ENHANCING SUSTAINABILITY AND PROFITABILITY IN AGRICULTURE THROUGH AI-POWERED CROP PROTECTION

Abstract

This paper presents selected features of the xarvio Field Manager solution that enhance agricultural sustainability. Conventional farming still relies heavily on agrochemicals to ensure a safe and sufficient food supply for our growing population. However, this work proposes new methods to reduce chemical use, benefiting both the environment and the farmer's profitability. The tools introduced follow the 4R principles: applying the right products at the right time, with the right dosage, and only on the required places. In the 2019 European cereal season, these solutions were used across 340,000 hectares. By optimizing fungicide use through precise timing or by skipping unnecessary applications, field trials showed an average savings of up to 30% compared to standard practices. The system also reduced tank leftovers by 72% by calculating the exact amount of ingredients needed for each field. In Brazil, targeting only the areas with weeds led to a 61% average savings in herbicide use. With the fully automated buffer zone solution in Germany, adherence to regulations and the conservation of protected areas became both effective and convenient. The solutions detailed here are highly scalable and can be implemented on a much larger scale, making them strong candidates for significantly reducing the environmental impact of crop protection products on land and water, thereby making farming more sustainable without jeopardizing food security. As a result, these solutions contribute directly to specific UN Sustainable Development Goals (SDGs), including target 2.4, which calls for resilient, sustainable agricultural practices, and target 12.4, which aims for the environmentally sound management of chemicals and their reduced release into the environment. While there is still much work to be done to fully achieve these goals, we believe the tools presented here are a significant step in the right direction.

 Index Terms—Deep Learning, Precision Agriculture, Digital Farming, Sustainable Agriculture, Agronomic modelling, Responsible Pesticide Usage, Machine Learning, Computer Vision, Crop Health Monitoring, Robotics, AI-Powered Crop Protection