Real-time yield estimation based on deep learning

Maryam Rahnemoonfar; Clay Sheppard

doi:10.1117/12.2263097

8 May 2017 Real-time yield estimation based on deep learning

Maryam Rahnemoonfar, Clay Sheppard

Proceedings Volume 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II; 1021809 (2017) https://doi.org/10.1117/12.2263097
Event: SPIE Commercial + Scientific Sensing and Imaging, 2017, Anaheim, CA, United States

Abstract

Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.

Citation Download Citation

Maryam Rahnemoonfar and Clay Sheppard "Real-time yield estimation based on deep learning", Proc. SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, 1021809 (8 May 2017); https://doi.org/10.1117/12.2263097

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