The Agricultural industry is always at risk - crops are affected by weather, diseases and pests. When a global pandemic hits suddenly, it becomes very difficult to manage various processes because most are not digital. In parallel, the rapid increase in the population and urbanization, demands more food production on less land. Farming under the demand pressure by increasing input consumption, leads to increases costs and negative impacts to the environment such as decreasing soil fertility continuously over time. There is therefore the need to move beyond traditional farming. To produce more yield in the agricultural field, there is the need to dig deep into the technological field and apply sensors, Internet of Things (IoT), big data analytics, cloud computing and machine learning (ML) and deep learning (DL) techniques. Better decision making, prediction and reliability depend upon high level of knowledge base and perceptions.
Tradition methods used by the farmers are not sufficient to fulfil the requirements of modern agriculture. Agricultural work is hard, and labour shortages in this industry are nothing new. Farmers can solve this problem with the help of automation. Driverless tractors, smart irrigation and fertilizing systems, smart spraying, vertical farming software, and AI-based robots for harvesting are some examples of how farmers can get the work done without having to hire more people. Compared with any human farm worker, AI-driven tools are faster, harder, and more accurate. Thus, Artificial Intelligence has emerged as a significant approach for resolving a wide range of agricultural challenges due to its ability to learn quickly. Artificial Intelligence (AI) has the promise to revolutionizing agriculture output through improved management, decision making processes and increase in productivity and farm profitability. Considering the benefits of artificial intelligence for sustainable farming, the various applications of artificial intelligence in agriculture such as Analyzing market demand, managing risk, breeding seeds, monitoring soil health, irrigation, fertilization, weeding, insecticides protecting crops, feeding crops, spraying and harvesting with the help of sensors and other means embedded in robots and drones. These technologies saves the excess use of water, pesticides, herbicides, maintains the fertility of the soil, also ensures the efficient use of man power, elevate productivity and improve the quality. This research topic welcomes Reviews, Mini review or data report that contribute to the implementation of AI application for improving crop production in the following fields
1. AI for automation and robotics for field work
Synthesis of studies related to AI autonomous tractors, Agriculture robotics for surveying, AI drone seeders, AI fruit and vegetable picking harvester, AI drone sprayer, AI pruner etc.
2. AI for IoT sensors for capturing data and analyzing data
Synthesis of studies related to IoT sensors technology, Drone, GIS and Remote Sensing
3. AI for big data for informed decision-making
Synthesis of studies related to data analytics on real time information on crop need including farming practices such as irrigation, fertilization, weed detection, crop disease and pest detection and protection, and harvesting and yield monitoring
The Agricultural industry is always at risk - crops are affected by weather, diseases and pests. When a global pandemic hits suddenly, it becomes very difficult to manage various processes because most are not digital. In parallel, the rapid increase in the population and urbanization, demands more food production on less land. Farming under the demand pressure by increasing input consumption, leads to increases costs and negative impacts to the environment such as decreasing soil fertility continuously over time. There is therefore the need to move beyond traditional farming. To produce more yield in the agricultural field, there is the need to dig deep into the technological field and apply sensors, Internet of Things (IoT), big data analytics, cloud computing and machine learning (ML) and deep learning (DL) techniques. Better decision making, prediction and reliability depend upon high level of knowledge base and perceptions.
Tradition methods used by the farmers are not sufficient to fulfil the requirements of modern agriculture. Agricultural work is hard, and labour shortages in this industry are nothing new. Farmers can solve this problem with the help of automation. Driverless tractors, smart irrigation and fertilizing systems, smart spraying, vertical farming software, and AI-based robots for harvesting are some examples of how farmers can get the work done without having to hire more people. Compared with any human farm worker, AI-driven tools are faster, harder, and more accurate. Thus, Artificial Intelligence has emerged as a significant approach for resolving a wide range of agricultural challenges due to its ability to learn quickly. Artificial Intelligence (AI) has the promise to revolutionizing agriculture output through improved management, decision making processes and increase in productivity and farm profitability. Considering the benefits of artificial intelligence for sustainable farming, the various applications of artificial intelligence in agriculture such as Analyzing market demand, managing risk, breeding seeds, monitoring soil health, irrigation, fertilization, weeding, insecticides protecting crops, feeding crops, spraying and harvesting with the help of sensors and other means embedded in robots and drones. These technologies saves the excess use of water, pesticides, herbicides, maintains the fertility of the soil, also ensures the efficient use of man power, elevate productivity and improve the quality. This research topic welcomes Reviews, Mini review or data report that contribute to the implementation of AI application for improving crop production in the following fields
1. AI for automation and robotics for field work
Synthesis of studies related to AI autonomous tractors, Agriculture robotics for surveying, AI drone seeders, AI fruit and vegetable picking harvester, AI drone sprayer, AI pruner etc.
2. AI for IoT sensors for capturing data and analyzing data
Synthesis of studies related to IoT sensors technology, Drone, GIS and Remote Sensing
3. AI for big data for informed decision-making
Synthesis of studies related to data analytics on real time information on crop need including farming practices such as irrigation, fertilization, weed detection, crop disease and pest detection and protection, and harvesting and yield monitoring