### Benefits of Object Detection for Apple Orchards  
The current issue in the agriculture field is that most of the machinery currently being used requires worker supervision [15; 16; 17]. The inclusion of automated machinery can greatly increase the yield produced compared to manual labour and lessen the load required as it is a very labour-intensive job [18; 19; 20]. Another benefit is that object detection allows for more information to be presented or collected at a faster rate. For each experiment, the number of fruit flowers is needed to understand the impact that the adjustments have made on the apple tree yield. This process can take a very long time based on the type of apple tree due to the significant number of fruit flowers or apples present. The authors in [6] experimented on the effects of pollination by covering some of the branches of an apple tree. To determine the benefits of pollination, the number of fruit flowers were counted and then compared to the amount fruits yielded by that branch [6]. It took approximately 10 min to observe and count two branches. With two observations per tree, each tree needed 20 min to fully count the fruit flowers. The authors also stated that during bloom period, each orchard was visited twice in two different observation periods [6]. Depending on the number of trees needed to be counted, the process would be excruciatingly long and tedious. With object detection, this process can be brought down to just 46 s per frame to detect an image which was obtained using the two-stage detector, Faster R-CNN [5]. The inclusion of automation can drastically increase the efficiency of the operation as well as the quality. The authors in [8; 19; 21] showed multiple computation speeds of different detection algorithms for an object detection application in agriculture with heavily occluded objects where the speeds ranged from the fastest (23 ms) to the longest (348 ms) [8]. The authors in [22] showed that detection algorithms were capable of obtaining an accuracy of 99.5% for segmentation and 95% for classification for almonds while having a computation time of less than 50 ms. There have been many different object detection algorithms used in the process of implementing autonomous operations for agriculture, with each algorithm having benefits for different applications and parameters. A common algorithm that is used is the two-stage detector, Faster R-CNN [17; 23; 24]. It can produce very accurate detections which is important in developing an efficient process [25]. Though the detection speed is not as fast compared to one-stage detectors, the purpose of the operation was to correctly identify the fruits and or branches in the orchard. Ref. [17] used Faster R-CNN to design a shake-and-catch robot for apples. The algorithm was trained to differentiate between the apples,leaves and branches and then locate the optimal location on the branch to have a robotic arm grab the branch. The authors in [23; 24] both used Faster R-CNN to detect multiple different fruits and other produce on a tree, such as apples, mangoes, almonds, peppers, etc. They both were successful in obtaining accurate results with minor errors, especially in cases where less data was available [24] or with the detection of smaller objects [23]. The authors in [15] used MobileNet and a single-state detector (SSD) for the detection of tomatoes. MobileNet was used to implement the model on a smaller device, specifically the Raspberry Pi, which MobileNet allows due to having a low computational cost. SSD was selected for fast detection rate, while having an appropriate level of accuracy. Two tests were run, the first being object detection of tomatoes based on sample images and the second using a robotic arm to identify the location of the tomatoes and grabbing them [15]. The results produced a mean accuracy of 84% for the detection and 89% for the robotic arm successfully grabbing the fruit [15].