Fruit ripeness detection using deep learning

Abstract—The agricultural industry has been facing challenges in traditional and manual visual grading of fruits due to its laborious nature and inconsistent inspection and classification process. To accurately estimate yield and automate harvesting, it is crucial to classify the fruits based on their ripening stages. However, it can be difficult to differentiate between the ripening stages of the same fruit variety due to high similarity in their images during the ripening cycle. To address these challenges, we plan to develop an accurate, fast, and reliable fruit detection sys- tem using deep learning techniques. The modernization of crops offers opportunities for better quality harvests and significant cost savings. Our approach involves adapting the state-of-the- art object detector faster R-CNN, using transfer learning, to detect fruits from images obtained through modalities such as colour (RGB) and Hyper Spectral Imaging System (HSI). Our system’s robustness will enable us to differentiate between fruit varieties and determine the ripening stage of a particular fruit with effectiveness and accuracy. The system will also efficiently segment multiple instances of fruits from an image and accurately grade individual objects. Index Terms—Modernisation, HSI, CNN, RGB