sensors used for smart farming are shown in Figure 1.8.
Electromagnetic sensors detect the ability of soil particles to conduct or acquire electrical charge using electric circuits. The electrical conductivity of the soil is immediately recorded in a logger after it becomes a member of the electromagnetic circuit. To measure electrical conductivity, contact EC sensors use electrodes and make contact with the soil. The data, as well as the position information, are stored in a data logger. Contact EC sensors are highly common in precision agriculture because of their large area coverage and less prone to outside interference.
Figure 1.8 Various sensors for smart farming.
Optoelectronic devices allow for such interaction. In general, optoelectronic sensors use the properties of various materials to create a spectral signature. A common type of sensor in this category is the soil organic matter (SOM) sensor that uses the optical method.
An ion-selective electrode is used for some measurements. The activity of certain ions is detected by these electrodes. The mechanical resistance of the soil is measured by mechanical sensors. Airflow sensors are used to determine the air permeability of the soil. The sensor detects the pressure required to compress a specific volume of air into the soil at a specific depth.
Acoustic sensors: The change in noise level caused by the tool’s interaction with the soil particles is used to evaluate soil texture. Soil pH sensors detect acidity and alkalinity in the soil, which can lead to fertility issues, stunted growth, oddly colored leaves, and poor plant health. Ammonium sensors detect exact ammonia and nitrogen concentration and nitrate sensors are devices that detect the presence of nitrate in the soil or water.
Potassium sensors: These devices monitor potassium levels in the soil to better understand the relationship between free potassium in soil nutrients reserves, soil texture, and root growth, which could lead to more efficient fertilizer use. Soil-water sensors are useful for detecting local energy and water balances, delivery of applied chemicals to plants and groundwater, irrigation management, and precision farming.
1.7 Advantages of Using AI in Agriculture
In agriculture, AI aids farmers in comprehending data insights, such as temperature, precipitation, wind speed, and solar power.
1 i. Essential plants can be produced, harvested, and marketed more efficiently with AI.
2 ii. The focus of AI implementation is on detecting defective crops and improving the chances of healthy crop production.
3 iii. Agro-based businesses have benefited from the advancement of AI technology.
4 iv. AI is being used in applications such as weather forecasting and automated machine adjustments.
Artificial Intelligence has the potential to improve crop management practices, allowing many tech companies to use advanced algorithms
1.8 Role of AI in Indian Agriculture
In India, adoption of modern technologies referred to as Agritech developments in various verticals like smart irrigation, weather technology solutions, and so on. The Government of India has initiated a new program called AGRI-UDAAN with the aim to boost innovations and entrepreneurship in India. Indian agriculture also attracts a foreign direct investment equity inflow of about 2.45 billion dollars (according to DIPP). To meet one-sixth of the total Indian economy, we need nearly half of India’s land and huge labor.
Various AI and ML methods are employed in predictive agricultural analytics to anticipate the best time to sow seeds, receive alerts on upcoming pest attacks, and so on. Artificial Intelligence in agriculture enables the most efficient use of farming data, allowing types of equipment such as smart drones, autonomous tractors, soil sensors, and Agri-bots to support smart farming.
In the fiscal year 2019 to 2020, 133 agreements raised more than $1 billion for Indian agrifood tech start-ups. India’s agricultural exports increased to $37.4 billion in 2019, and this is expected to rise further with improvements in the supply chain, as well as better storage and packaging. All of these measures will go a long way toward ensuring farmers receive fair pricing and reducing agrarian stress. Investments in technology are helping to boost agricultural output and productivity even further. Disruptive technologies, such as AI, are transforming Indian agriculture, and an increasing number of agri-tech businesses are developing and implementing AI-based solutions.
Artificial Intelligence has the potential to improve farm production, alleviate supply chain restrictions, and expand market access. It has the potential to benefit the entire agriculture value chain. By2026, AI in global agriculture is expected to be a $4 billion opportunity. The use of AI in agriculture in India might promote mechanization. By implementing precision agriculture, it would boost productivity. Agriculture technology businesses in India are attempting to combine AI-based technical solutions across a variety of use cases, including crop production and soil fertility monitoring, predictive agricultural analytics, and supply chain efficiency. Industry and government have teamed up to develop an AI-powered crop production forecast model that will provide farmers with real-time advice. To help raise crop output, improve soil yield, limit agricultural input waste, and warn of pest or disease outbreaks, the system uses AI-based prediction tools.
To offer correct information to farmers, the system incorporates remote sensing data from the Indian Space Research Organization (ISRO), data from soil health cards, weather predictions from the India Meteorological Department (IMD), and soil moisture and temperature analysis, among other things [35]. Similarly, a growing number of Indian start-ups are using AI-based agricultural solutions. A start-up has used data science, AI, and ML algorithms, as well as data sets from ISRO, to estimate crop damage and give compensation based on the amount of damage.
Even though the green revolution system in India made the nation self-sufficient in food grains, the agricultural sector should use modern technologies like AI, ML, and robotics. Many start-ups help in finding ways so that farmer receives various inputs and suggestions via mobile phones. CropIn is a company in Bengaluru that helps the farmers to know the quality of soil, assists the farmers to monitor the crops, and alerts them when the disease impends on the crops through a specific alert system. Deep Learning algorithm-based Graphical User Interface system has been developed by Intello labs situated in Bengaluru. It helps farmers to know crop health through image processing techniques. Microsoft India came out with a new AI-based App, which helps the farmer to sow the seeds at right time with the big data techniques by collecting climatic data over the past 30 years from 1986 to 2015.
Increased public and private investments, particularly from venture capitalists, are required to enable these AI technologies
1.9 Case Study in Plant Disease Identification Using AI Technology—Tomato and Potato Crops
The traditional method of plant disease identification through visual observations of the symptoms of plant leaves leads to a significant high degree of inaccuracy. Today modern tools incorporate graphical processing unit which includes ML-based algorithms which precisely detect the plant disease and assist the pathologist to easily identify the disease. Machine learning is the subset of AI that uses algorithms that predicts experience. Nowadays, a technology named DL using a large amount of processing layers made exponential growth in AI. The common diseases of tomato and potato crops
