The Digital Agricultural Revolution. Группа авторов

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СКАЧАТЬ 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]:

      1 (i) managing weeds in small area cultivation,

      2 (ii) inadequate labor and modern tools,

      3 (iii) less information about weed biology,

      4 (iv) impact of climate change on growth of weeds,

      5 (v) lack of knowledge in usage of herbicide which kills the weeds.

      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 of groundwater. Artificial Intelligence coupled with image processing techniques helps in proper water management thereby increasing yield.

      1.5.5 Crop Harvesting

      It is reported that about 40% of the annual agricultural cost is being spent on labor employment. Nowadays, AI-based robots are being deployed to reduce labor costs. Artificial Intelligence also finds application in supply chain management [22] for crops.

1.6 Indian Agriculture and Smart Farming

      As the population increases, the traditional methods of agriculture are not suitable, and adopting the latest technologies is essential for improving productivity and farmers’ income. The revolution in the latest technologies like Big Data, Cloud Computing, Internet of Things, and Satellite, and Drone-based image analysis has made a big impact in many Indian industries from IT to health care. Nearly 450 start-ups from India are using the latest technologies like AI, ML, blockchain, big data, drones, and IoT with the main aim of increasing agricultural productivity [24]. Gobasco is a private concern in Lucknow which offers an online marketplace for best buying and selling supply goods. Gramophone in Indore provides chatbots for farmers, which help farmers to have better crop cycles. Jivabhumi, Bengaluru uses blockchain technology to bridge the gap between farmers and consumers. Digital farming is adopted in the states of Gujarat, Maharashtra, and Rajasthan by Agrostar, Pune, which helps farmers 24/7 to know the crop requirements along with required services [26].

      According to the NASSCOM report 2019, smart farming is predicted to reach 23.14 billion dollars by 2022. The following are some of the smart farming applications:

       precision farming uses IoT sensors in the field, and farmers can view the data through mobile applications;

       agricultural drones manage large farms using airborne sensors, which are capable of collecting thermal and visual data;

       livestock monitoring helps to detect sick animals;

       smart greenhouses use solar-powered sensors, which help the farmer remotely turn on/off the lights, fan, monitor temperature, and humidity, and so on;

       smart irrigation uses a sprinkler system along with an alert system;

       farm management system for collecting and storing sensor data in the cloud and access through various devices and applications;

       apart from soil health monitoring, optimal seed sowing, water management, and weed management, AI also helps in identifying plant diseases.

      1.6.1 Sensors for Smart Farming

Sensors collect data from crops, livestock, soil, and the atmosphere. The sensor data are supplied to a cloud-based IoT platform with preprogrammed decision rules and models that determine the status of the studied object and highlight any flaw or requirement. Following the discovery of difficulties, the IoT platform’s user and/or ML-driven components assess whether and which location-specific treatments are required. СКАЧАТЬ