Nurturing Agribusiness: A Sustainable Farming System for Tomato Crop Management Leveraging Machine Learning

Abstract

The agriculture industry is undergoing a transformative shift with the introduction of IoT technology, enabling global connectivity for farmers. This technology offers a plethora of advantages, ranging from precise seed selection based on soil analysis to efficient crop maintenance, water management, and enhanced marketing support for improved profitability. To further enhance tomato farming practices, we propose the implementation of a smart farmer marketing assistant that streamlines the process of segregating yield based on its growth stage, reducing labor and time requirements.Further, the frame work is capable of early-disease management system that can detect diseases like early blight,light blight, buck eye rot and anthranose and suggest remedy. By adopting this innovative approach, financial losses associated with traditional methods are minimized.The traditional practice of combining all categories of vegetables (ripened, unripened, and partially rotten) in a single container often results in reduced shelf life for the produce. In our framework, we employ color sorting to categorize the vegetables, ensuring proper packing into their respective bins. This valuable data is made accessible through a cloud environment, providing potential buyers with comprehensive information about the yield, its category, and pricing. This increased visibility empowers farmers to reach a global market and sell their produce at competitive prices.
 In this context, we present a case study focused on the tomato crop, where we have successfully developed a prototype utilizing ESP32, a color sensor, and Google Firebase. This comprehensive framework effectively harnesses the power of IoT, Machine Learning, and potential marketing strategies, transforming the way farmers manage their crops and connect with buyers on a global scale with highly accurate 87.9% experimental results.