Aid System for Estimating Agricultural Yield Using a Deep Learning Technique: Tomato Case
Tahirou Djara
*
Université d’ Abomey-Calavi, Benin.
Sekoude Jehovah-nis Pedrie Sonon
Institut d’Innovation Technologique, Benin.
Aziz Sobabe
Institut d’Innovation Technologique, Benin.
Abdul-Qadir Sanny
Institut d’Innovation Technologique, Benin.
*Author to whom correspondence should be addressed.
Abstract
The precision of traditional methods for estimating crop yield is a major challenge, particularly for large areas. To improve this process, we developed a tomato detection and localization system using deep learning techniques. The system uses Faster-RCNN, a cutting edge technology of object detection model, to detect and localize tomatoes in images. We trained the model on a database of 150 images, which were normalized to 100*100 pixels in RGB. The system estimates the real sizes of tomatoes using the Ground Sampling Distance method and predicts their masses using a regression model. The model produces an average absolute error of 42.365% and a quadratic error of 51.044%. Our system provides a more efficient and accurate way to estimate tomato crop yields on a large scale.
Keywords: Tomato, object detection, convolutional neural networks, deep learning, drone, agricultural yield, precision agriculture, ground sampling distance, regression model, faster-RCNN