereby facilitating effective management of livestock, crops and other field conditions.

However, there are some risks associated with the implication of machine vision in agricultural processes. These risks are mostly related to the low rate of acceptance towards technology by various firms or farmers. Mostly lack of knowledge and training leads the producers or farmers to face difficulties while adopting the technology. The images produced through machine vision must be understood by them to utilize the same for developing their agricultural prospects. It can be said that no proper idea about the use of the technology makes them incapable of enjoying its benefits and as such maximum utility cannot be achieved. As a result, it increases the impact of those risks on the operations thus hindering the success of the firms. Moreover, there is a need to ruggedize the key mechanisms of technology. The design or the architecture of the machine vision should be appropriate and suitable for ensuring effective implementation in the industry. The engineers need to develop proper software and hardware for maintaining appropriate calibration when used extensively. Image processing sensors should be designed properly for collecting most data and information and storing them securely to assist the agricultural professionals for performing their activities efficiently.

References

Daniel, M. (2017). Bridging the machine vision data gap for Manufacturing 4.0: Mathew Daniel, at data management software provider Sciemetric Instruments, explains the importance of centralized data collection in factory automation–and why image data is a key part of that. Imaging and Machine Vision Europe, (82), 16-18.

Jones, A. D., & Ejeta, G. (2016). A new global agenda for nutrition and health: the importance of agriculture and food systems. Bulletin of the World Health Organization, 94(3), 228.

Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90.

Matthews, S. G., Miller, A. L., Clapp, J., Plötz, T., & Kyriazakis, I. (2016). Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. The Veterinary Journal, 217, 43-51.

Meng, Q., Qiu, R., He, J., Zhang, M., Ma, X., & Liu, G. (2015). Development of agricultural implement system based on machine vision and fuzzy control. Computers and Electronics in Agriculture, 112, 128-138.

Naik, S., & Patel, B. (2017). Machine Vision based Fruit Classification and Grading-A Review. International Journal of Computer Applications, 170(9), 22-34.

Pajares, G., García-Santillán, I., Campos, Y., Montalvo, M., Guerrero, J., Emmi, L., … & Gonzalez-de-Santos, P. (2016). Machine-vision systems selection for agricultural vehicles: A guide. Journal of Imaging, 2(4), 34.

Raveendra, P., Reddy, V. S., & Subbaiah, G. V. (2019). Vision based weed recognition using LabVIEW environment for agricultural applications. Materials Today: Proceedings.

Sarkar, N. R. (2017). Machine vision for quality control in the food industry. In Instrumental methods for quality assurance in foods (pp. 167-187). Routledge.

Smith, L. N., Zhang, W., Hansen, M. F., Hales, I. J., & Smith, M. L. (2018). Innovative 3D and 2D machine vision methods for analysis of plants and crops in the field. Computers in industry, 97, 122-131.

Steger, C., Ulrich, M., & Wiedemann, C. (2018). Machine vision algorithms and applications. John Wiley & Sons.

Zhao, Y., Gong, L., Huang, Y., & Liu, C. (2016). A review of key techniques of vision-based control for harvesting robot. Computers and Electronics in Agriculture, 127, 311-323.

Disrupting the Agricultural Industry with Machine Vision technology

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