AI-INTEGRATED PLANT DISEASE SCANNING APPLICATIONS: A SYSTEMATIC LITERATURE REVIEW
Keywords:
AI, plant, machine learning, scan, agriculture, plant , diseaseAbstract
The evolution of digital technology has revolutionized the modern agricultural landscape, gradually incorporating the detection and management of plant diseases. In this work, we aimed to provide a systematic review addressing the state-of-the-art regarding the application of plant disease scanning embedded in artificial intelligence, subsistent from the newest literature as possible. The literature reviews from multiple sources revealed that Big Data and machine learning algorithms like Convolutional Neural Network (CNN) enhance the efficiency of plant disease identification. It has also been proven that integrated scanning systems in combination with the Internet of Things are able to monitor the conditions of plants in real time and provide accurate management recommendations. The integration of AI, IoT, and data analytics provide smart plant to achieve faster, effective, and accurate plant health management data mining through machine-learning methods.
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