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A Survey Of Agriculture Crop Monitoring Using IOT Based Image Processing And Machine Learning Techniques

Accurate crop disease prediction and continuous monitoring is essential in agriculture to improve the crop yield. Internet of Things (IoT) are being used in developing decision support systems for traditional farming methods to optimize disease estimation and yield estimation. Traditionally, a large numbers of statistical and scientific models have been implemented to monitor and predict the crop yield estimation in the agriculture fields. However, most of these models are limited to small and fixed number of agriculture characteristics. Image processing and machine learning approaches are used to predict the disease on agricultural crops using IoT devices. As the size of the training images and features increases, these approaches are incorporate to find and analyze the crop yield in agriculture field. In this paper, we have studied and analyzed various agricultural crop monitoring approaches using IoT based image and machine learning techniques. IoT based image processing and machine learning approaches are necessary for the process of agricultural crop monitoring. Furthermore, we have also stu-died the advantages and limitations of these approaches on complex crop data.