Machine learning has been successfully applied to big data analytics across various disciplines. However, as data is collected from diverse sectors, much of it is private and confidential. At the same time, one of the major challenges in machine learning is the slow training speed of large models, which often requires high-performance servers or cloud services. To protect data privacy while still allowing model training on such servers, privacy-preserving machine learning using Fully Homomorphic Encryption (FHE) has gained significant attention. However, its widespread adoption is hindered by performance degradation. This paper presents our experiments on training models over encrypted data using FHE. The results show that while FHE ensures privacy, it can significantly degrade performance, requiring complex tuning to optimize.

In the realm of precision agriculture, a crucial element involves the precise quantification or estimation of seedlings, fruits, and other agricultural produce on expansive multi-acre farms at various stages of cultivation. With the advent of unmanned aerial vehicles (UAVs), capturing images of watermelon fields has become a straightforward task. These images can be subsequently processed, segmented, and categorized to determine the total count of watermelons. Currently, conventional methods are employed to address this challenge, but they have their limitations. The field has benefited from the evolution of machine learning, which has the potential to streamline the process. Nevertheless, the training phase is intricate, and achieving a valuable model can be demanding. This research delves into an examination and presentation of the existing pre-trained models for image processing in this context.