Abstract
Strawberry growers in Florida suffer from a lack of efficient and accurate yield forecasts for strawberries, which would allow them to allocate optimal labor and equipment, as well as other resources for harvesting, transportation, and marketing. Accurate estimation of the number of strawberry flowers and their distribution in a strawberry field is, therefore, imperative for predicting the coming strawberry yield. Usually, the number of flowers and their distribution are estimated manually, which is time-consuming, labor-intensive, and subjective. In this paper, we develop an automatic strawberry flower detection system for yield prediction with minimal labor and time costs. The system used a small unmanned aerial vehicle (UAV) (DJI Technology Co., Ltd., Shenzhen, China) equipped with an RGB (red, green, blue) camera to capture near-ground images of two varieties (Sensation and Radiance) at two different heights (2 m and 3 m) and built orthoimages of a 402 m2 strawberry field. The orthoimages were automatically processed using the Pix4D software and split into sequential pieces for deep learning detection. A faster region-based convolutional neural network (R-CNN), a state-of-the-art deep neural network model, was chosen for the detection and counting of the number of flowers, mature strawberries, and immature strawberries. The mean average precision (mAP) was 0.83 for all detected objects at 2 m heights and 0.72 for all detected objects at 3 m heights. We adopted this model to count strawberry flowers in November and December from 2 m aerial images and compared the results with a manual count. The average deep learning counting accuracy was 84.1% with average occlusion of 13.5%. Using this system could provide accurate counts of strawberry flowers, which can be used to forecast future yields and build distribution maps to help farmers observe the growth cycle of strawberry fields.
Highlights
Strawberries are a high-value crop in the economy of Florida
We presented a deep learning strawberry flower and fruit detection system, based on high resolution orthoimages reconstructed from drone images
The system could be used to build yield estimation maps, which could help farmers predict the weekly yields of strawberries and monitor the outcome of each area, in order to save their time and labor costs
Summary
Based on a report from the U.S Department of Agriculture, the value of production for strawberries in Florida was $282 million in 2018, the second-largest in the United States [1]. The strawberry harvest season runs from December to April and, during this time, flowers form and become fruit in subsequent weeks. In the main production areas in central Florida, the mean daily temperature is 25 ◦C in early November, declining to 15 ◦C in the middle of January and rising to 21 ◦C in late April [3]. The day lengths and temperatures are conducive to flower bud initiation [4,5]. In Florida, the fruit development period typically extends from three weeks to six weeks as the day length declines and temperatures sink below the average [6,7]
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