Performance Analysis of Fruits Classification System Using Deep Learning Techniques

In agriculture sector the problem of identification and counting the number of fruits on trees plays an important role in crop estimation work. At present, manual counting of fruits and vegetables is carried out. Manual counting has many drawbacks as it is time consuming and is labor intensive. The automated fruit counting approach can help crop management system by providing valuable information for forecasting yields or by planning harvesting schedule to attain more productivity. This work presents an automated fruit maturity detection and fruit counting system using image processing. But, it is now a critical role with image processing and using computer vision to identify and recognize fruits in real time. This research introduces an integrated and effective technique for fruit detection and recognition running on an embedded system in a neural compute stick (NCS) Movidius at fast frames per second (FPS). This can be achieved by applying in-depth computer vision training. Using deep learning techniques and OpenCV libraries, suggested fruit detecting through live streaming. This includes the MobileNet software single-shot detector (SSD) algorithm that is equipped by Caffe system. Raspberry Pi 3 has been used in this paper to implement this program. This helps to track and record the images, identifying and recognizing the fruits. We use neural compute stick Movidius that is used to achieve high FPS with the Raspberry Pi 3. The proposed method introduces several improvements, such as multi-scale functionality, default boxes, and deeply divisible convolution. Such enrichments allow achieving excellent accuracy in fruit recognition.