Agriculture

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                        Tomato bearing recognization

Project Overview

In this project, we explored how plants' fruit-bearing stages could be analyzed using the bounding box tool in the agricultural sector. This task, traditionally performed by humans, is notably intricate, time-consuming, and laborious. Prior to integrating artificial intelligence (AI) into agricultural practices, this classification process—based on plant size and color—demanded considerable amounts of time, financial resources, and effort. 

Moreover, it was prone to human error, leading to losses for both farmers and consumers, which in turn affected productivity and earnings negatively. However, with the adoption of AI, we're now able to efficiently process this data for use with agricultural robots or drones. 

Project Objectives

This advancement significantly addresses labor shortages, especially during peak harvesting times. Additionally, it supports optimal harvest timing decisions, enhances profit forecasts, and minimizes damage to fruits.