Exploring the Role of Computer Vision in Non-Destructive Gender Detection for Agriculture

Abstract

Gender detection in plants forms an important aspect of the agriculture industry and cannot be underrated in crop producing activities. Conventional ways of gender identification, which might be done manually where a person observes the gender manually, are time consuming and may end up with errors, especially during the initial stages of plant development. This paper examines the use of computer vision so as to achieve non-destructive gender classification in agricultural crops. We discuss how the deep learning models specifically the convolutional neural networks (CNNs) could be used to classify the gender of plants using visual information. We will review the recent progress in machine learning and computer vision to study the current approaches and introduce a new technology based on huge datasets of plant images. We found that CNNs are able to attain good classification rates that were not reached in the gender-detection tasks before. The limitations of models also present a problem and these are discussed in the study especially on how to handle environmental variations as well as image quality. Concluding, the provided research proves that computer vision holds the promises of transforming the detection of gender in agriculture, which would have relevance to crop management and automated farming. Future research should deal with making the models more robust and changing the data to such an extent as to allow generalizability of the models to be increased.

Keywords: Computer vision, gender detection, agriculture, deep learning, solution,