Automatic Arecanut Harvester

Abstract:

Manual harvesting of arecanut (betel nut) palms is slow, dangerous, and costly . We propose a quadcopter-based harvester: a small drone carrying a cutting blade, controlled by an STM32 microcontroller. The STM32 reads an MPU‑9250 IMU (accelerometer/gyro) to estimate attitude, and a PID controller maintains a stable hover A 2.4 GHz nRF24L01 radio link lets an operator control the throttle and steering remotely. In prototype tests, the UAV hovered steadily and successfully severed arecanut bunches from tree branches. This electric drone significantly reduces human labor and hazards, although limited battery life and payload remain constraints. Future work will add autonomous navigation and higher-capacity batteries to enable longer, self-guided harvesting missions.

1. Introduction

Arecanut is a major plantation crop in southern India, but harvesting it is “dull and dangerous.” Workers must climb tall, smooth palm trunks with ropes and sickles, risking falls, injury, and labour shortage. Aerial robots (drones) offer a promising remedy. By flying to the nut clusters, a UAV can collect data or carry tools without endangering people. In agriculture, drones already optimize operations and improve yields: they survey fields, spray chemicals much faster than ground crews. Motivated by these benefits, this project’s objective is to design a quadcopter UAV that can detach arecanut bunches from palms. The goal is a proof-of-concept system – initially radio-controlled – that safely climbs to the nut clusters and cuts the nuts for collection, thus increasing efficiency and worker safety.

2. System Overview

The UAV harvesting platform integrates standard drone hardware with a custom cutter. Its main components are:
Flight Controller (STM32 MCU + IMU): We use a 32-bit STM32F41CEU6microcontroller running on-board firmware. An MPU‑9250 IMU (3-axis gyro + accel + magnetometer) is connected via I2C to measure roll, pitch, and yaw. The STM32 computes filtered attitude angles (using a complementary filter and PID loops) to stabilize the quadcopter.
Propulsion (Motors & ESCs): Four brushless DC (BLDC) motors with propellers provide lift. Each motor is driven by an electronic speed controller (ESC) controlled by PWM outputs from the STM32. In our prototype, we chose 10″ propellers with matched motors for sufficient thrust.
Wireless Link (nRF24L01 Radio): A 2.4 GHz nRF24L01+ transceiver module serves as the RC link. One nRF24L01+ is attached to the STM32 on the UAV, and a matching module on a hand-held transmitter sends pilot commands (throttle, yaw, etc.). These modules use SPI and can easily interface with MCUs like the STM32. In tests, the link gave a reliable control range of tens of meters.
Cutter Mechanism: A small cutting blade is mounted above the drone, driven by a servo-actuated arm on a linear guide. When positioned under a nut cluster, the blade swings or slides to sever the bunch from the stem. A locking pin holds the cutter in place during flight. This blade-and-motor unit was sized to cut tough palm stalks.

3. Flight Control and Wireless Link

The STM32 flight controller implements a classic cascaded PID control scheme. Gyroscope and accelerometer readings from the IMU are fused (via a complementary filter) to estimate the quadcopter’s orientation. As one reference notes, “A common method to control quadcopters is PID control loops” due to their simplicity. We tuned three nested PID loops: an inner loop stabilizes angular rates (gyros) and an outer loop stabilizes angles (roll, pitch, yaw). The total thrust and torques computed by the PIDs are converted into four motor speed commands. In practice, this lets the UAV hold altitude and resist disturbances; during tests, the drone could maintain level hover with only a few degrees of drift. For remote operation, a 2.4 GHz nRF24L01+ radio link connects the UAV to a handheld controller. The flight deck on the drone runs a small receiver circuit with an nRF24L01+ module. A paired nRF24L01+ on the transmitter sends pilot inputs over SPI.

In other words, the nRF24L01+ system carries the throttle, yaw, pitch, and roll commands from the pilot’s joystick to the UAV. Because each module can form links with multiple nodes, this setup could be extended. Together, the PID loops and radio link allow stable but manual flight. The pilot can, for example, hold the UAV in place under a palm by centering the sticks; the controller automatically adjusts motor speeds to cancel any drift. During cutting, the operator lifts or lowers the throttle to position the blade at the bunch, then triggers the blade motion.

