Development of a machine vision system to identify matured pepper spikes
Meera T
Development of a machine vision system to identify matured pepper spikes - Tavanur Department of Farm Machinery and Power Engineering, Kelappaji College of Agricultural Engineering and Technology 2020 - 98p.
M Tech
Black pepper is a perennial crop and one of the most economically significant spices in
India. It has a high commercial value in the market all around the world. Its fruit is
harvested, dried and powdered for many cuisines and processed for many value added
products. Black pepper is a flowering vine growing up to 4 m in height. The berries
turns from green to red on maturity and are harvested when it starts to turn red. For
achieving good quality and good sized pepper, it should be harvested at its proper
matured state. Farmers for their time saving and due to heavy work intensity, harvest
almost all the fruits which are in a range of maturity along with the real matured ones.
This eventually affects the crop yield and quality. Hence employing an automated
identification system in this case would be effective. An application programme
interface was developed for this, using the fruit features like the shape, colour and size.
By using the machine learning techniques and computer vision technology, two
programmes were developed in python language, one using OpenCV library and Haar
Cascade classifier, and other platform with TensorFlow as library and faster-RCNN as
classifier. Studies were also carried out to analyse the physical properties of black
pepper. Using image acquisition, a dataset was created and was used for training and
preparation of both the models. The hardware part of the system comprised of a webcam
as sensor, Raspberry Pi processor, a RPI display unit and some accessory parts. The
hardware and software parts were installed and assembled, and subjected to
performance evaluation. It was revealed that the Tf-RCNN platform had better
performance and efficiency. The performance evaluation parameters viz., sensitivity,
specificity and accuracy values were 78%, 71% and 75% respectively for the second
model. It was statistically verified that there is a significant difference between the two
platforms and the second model had better consistency.
Farm Power and Machinery
Pepper spikes
Pepper harvester
Open CV- Haar cascade platform
Tf- RCNN platform
631.3 / MEE/DE PG
Development of a machine vision system to identify matured pepper spikes - Tavanur Department of Farm Machinery and Power Engineering, Kelappaji College of Agricultural Engineering and Technology 2020 - 98p.
M Tech
Black pepper is a perennial crop and one of the most economically significant spices in
India. It has a high commercial value in the market all around the world. Its fruit is
harvested, dried and powdered for many cuisines and processed for many value added
products. Black pepper is a flowering vine growing up to 4 m in height. The berries
turns from green to red on maturity and are harvested when it starts to turn red. For
achieving good quality and good sized pepper, it should be harvested at its proper
matured state. Farmers for their time saving and due to heavy work intensity, harvest
almost all the fruits which are in a range of maturity along with the real matured ones.
This eventually affects the crop yield and quality. Hence employing an automated
identification system in this case would be effective. An application programme
interface was developed for this, using the fruit features like the shape, colour and size.
By using the machine learning techniques and computer vision technology, two
programmes were developed in python language, one using OpenCV library and Haar
Cascade classifier, and other platform with TensorFlow as library and faster-RCNN as
classifier. Studies were also carried out to analyse the physical properties of black
pepper. Using image acquisition, a dataset was created and was used for training and
preparation of both the models. The hardware part of the system comprised of a webcam
as sensor, Raspberry Pi processor, a RPI display unit and some accessory parts. The
hardware and software parts were installed and assembled, and subjected to
performance evaluation. It was revealed that the Tf-RCNN platform had better
performance and efficiency. The performance evaluation parameters viz., sensitivity,
specificity and accuracy values were 78%, 71% and 75% respectively for the second
model. It was statistically verified that there is a significant difference between the two
platforms and the second model had better consistency.
Farm Power and Machinery
Pepper spikes
Pepper harvester
Open CV- Haar cascade platform
Tf- RCNN platform
631.3 / MEE/DE PG