Revolutionizing agricultural efficiency with advanced coconut harvesting automation

Abstract

The precision coconut harvesting system aims to develop an efficient system for accurately detecting  coconuts in agricultural landscapes using advanced image  processing  techniques.  Coconut  cultivation  is  vital  to  many  tropical economies  and  precise  monitoring  is  essential  for  optimizing  yield  and resource  utilization.  Traditional  methods  of  coconut  detection  are  labour-intensive   and   time-consuming.   The   proposed   computer vision-basedapproach  automates  and  enhances  coconut  detection  by  analyzing  high-resolution images of coconut plantations. Pre-processing techniques improve image  quality  and  object  detection  algorithms  such  as convolutional  neural networks (CNNs)   identify   coconut   clusters.   Challenges   like   lighting variations and background clutter are addressed using feature extraction and pattern  recognition.  A  user-friendly  interface  visualizes  detection  results, aiding  farmers  in  timely  decision-making.  Extensive  testing  on  diverse datasets   evaluates   system   effectiveness.   This   model   aims   to   advance precision   agriculture,   enhancing   productivity   and   informing   coconut farmers’decision-making  processes.  Using  a  CNN  model,  the  accuracy  of coconut detection based on its ripeness was 98.8%.