Automated harvesting decision system
Introductin
The harvesting decision of date fruits depends on several factors such as the date type, maturity stage, harvesting methods (i.e. selective harvesting and bunch-based harvesting, the latter method is used in large orchards for commercial production), and many external factors (e.g. climatic conditions, market demand, etc.).
The decision to harvest date fruit depends on:
Challenges
Date’s orchards usually have many varieties of dates with a lot of similarities on their visual appearance and these varieties have different harvesting decisions.
Proposed solution:
The proposed system determines the decision to harvest date bunches in two steps. First, the user enters the required harvesting stage for each date type, according to the climatic conditions, market demand, and so on. Next, the system automatically recognizes the types (varieties) of date bunches in the orchard and defines their maturity stages, then making the harvesting decision. The harvesting decision system uses deep convolutional neural networks CNNs to recognize the type and maturity stage of dates.
Block diagram of the automated harvesting decision system for date fruits.
Referred paper:
H. Altaheri, M. Alsulaiman and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", in IEEE Access, vol. 7, pp. 117115-117133, 2019. (10.1109/ACCESS.2019.2936536)
Dataset download links: (You have to login with an IEEE Account to download the files. IEEE Account is FREE)
High resolution images (8079 images, 42 GB).
A preview of the 8079 images. size: 224 X 224. (108 MB).
The annotation (labeling) files (104 KB)
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