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Efficient nutrient management and precise fertilization are essential for advancing modern agriculture, particularly in regions striving to optimize crop yields sustainably. The AgroLens project endeavors to address this challenge by…
The success of deep learning in visual recognition tasks has driven advancements in multiple fields of research. Particularly, increasing attention has been drawn towards its application in agriculture. Nevertheless, while visual pattern…
Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much…
United Nations set Sustainable Development Goals and this paper focuses on 7th (Affordable and Clean Energy), 9th (Industries, Innovation and Infrastructure), and 13th (Climate Action) goals. Climate change is a major concern in our…
For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits greatly from real-time, on-the-spot analysis of soil at low cost. Colorimetric paper sensors are ideal candidates for cheap and…
Grasslands are known for their high biodiversity and ability to provide multiple ecosystem services. Challenges in automating the identification of indicator plants are key obstacles to large-scale grassland monitoring. These challenges…
Water is essential for agricultural productivity. Assessing water shortages and reduced yield potential is a critical factor in decision-making for ensuring agricultural productivity and food security. Crop simulation models, which align…
The rapid adoption of AI in Earth system science promises unprecedented speed and fidelity in the generation of climate information. However, this technological prowess rests on a fragile and unequal foundation: the current trajectory of AI…
In this work we propose a software platform for the collection, visualization, management and analysis of heterogeneous and multisource data for soil characteristics estimation. The platform is designed in such a way that it can easily…
Over the past decade, unprecedented progress in the development of neural networks influenced dozens of different industries, including weed recognition in the agro-industrial sector. The use of neural networks in agro-industrial activity…
Object detection and semantic segmentation are two of the most widely adopted deep learning algorithms in agricultural applications. One of the major sources of variability in image quality acquired in the outdoors for such tasks is…
Expensive sensors and inefficient algorithmic pipelines significantly affect the overall cost of autonomous machines. However, affordable robotic solutions are essential to practical usage, and their financial impact constitutes a…
Digital technologies ignited a revolution in the agrifood domain known as precision agriculture: a main question for enabling precision agriculture at scale is if accurate product quality control can be made available at minimal cost,…
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including computing vision, natural language processing, time-series…
Plant diseases are major causes of production losses and may have a significant impact on the agricultural sector. Detecting pests as early as possible can help increase crop yields and production efficiency. Several robotic monitoring…
Precision soil greenhouse gas (GHG) flux prediction is essential in agricultural systems for assessing environmental impacts, developing emission mitigation strategies and promoting sustainable agriculture. Due to the lack of advanced…
Dairy farming is a particularly energy-intensive part of the agriculture sector. Effective battery management is essential for renewable integration within the agriculture sector. However, controlling battery charging/discharging is a…
The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation…
Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because of its promises to bring vast benefits for consumers and businesses, with considerable benefits…
Accurate, timely, and farm-level crop type information is paramount for national food security, agricultural policy formulation, and economic planning, particularly in agriculturally significant nations like India. While remote sensing and…