Farming automation has proved to increase the return from the soil while strengthening the soil’s fertility. This article is a brief overview regarding the up-to-date application of automation in agriculture.
Pervasive Automation in Agriculture
Article from | Len Calderone
This article discusses different automation practices like IoT, wireless communications, machine learning, artificial intelligence, and deep learning. Farming automation has proved to increase the return from the soil while strengthening the soil’s fertility. This article is a brief overview regarding the up-to-date application of automation in agriculture.
The agricultural workforce is declining, compelling the adoption of internet connectivity solutions in farming practices. Using IoT technology certifies the optimum application of provisions to achieve high crop yields and reduce operational costs. Surveys indicate that the use of IoT devices in the agriculture industry will reach $75 million this year, while escalating 20% a year.
IoT smart farming solutions monitors the crop field with the help of sensors that report the light, humidity, temperature, soil moisture, and crop health to growers, who can monitor field conditions from anywhere. The growers can also choose between manual and automated actions based on this data. Smart farming is extremely effective versus the conventional method. Let's say that the soil moisture level decreases. The grower can utilize sensors that will start irrigation.
Using IoT, the key components are sensors, control systems, robotics, autonomous vehicles, automated hardware, motion detectors, and wearable devices. This data will provide the grower with the state of the business in general, along with how effective an employee is, and how well the equipment is working.
A smart greenhouse can use IoT intelligence to monitor and control the climate, putting an end for the need of manual intervention. Smart greenhouses use modern sensors to automatically capture and deliver information 24/7 on the surroundings and crops. This data is entered into an IoT platform where analytical algorithms turn the data into actionable intelligence to reveal abnormalities. HVAC and lighting operations, along with irrigation and spraying activities can be regulated on-demand.
Automated Micro Irrigation System
Photo:U.S. Department of Agriculture
It's no wonder that farmers are transforming to wireless technologies. Only ten percent of irrigated farms in the U.S. use advanced irrigation management techniques. Wireless technology has the potential to help farmers more efficiently manage water use.
Growers can use local wireless networks to retrieve real-time information on the up-to-date conditions of their fields and the condition and location of their equipment.
Growers can use 4G and 5G networks on their smartphones or tablets to access real-time information on their fields and the agricultural markets remotely. Wireless technologies are facilitating two water-related challenges for grower, which are scarcity
and environmental impact. Water has become increasingly scarce, especially in the western states. We can expect that 40 states will have some type of water shortage in the next decade. As water becomes ever more in short supply, growers’ irrigation costs will rise, cutting into their profits and the economic strength of the agricultural industry.
Wireless technology assists growers from both over and under watering their crops. This technology can be connected to soil moisture monitors to allow growers to immediately retrieve information on the actual soil moisture requirements of their fields. They can then use the wireless technology to activate or deactivate their irrigator remotely while adjusting to the crops' water needs.
Only artificial intelligent (AI) based systems have proved to be feasible and reliable. Artificial intelligence does not generalize the problem; it gives a particular solution to a particular defined complex problem. AI manufacturers are developing robots that can perform multiple tasks on the farm without difficulty. These robots are trained to control weeds and harvest the crops at a faster pace with higher volume compared to humans.
When used in farming, AI gives growers a weapon against pests. Pests are one of the worst enemies of the farmers, damaging the crops before they are harvested and stored. Insects like locusts, grasshoppers, and others are eating the profits of farmers and consuming the grains meant for humans.
Spraying for Pests
Everyday machines learn to unravel difficult tasks. So, what is machine learning functions in agriculture today and why should growers care?
Tech companies have been working on pioneering innovations that will minimize human intervention in farming. Agriculture is one of the crucial areas where such inventions have become a necessity as well as advantageous. Smart tractors equipped with software using machine intellect, such as sensors, radar, and GPS cultivate the land and harvest the crop without needing a driver. Using such an autonomous crop handling system, it is possible to farm much more ground for longer periods of time.
Machine Learning can help growers to predict yield and crop quality, detect weeds and disease. New cutting-edge methodologies have gone beyond predictions based on historical data, to including computer vision technologies to provide data on the go for the wide-ranging multidimensional analysis of crops, weather, and economic conditions.
Unlike humans, machines can make use of seemingly meaningless data and interconnections to reveal new information concerning the overall quality of the crops. Machine learning helps with disease detection where agrochemical input is targeted in terms of time, place and affected plants. Computer vision and machine learning algorithms can increase the detection of weeds at a low cost and with no environmental issues. In the future, machine learning will drive robots that will destroy weeds, reducing the need for herbicides.
Deep learning (DL) integrates image processing and big data analysis with great potential. Deep learning is a recent tool in the agricultural arena, although it has been successfully applied to other industries. Deep learning is a class of machine learning algorithms. A deep learning technology is based on artificial neural networks (ANNs). As a part of artificial intelligence, it's a technique that teaches computers to do what comes naturally to humans. That is learning by example. Deep learning is the key technology behind driverless cars.
In conventional machine learning, the learning process is regulated, and the data needs to be labelled before entering into the computer. The foremost benefit of DL is that the program builds the feature set by itself without supervision. Unsupervised learning allows the operator to work in a real-time situation that is unpredictable and ever-changing. This is important as the IoT continues to become more prevalent, because most of the data humans and machines create is unstructured.
Pervasive automation in agriculture expands accurate and controlled growing through proper guidance to farmers about optimum planting, water management, crop rotation, timely harvesting, nutrient management and pest attacks. By using machine learning algorithms in connection with images captured by satellites and drones, artificial intelligence predicts weather conditions, analyzes crop sustainability and evaluates farms for the presence of diseases or pests and poor plant nutrition.
The content & opinions in this article are the author’s and do not necessarily represent the views of AgriTechTomorrow
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