Molecular Plant Breeding
Mojtaba Khayam Nekouei; Mohammad Reza Ghaffari; Mohsen Mardi; Zahra Ghorbanzadeh; Rasmieh Hamid; Mehrshad Zeinalabedini
Abstract
Today, using advanced technologies such as the global positioning system (GPS), agricultural drones, satellite mapping, remote sensors, and precision agriculture machinery provides farmers with a lot of big data during production. According to the reports, this can be considered a part of the digital ...
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Today, using advanced technologies such as the global positioning system (GPS), agricultural drones, satellite mapping, remote sensors, and precision agriculture machinery provides farmers with a lot of big data during production. According to the reports, this can be considered a part of the digital economy in precision agriculture and be economically exploited. The analysis of this data cannot be processed by traditional processing systems due to its complexity. Given the size and complexity of big data, artificial intelligence can transform this data into valuable information through machine learning algorithms. This technology is being used to performance prediction algorithms, reducing agricultural inputs such as fertilizers and poisons, monitoring the growing conditions, pest management, breeding, molecular studies and finally value chain management. Developing programs using artificial intelligence technology will soon be able to manage the time of agricultural products entering the market, in addition to determining the planting time in order to increase productivity. The production of bio fertilizer from agricultural waste can be another achievement of the development of algorithms based on artificial intelligence to reduce the negative environmental effects and increase the economic productivity of the remaining waste from agricultural products. This study discusses the important applications of artificial intelligence in agriculture and its impact on Precision agriculture.
