What is the impact of artificial intelligence (AI) on agriculture? - Agribusiness Education and Research International

What is the impact of artificial intelligence (AI) on agriculture?

An important part of the economy is agriculture. Across the globe, the primary issue and burgeoning topic is agriculture automation. The demand for food and jobs is rising along with the population’s rapid growth. The farmers’ old methods were insufficient to meet these needs. New automated techniques were therefore introduced. In addition to meeting the world’s food needs, these innovative techniques gave billions of people access to jobs. A revolution in agriculture has been ushered in by artificial intelligence. Crop output has been shielded by this technology from a number of threats, including population expansion, climate change, job concerns, and issues with food security.

Artificial Intelligence is a new technology in agriculture. AI-powered machinery and equipment have raised the bar for today’s agricultural sector. Crop productivity has increased along with real-time monitoring, harvesting, processing, and selling thanks to technology. Drones and agricultural robots, together with other advanced technologies, have revolutionized the agro-based industry. Many advanced computer-based systems are made to identify numerous crucial factors, such as crop quality, yield, and weed detection, among many other methods.

AI’s effects on agriculture

AI-based solutions help to increase productivity across the board and manage the issues that many industries, including the agricultural sector, confront. These challenges include those related to crop yield, irrigation, soil content sensing, crop monitoring, weeding, and crop establishment. Robots for agriculture are designed to provide high-value AI applications in the aforementioned field. The agricultural sector is in trouble due to the growing global population, but AI has the ability to provide much-needed solutions. Artificial Intelligence (AI)-driven technical advancements have allowed farmers to increase yields while using less input, while simultaneously improving the quality of the yields and speeding up the time crops reach the market. 75 million linked devices will be used by farmers by 2020. Farmers are predicted to produce 4.1 million data points per day on average by 2050. The agriculture industry has benefited from AI in a number of ways, including the following:

Identification and interpretation of images

There has been a rise in interest in autonomous UAVs in recent years. These applications include forest fire detection, search and rescue, human body detection and geolocation, recognition and surveillance. Drones, also known as unmanned aerial vehicles, or UAVs, are becoming more and more popular as a means of reaching great heights and distances and completing a variety of tasks due to their versatility and amazing imaging technology, which includes delivery and photography. They can also be controlled with a remote controller and are quite dexterous in the air, which allows us to do a lot with them such as photo editing.

Read: What is climate-smart agriculture (CSA)?

Competencies and Labor Force

Farmers may now gather vast amounts of data from public and government websites, analyze it all, and receive answers to many unclear problems. Artificial intelligence (AI) also enables us to irrigate more intelligently, hence increasing agricultural yield. Artificial intelligence will likely lead to farming in the near future being a combination of biological and technology talents, which will reduce losses and burdens for farmers while also improving quality outcomes for all.

The UN predicts that by 2050, two thirds of the world’s population would reside in cities, necessitating a reduction in the demands placed on farmers. Artificial Intelligence (AI) has applications in agriculture that can automate many procedures, lower risks, and make farming relatively simple and effective for farmers.

Weed and pest management

Herbicides have negative impacts on the environment and human health, thus applying them specifically to manage weeds is still difficult. Systems with AI and image processing capabilities can evaluate crop growth automatically and warn of the likelihood of insect assaults. Among the latest developments in pest management is the creation of early warning models. Primarily focused on large datasets, these models initially gather many data points that may contribute to the spread of pests. Real-time data on soil, weather, and other environmental factors are gathered from the fields, and a neural network is trained to forecast the level of pest presence in the field.

It is also discovered that, in large fields and greenhouses, object detection utilizing Faster R-CNN (faster region-based convolutional neural network) is particularly effective in eliminating pests. New technologies for spraying can increase the effectiveness of the spray, including air-assisted, ULV, canopy, ultrasonic sensor-based, and electrostatic sprayers. In doing so, It will lessen the need for pesticides, the risk of over-application, and the pollution of soil and groundwater. It will also be able to limit the number of sprays applied to horticultural crops and the field.

Read more: 4IR impact on agriculture

Application of fertilizer

Another significant factor contributing to the low yield from the agricultural area is the over- or under application of fertilizers, in addition to the existence of weeds. Before applying fertilizers, soil testing is necessary to determine the crop’s nutrient requirements. The farmers frequently omit this step due to the intricate laboratory procedures. Organizations now days are encouraging farmers to use digital and technology-enabled farming methods.

