Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline [1]. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net [2], FCN [3], and Mask- RCNN [4] were popularly used, typically based on ResNet [5] or VGG [6] base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

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Generally, the rate of technological advancement is increasing with time. Specifically, the technologies that are the building blocks of Farming 4.0 are now advancing at a rapid pace never witnessed before. In this chapter, the authors study the advances of major core technologies and their applicability to creating a smart farm system. Special emphasis is laid on cost of the technology; for, expensive technology will still keep small farmers at bay as major population of farmers inherently are new to technology, if not averse. The authors also present the pros and cons of alternatives in each of the subsystems in the smart farm system.

Deep learning opens up a plethora of opportunities for academia and industry to invent new techniques to come up with modified or enhanced versions of standardized neural networks so that the customized technique is suitable for any specialized situations where the problem is about learning a complex mapping from the input to the output space. One such situation lies in a farm with huge cultivation area, where examining each of the plant for any anomalies is highly complex that it is impractical, if not impossible, for humans. In this chapter, the authors propose an optimized deep learning architectural model, combining various techniques in neural networks for a real-world application of deep learning in computer vision in precision farming. More precisely, thousands of crops are examined automatically and classified as healthy or unhealthy. The highlight of this architecture is the strategic usage of spatial and temporal features selectively so as to reduce the inference time.

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