Introduction to deep learning in precision agriculture: Farm image feature detection using unmanned aerial vehicles through classification and optimization process of machine learning with convolution neural network

With the increasing demand for maximizing efficiency in food production, technology can offer and perform farm operations for yield optimization. Modern agriculture can automate the entire process of cultivation from land preparation and preplanting to postharvest processes through data collection and processing. It implies that the analysis of big agricultural/farm data can be performed with the use of new information and communication technologies. This chapter applies precision agricultural technology with an unmanned aerial vehicle, embedded with optic and radiometric sensors, to obtain high spectral resolution images of a plantation’s status during a normal production/growth cycle. Then, the convolution neural network deep learning technique is employed to train images to develop an end-to-end multiclass classification system to determine the plant’s overall health status. Applying the pretrained model to the new images showed that the model was accurately able to predict any plant condition with an average of 99% accuracy.