Agriculture plays an important role in the economic sector, and with the population increasing tremendously, so is the demand for food and employment. Traditional methods used by farmers were not sufficient to meet these requirements, so new automated methods were introduced. Artificial intelligence (AI) in agriculture has meant an agricultural revolution, protecting crop yields from several factors such as climate change, population growth, employment problems and food security issues. This article will audit the various applications of AI in agriculture, such as irrigation, weeding, fumigation with the help of sensors and other means integrated into robots and drones.AI systems help improve the overall quality and accuracy of the harvest, known as precision agriculture.
AI technology helps detect plant diseases, pests and poor nutrition on farms. AI sensors can detect and attack weeds and then decide which herbicide to apply in the region. These technologies save the excessive use of water, pesticides and herbicides, maintain soil fertility, also help the efficient use of labor and increase productivity and improve quality. Automation in agriculture is a main concern and an emerging topic around the world. Video surveillance systems with AI and machine learning scale as easily for a large scale agricultural operation as they are for an individual farm.
The choice of effective combined parameters at the processing stage results in a high quality and quantity of the food product and, at the same time, avoids the overuse of resources. At Microsoft, FarmBeats enables data-based precision agriculture by using sensors and drones to precisely target pesticides and water where they are needed. The adoption of pesticides, fertilizers and high-yield crop varieties, among other measures, transformed agriculture and guaranteed a secure food supply for many millions of people for several decades. Using data from infrared drone cameras combined with sensors in the fields that can monitor the relative health levels of plants, agricultural teams using AI can predict and identify pest infestations before they occur. The World Economic Forum has placed AI and AI-powered agricultural robots (called “agbots”) at the forefront of the fourth agricultural revolution. As for unsupervised learning, it includes algorithms such as artificial neural networks (ANN), clustering, genetic algorithms and deep learning, which use unlabeled data sets without prior knowledge of input and output variables. AI provides more efficient ways to produce, harvest and sell agricultural products, in addition to focusing on controlling defective crops and improving the potential of healthy agricultural production.
In addition, AI is used in applications such as automated machine settings for weather forecasting and the identification of diseases or pests with an accuracy of 98%.The sophistication of agricultural robots has grown rapidly; an example is shown on the control panel of the VineScout robot in use. By helping humans in fields and factories, AI can constantly process, synthesize and analyze large amounts of data. It can surpass humans in detecting and diagnosing anomalies such as plant diseases, as well as making predictions about performance and climate. Due to future challenges for the agricultural and food sector as well as various factors such as climate change, population growth, technological progress and natural resources (water), artificial intelligence can be used to revolutionize agriculture and food production.
