Technologies to Improve Crop Productivity Using Iot and Ai


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The aim of smart farming is to make every feature of farming more consistent, foreseeable, and supportable. The role of the different agri-technologies is to close this information gap by providing accurate measurements of the aspects that regulate farming consequences. Smart Farming is characterized by the application of contemporary information and communication technologies (ICT) into farming, foremost to what can be termed a Third Green Revolution. Following the mutinies in plant breeding and genetics, the Third Green Revolution is enthralling the farming domain through the mutual solicitation of ICT resolutions such as precision apparatus, the Internet of Things (IoT), sensors, geo-positioning systems, Big Data, and robotics. Artificial Intelligence (AI) based technologies help to improve efficiency in all the fields and also manage the challenges faced by various industries, including the various fields in the agricultural sector like crop yield, irrigation, soil content sensing, crop monitoring, weeding, and crop establishment. The proposed systems help to improve the overall harvest quality and accuracy – known as precision agriculture. AI technology helps in detecting disease in plants, pests and poor nutrition in farms. AI sensors can detect and target weeds and then decide which herbicide to apply within the region. The system will traverse the fields and work autonomously to respond to the needs of crops, and perform weeding, watering, pruning, and harvesting functions guided by their own collection of sensors, navigation, and crop data. Furthermore, this work surveys the work of many researchers to get a brief overview of the current implementation of automation in agriculture, specifically the weeding systems through robots and drones. The various soil water sensing methods are discussed, along with two automated weeding techniques. The implementation of drones is being discussed. The various methods used by drones for spraying and crop-monitoring are also discussed in this work. The main concern of this work is to audit the various applications of artificial intelligence in agriculture, such as irrigation, weeding, and spraying, with the help of sensors and other means embedded in robots and drones. These technologies save the excess use of water, pesticides, and herbicides, maintain the fertility of the soil, help with the efficient use of manpower, and improve the quality. Additionally, screening tests are performed to detect potential health disorders or diseases in people who do not have any symptoms of disease. The goal is early detection and lifestyle changes or surveillance, to reduce the risk of disease, or to detect it early enough to treat it most effectively. Plant leaves can be used to effectively detect plant diseases. However, the number of images of unhealthy leaves collected from various plants is usually unbalanced. However, it is difficult to detect diseases using such an unbalanced dataset as that described in this work.