Several drought indices are available to compute the degree of drought to which crops are exposed. Th ey vary in complexity, generality, and the adequacy with which they represent processes in the soil, plant, and atmosphere. Agricultural Reference Index for Drought (ARID) was developed as a reference index to approximate the water stress factor that is used to aff ect growth and other physiological processes in crop simulation models. Using RMSE, Willmott d index, and modeling effi ciency (ME) as performance measures, ARID was evaluated using soil water contents in the root zone measured daily in two grass fi elds in Florida. Th e ability of ARID was assessed through comparison with the water defi cit index (WSPD) of the Decision Support System for Agrotechnology Transfer (DSSAT) CERES-Maize model. Seven other drought indices were compared with WSPD to identify the most appropriate agricultural drought index. Values of each index were computed for full canopy cover periods of maize (Zea mays L.) crops for 16 locations in the U.S. Southeast. Using periodic values, the performance of each index was assessed in terms of its correlation (r) with and departure from WSPD. Th e ARID reasonably predicted soil water contents (RMSE = 0.01-0.019, d index = 0.92-0.94, ME = 0.66-0.73) and adequately approximated WSPD (r = 0.90, RMSE = 0.15). Among the indices compared, ARID mimicked WSPD the most closely (RMSE smaller by 1-83%, r larger by 1-630%) and captured weather fl uctuation eff ects the most accurately. Results indicated that ARID may be used as a simple index for quantifying drought and its eff ects on crop yields.

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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.

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2016-11-03T14:11:40

openAccessArticle: Truecover date: 2015-01-01pii: S2212-0963(15)00005-4Harvest Date: 2016-01-06 13:07:50issueName:Page Range: 11-11href scidir: http://www.sciencedirect.com/science/article/pii/S2212096315000054pubType

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