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Figure 1.6 Steps involved in advanced technologies.

      Aerial drones can evaluate and monitor crops in addition to—or perhaps instead of—IoT monitoring. The drones collect information about plants down to a single leaf using cameras and sensors placed inside. All of the acquired data, when fed into an ANN or ML solution, can produce a detailed image of a farmer’s herd.

      Earth observation satellites have recently made high spatiotemporal remote sensing data available. Satellite and aerial imaging technologies are particularly valuable for capturing effective sensory images to monitor the environment, floods, fires, droughts, and other natural disasters, as well as agricultural applications like mapping, crop evaluation, crop health, and drought prediction. It offers high-speed spatial data at the global level. Numerous agricultural and hydrological indices have been created from this distant data to define the state of the land surface, primarily vegetation, groundwater level, soil moisture, and so on, to monitor and detect the beginning, duration, and severity of drought.

      Managing cattle and livestock is no easy task. Farmers must not only keep track of each animal’s whereabouts, but they must also stay updated about their health. Farmers tie their cows with Fitbit-like IoT wristbands that monitor data in real-time to relieve some of the burdens. Animals will be benefitted from such wearable devices.

      Experts can utilize the data acquired to develop predictive models and compare the performance to gain insights. The sharing of thousands of setups and pertinent facts in the farming world can lead to more efficient operations across the board. Agricultural specialists can exchange and consume a large quantity of knowledge, which includes anything from soil and seed tests to yield large production.

1.5 AI in the Lifecycle of the Agricultural Process

Despite modern technologies, instability in climate and unsustainable agriculture practices cause agricultural distress. The AI technology can help agriculture in the sectors [5] as shown in Figure 1.7.

      1.5.1 Improving Crop Sowing and Productivity

      Artificial Intelligence helps the farmers to determine the appropriate crop production in a favorable climate. An AI-based machinery helps in sowing crops at equidistant intervals and optimal depths. For example, in Andhra Pradesh, AI-powered sowing mobile application helps the farmers to increase the yield by about 30% per hectare [39]. The pilot farming was launched with the combined effort of Microsoft and ICRISAT (International Crops Research Institute for the Semi-Arid Tropics) and was implemented in the Kurnool district in 2016. Machine learning with business intelligence tools helped the farmers and Government to use digital technologies with the dashboard providing SMS for seed sowing, optimal seed depth, land preparation, and weed management [6].

Figure 1.7 The lifecycle of the agriculture process.

      1.5.2 Soil Health Monitoring

      Adequate amounts of moisture and nutrient content in the soil also contribute to the best yield. Soil health can be effectively monitored using distributed technology with DL and image recognition approach. Remote sensing techniques along with hyperspectral imaging and 3D laser scanning are also used for constructing crop matrices for better yield. The Indian Government introduced schemes like Soil Health Management (SHM) and Soil Health Card (SHC). The SHM scheme promotes judicious usage of chemical fertilizers, soil test recommendations, ensuring quality fertilizers, and so on. Each farmer is given SHC to make sure that a good harvest is possible by analyzing the soil quality. According to this scheme, the states like Madhya Pradesh, Rajasthan, Karnataka, and Uttar Pradesh [7] and nearly 45 million farmers got benefitted.

      1.5.3 Weed and Pest Control

      India needs 400 million tons of food to feed nearly 1.7 billion people by 2050 [12]. The food production decreases due to irregular climate which favors weed growth and thereby reduces the yield and quality of production. Many researchers in India studied the economic loss due to the presence of weeds. According to Sahoo and Saraswat, the loss was estimated to be INR 28 billion in the last two decades [8]. Bhan et al. [9] estimated that the 31.5% of reduction is mainly due to weeds. Varshney and Babu [10] estimated an economic loss of INR 1050 billion/year. Yogita et al. [11] estimated about 11 billion dollars lost due to weeds. The major crop which estimated economic losses is groundnuts, maize, soybean, wheat, rice, and so on [11, 28]. It is reported that about one third of total losses are because of weeds [13]. Despite efforts taken by weed management, weeds are considered to be a serious issue for different crops and other ecosystems. The main challenges faced by Indian farmers are as follows [36]:

      Various weed managements are prevailing, namely chemical, mechanical, biological, and cultural control. It is difficult to manage the weed effectively using single weed management. The use of integrated weed practices is suggested by many researchers [14–20] for major crops like rice, wheat, finger millet, maize, cotton, groundnut, and so on [29]. In a nutshell, it is proven that the herbicides combined with hand weeding help in removing weeds and increase crop production [21]. However, location-specific weed management with AI technology is necessary for Indian crops.

      1.5.4 Water Management

In India, because of diverse climatic conditions, water management is not effective. Modern technologies are being used—thermal imaging camera, which monitors the crop determines whether it is getting adequate water. It is reported that water scarcity in India can lead to about 6% of the Gross Domestic Product (GDP) by 2030. The researchers also predicted that about 70% of groundwater is being pumped faster than estimated [23]. It is high time to look into the overpumping

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