Smarter fertilizer application can be facilitated by AI and IoT technologies. An NPK sensor that is made of light-emitting diodes (LED), light-dependent resistors, and resistors can be used to monitor the amounts of potassium (K), phosphorous (P), and nitrogen (N). Photoconductivity and colorimetric principles are the foundation of the sensor’s operation. An on-chip processing unit or system receives the values directly from the NPK sensor. With fog computing or edge computing, more analysis is performed. An edge server connected to the internet does edge computing, while the processor in direct touch with the sensors performs fog computing.

Increase the yield to the maximum

The ideal performance level for every plant is determined by variety selection and seed quality. New technologies have made it easier to choose the best crops and have even enhanced the selection of hybrid seeds that are most appropriate for the demands of farmers. It has been put into practice by comprehending how seeds respond to varied soil types and climatic circumstances. Plant diseases are less likely as a result of gathering this data. We can now satisfy customer demands, annual results, and market trends, allowing farmers to effectively optimize crop returns.

Read more: Application of Drone in Agriculture

Chatbots designed for agricultural use

The conversational virtual assistants that automate user interactions are known as chatbots. With the use of machine learning and artificial intelligence, chatbots have made it possible for us to comprehend natural language and communicate with consumers in a more tailored manner. The facility is primarily designed for retail, travel, media, and agricultural. Agriculturalists have made use of it by helping them find solutions to unresolved concerns, offering guidance, and making various recommendations.

Mechanized management of livestock

A major component of rural development and subsistence is livestock. According to studies, there is a significant yield gap because farmers continue to use antiquated methods. The main issue is how to structure and direct the expansion of this cattle industry so as to reduce the yield difference. This presents a research opportunity that can be leveraged to produce a sustainable solution with the aid of cutting-edge technologies. By automating every step of the monitoring, analysis, and decision-making process, the Precision Livestock Farming (PLF) system ensures the health and welfare of the cattle.

Read more: What is Smart Agriculture?

In most rural communities, backyard poultry farming is still an ancient activity that benefits women’s organizations greatly. Using real-time advanced data analysis, a poultry management system makes automation easier. The environment, precision feeding system, and chicken welfare are the three primary pillars of precision poultry farming. Numerous sensors in environment monitoring systems measure temperature, humidity, and chemicals like CO2 and ammonia, which can have an adverse effect on the health of the bird.

Furthermore, prediction methods based on deep learning can forecast the broiler weight up to 72 hours ahead of time. Keeping an eye on all of the farm’s animals can be a laborious task that demands careful attention to detail. One option for automated monitoring is to use sensors affixed to the animals; they can be used in both small and large-scale cow farms.

The prompt and effective re-productivity of the cattle has a major impact on the productivity of a dairy farm. Because there are no reliable methods for identifying the calving and estrus events in time, the conventional artificial insemination system is inaccurate. Automated systems that make use of sensors—such as temperature, pedometer, accelerometer, and others—can gather data regarding the cattle’s present condition. Their patterns of activity and duration change from a normal condition during estrus and calving events. An artificial intelligence machine trained on the gathered data can use this pattern to spot activity anomalies and determine that the cow is in estrus or is about to give birth. Hourly rumination, feeding, and resting times are the three most crucial factors that can be used to assess the health of the cattle. From the accelerometer data, they can be easily produced. Accelerometers placed on the cattle’s neck and legs are used to measure the rumination and feeding times as well as the standing times. Binary classification algorithms like logistic regression can be employed to determine whether or not the cattle are in estrus.

Greenhouse Control

Due to the involvement of numerous components, maintaining the environmental variables within the greenhouse is a laborious task. One area where technology intervention can make farming easier than ever before is in the region where these climate swings can also harm crops. Sensors are used in modern greenhouses to measure environmental parameters and the local climate. Large-scale wireless sensor networks with many nodes are able to sense, act, and communicate information with stakeholders. Broadly speaking, the design is comprised of a base station that handles monitoring and a wireless sensor network data handling sub-system.

AI application in agriculture, however, has the potential to enhance energy management. Green and clean agricultural products can be produced and energy savings can be achieved by using AI to simulate the energy required for farming activities. More farmers may choose to use renewable energy sources as a result of the growing use of AI and smart agriculture. The reduction of carbon footprints resulting from farming, agriculture, and food production is greatly aided by AI.

Read more: 4IR impact on agriculture

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