MTech

Black pepper is a perennial crop and one of India's most economically
significant spices. 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 on supporting
stakes. The berries turn 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 correct maturity stage. Generally, black pepper spikes were
harvested manually by climbing on supporting trees using bamboo poles. It is a
tedious task because there are chances of falling from ladders while harvesting and
also causes some musculoskeletal diseases to the labours. For their time saving and
heavy work intensity, farmers harvest almost all the fruits in a range of maturity
along with the real matured ones. This practice eventually affects the crop yield and
quality. Through robotic harvesting, black pepper spikes can be harvested at correct
maturity and also helps to overcome the difficulties faced by the labours. The main
functions of robotic harvesting are identification, plucking, depositing, and
controlling. KAU developed a machine vision system with the camera as sensor,
Raspberry pi 4 model B as the processor, and LCD as the display unit to identify
matured black pepper spikes. The programing code was written in python language,
and the Tensorflow-faster RCNN platform was used for the detection. Hence, a
robotic black pepper harvesting system was developed in the present study, and its
performance evaluation was carried out.
The physical properties of black pepper relevant to design and develop a
robotic black pepper harvesting system were determined. The developed robotic
black pepper harvesting system consists of a machine vision system to identify
matured black pepper spikes, a manipulator with 2 DOF, an end-effector with 1
DOF, and a control unit. Servo motors actuated the shoulder and elbow joints of the
manipulator and the cutting blades. Shear-type cutting was employed for detaching
pepper spikes from the pepper vine. The entire system was controlled by the
microprocessor Raspberry pi 4 Model B. For controlling the servo motors, the
library RPi.GPIO was installed on raspberry pi, and the programming code was
180
written in python language. Two lead-acid batteries with a voltage of 12 V and a
current 9Ah were connected in parallel to power the entire system. The overall
dimension of the developed unit was 59 × 18 × 162 cm, and it weighs 2.1 kg.
The performance evaluation parameters of the machine vision system viz.,
sensitivity, specificity, and accuracy were respectively as 85 %, 77 %, and 82 % in
Karimunda variety and 84 %, 77 %, and 82 % in Panniyur 1 variety. Time taken
for detection is 0.43 seconds. Also, the capacity of the developed robotic black
pepper harvesting system is 3.5 kg h-1
and 562 spikes h-1
in the Karimunda variety,
whereas 4.6 kg h -1
and 683 spikes h -1
in Panniyur 1 variety. The effectiveness
index, time taken for the entire operation, harvesting loss, and drying loss was 81%,
6.6 seconds, 4.9 %, and 39 % in the Karimunda variety and 82 %, 6.3 seconds, 7%,
and 66 % in Panniyur 1 variety respectively. The system takes 0.18 seconds for a
single cut for both varieties; it was fixed in the program.
A study was also carried out for manual harvesting and found that manual
harvesting has a capacity of 1052 spikes h-1
and 6.3 kg h -1
in the Karimunda variety
and 1654 spikes h -1
and 10.8 kg h -1
in the Panniyur 1 variety, which is higher than
robotic harvesting. The effectiveness index of the manual harvesting was 40% in
Karimunda and 38 % in Panniyur 1, which is lower than robotic harvesting. The
harvesting loss and drying loss of manual harvesting are 15.3 % and 56 % in
Karimunda and 17.5 % and 81 % in Panniyur 1, which is higher than robotic
harvesting. It was statistically verified and found a significant difference between
manual and robotic harvesting in terms of capacity, effectiveness index, harvesting
loss, and drying loss at a 5 % level of significance.