Optimizing Harvesting Operations with Cloud-Connected Robots for Fruit and Nut Crop Yield Enhancement with CNN

This research introduces a new method for improving the productivity of fruit and nut crops by combining cloud-connected robots with convolutional neural networks (CNNs) in harvesting. Conventional harvesting techniques often encounter inefficiencies and workforce scarcity, resulting in poor crop production. Our proposed method utilizes cloud-connected robotic devices with convolutional neural network (CNN) based vision systems to automate harvesting and maximize production. The CNN models are taught to reliably classify mature fruits and nuts under different environmental circumstances, allowing precision harvesting operations. Moreover, transmitting information to the cloud enables immediate data analysis and decision-making, resulting in easier-to-implement flexible harvesting plans considering variables like weather variations and crop conditions. We analyze the design and execution of the proposed approach, emphasizing its capacity to transform harvesting methods in farming. By conducting experiments and analyzing case studies, we provide evidence of the efficacy of our method in enhancing agricultural productivity, decreasing labor expenses, and enhancing overall operational efficiency. It explores novel possibilities for implementing sustainable agriculture methods via robots and machine learning in handling crops.