4. Testing and Results

Hover Stability: In flight tests, the tuned PID controller produced smooth hovering. After takeoff, the quadcopter held altitude with only minor oscillation. Roll and pitch angles stayed within a few degrees of level, even in a light breeze.

Harvest Trials: We first measured the force needed to cut an arecanut stalk using a dynamometer on a lathe. In field trials, the UAV approached a low-hanging cluster, and the cutter was activated. The blade successfully sliced through the nut stem, and the bunch dropped free in all harvest tests. The quadcopter supported the cutter’s recoil and did not flip or tip, confirming safe flight characteristics.
Safety and Failsafe: While testing, we verified basic safety responses damage occurred. We also implemented a low-battery cutoff: when the voltage dropped below a threshold, the system automatically throttled down to land.

5.Literature Review

Several studies inform our design:

Areca Harvesting Technologies: Traditional solutions involve climbing machines. For example, semi-automatic tree climbers using pedal power or petrol engines have been prototyped. However, these still require a frame on the tree. Rakesh et al. (2020) designed an areca harvesting module on a UAV, performing structural and aerodynamic analysis to ensure the drone could carry the cutter and payload. This work showed the feasibility of an areca harvester mounted on a quadcopter but focused mostly on mechanical design.

Drone-based Fruit Picking: Varadaramanujan et al. (2017) developed a drone with an articulated end-effector (suction-based gripper) to pluck fruits from trees. Similarly, Park et al. (2024) introduced a multi-degree-of-freedom harvesting mechanism for a hexacopter, emphasizing the kinematics of reaching and grasping fruit without destabilizing the UAV. These illustrate the potential of UAVs for aerial harvest tasks, motivating our integrated approach.

Flight Control Systems: Tang and Lin (2021) built a patrol quadcopter UAV using an STM32 MCU with an MPU9250 sensor for attitude control. They employed an Extended Kalman Filter and cascade PID controllers to achieve stable flight. This reinforces our choice of STM32+MPU9250 for precise orientation sensing and control accuracy. In general, quadcopter control is a closed-loop feedback system: the FC reads IMU data (roll, pitch, yaw), compares to pilot commands, and adjusts motor speeds via ESCs.

6. Advantages and Limitations

Advantages: The UAV harvester brings several key benefits. First, it greatly boosts productivity because no worker needs to climb each tree, labor costs drop dramatically (some estimates suggest a 60–70% cost reduction). Worker safety is also improved – accidents from falls or cuts are eliminated. The electric quadcopter produces zero on-site exhaust, giving a lower carbon footprint. Finally, precise blade control ensures clean cuts. In our tests, the cuts were repeatable and did not tear the tree, which preserves tree health and companion crops.
Limitations: Despite its promise, the current system has constraints. Flight time is limited: our prototype can fly only 10–15 minutes per battery, enough for a few trees. The cutter's payload and battery significantly reduce endurance. The craft must be operated manually via radio; no automatic navigation or target detection is implemented yet. In dense plantations, wind and obstacles could make flight harder. The cutting range is also fixed – the UAV can only reach nuts within its propeller height range. Further, the device can cost several hundred dollars in parts, so cost-effectiveness depends on scale. These limitations highlight areas for improvement before a field-ready product can be deployed.

7. Conclusion and Future Scope

This project demonstrated a working prototype of a quadcopter-based arecanut harvester. We showed that an STM32 flight controller with PID loops can stabilize the UAV in midair, and that a lightweight radio link (nRF24L01+) suffices for remote operation. In test flights, the drone hovered steadily, and its cutter successfully removed nut bunches from palm branches. The design promises to reduce the drudgery and danger of manual picking, offering many times the productivity of a human climber.

For Future work:
>On-board vision (using a camera and computer) could detect nut bunches and guide the cutter, eliminating the need for constant pilot input
>Improving the power system is also important: higher-capacity batteries or swappable packs would lengthen mission time. We might also refine the cutter mechanism – for example, adding a collection basket to catch cut nuts safely. In summary, our quadcopter harvester is a promising first step, and with further development (full autonomy and longer battery life) it could become a practical tool for modernizing arecanut farming.