A Review on AI and Remote Sensing Based Regenerative Agriculture Assessment

Regenerative Agriculture is a sustainable way of cultivating land by practicing principles such as—using soil cover or mulching, enabling multi-cropping instead of mono-cropping, integrating cattle, no tilling of land, and maintaining living roots. These principles are coupled with agricultural practices like water harvesting, non-usage of synthetic chemicals, and crop rotation. This results in taking care of soil health and improving groundwater quality. Due to regenerative agriculture’s ecological and health benefits, many food producers are committing to this methodology. However, the regenerative agriculture compliance assessment, which requires in-person farm visits to conduct stringent observations and audits, is costly. This increased input cost for farmers leads to an increase in the market price of regenerative agriculture-certified products. To identify potential solutions for this challenge, we first review research studies that demonstrate the applicability of AI-based remote sensing technologies in agricultural applications. We then map these studies to the problem of regenerative agriculture assessment. A technical framework is proposed to build a system for regenerative agriculture assessment using remote sensing and AI-based models. Specifically, this framework covers techniques to assess the five principles of regenerative agriculture and water conservation practice. The approaches presented here can help compliance agencies carry out assessments with reduced overhead, resulting in an overall increase in the adoption of regenerative agriculture. This will result in improving biodiversity, reducing soil erosion, improving water quality, conserving water, and reducing pest attacks.