Introduction
Plantos aims to provide small and medium-sized agricultural operations with a user-friendly and low-cost approach to harnessing hardware, data, and AI for farming practices. Precision Agriculture Technologies (PATs) are key to enabling farmers with cost-effective and environmentally sustainable farming practices. With growing populations, finite resources, and dwindling margins, implementing efficient farming practices has become more relevant than ever.
From little things, big things grow.
Why Plantos?
With the global population projected to reach 10 billion by 2050, the farming industry faces a critical challenge. It must adapt significantly to meet the needs of this growing population while simultaneously minimizing environmental impact and maintaining financial viability. With the increase in agricultural output comes a plethora of new obstacles and new ways to solve them. PATs are revolutionizing how farmers can scale to meet population needs, making it easier to optimize inputs, detect diseases, and predict crop yields—all of which serve to reduce input costs and conserve resources.
PATs come with their own set of obstacles that make implementation difficult for a major portion of small to medium-sized farming operations. Primary barriers to entry among small farms include costs, ease of use, and reliability/range. Farmers who are already operating on minimal margins find it difficult to take risks on new technology that has an unproven track record. Accurate and reliable hardware tends to be quite cost-intensive and operationally complex, which is why PATs are historically more frequently deployed in large-scale farming practices. Large swaths of the farming population are missing out on the efficiencies that PATs can offer due to these barriers.
Finding Solutions in Data
Plantos provides an easy to use and low-cost remote sensor for optimizing agricultural inputs crops. The goal is to create a plug-and-play sensor that provides a smooth user experience from onboarding and collecting your first dataset to reading outputs and implementing optimizations.
Combination of two key tech concepts:
- Remote sensing (RS)
- Machine Learning (ML)
Internet of Things (IOT) devices provide us with a low-cost method for collecting data in the field and relaying it to a network for analysis. Where low-cost RS lacks in accuracy, ML will be able to standardize data and maintian data integrity. Then predictive models will be able to digest the input data and provide users with actionable information right to their phone or computer.
Picture of Success
Envision a future where farming uncertainties are minimized through precise, real-time predictive analytics accessible via smartphones. In this world, successful farming no longer relies solely on generational wisdom and costly trial-and-error approaches. Instead, farmers can proactively identify potential challenges and implement preventive measures, thanks to data-driven insights that guide their decision-making. This technological advancement democratizes farming expertise, enabling both seasoned and novice farmers to optimize their operations with greater confidence and efficiency.
Precision Agriculture Primer
Commonly referred to as PATs or PAs, the term Precision Agriculture Technology refers to a growing school of data-driven tools for collecting input information from crops to optimize their yield and minimize resource use/costs. PATs are nothing new to the agriculture community but with ongoing advancements in hardware and AI, they are rapidly evolving how we approach farming:
"[...] precision agriculture has emerged as a crucial innovation in combating food insecurity by enhancing the efficiencies and sustainability of farming practices. Utilizing cutting-edge technologies such as GPS, remote sensing, and data analytics, precision agriculture optimizes resource use, reduces environmental harm, and enhances agricultural productivity."
PATs are used to tackle a wide-variety of different obstacles faced by farmers, some relevent implementations today are:
- GPS monitoring and automations
- Drone imaging to predict crop yields
- Remote sensors for measuring soil nitrogen
- AI imaging to detect diseases in crops
The Current Landscape of Precision Agriculture
- Published: 2025-04-18
- Updated: 2025-04-22
- Author: Tyler Dennis
- Discussion: 14-03-2025
Is Technology Threatening The Small Farm?
Precision Agriculture Technologies (PATs) are currently a catlyst to the growing disparity between large-scale farming operations and small to medium sized farms. While the agricultural industry is not a zero-sum game, large-scale farming practices clearly benefit from systematic advantages, widening the resource gap experienced by small to medium-sized farming operations. High barriers to entry, such as costs and complexity of use, result in the benefits of PAT implementation to be primarily realized by large-scale farms. The edge gained by utiliing PATs is compounding; resources saved from implementation one season can be reinvested into the crop and farming infrastructure the next season, allowing for an exponential increase in yield year after year.
Smallholder Farming = A Thriving Future
Ensuring the future of smallholder farming is synonymous with ensuring the future of food security, environmental stability, and wide-spread economic affluence. Accoding to the USDA, small family farms are defined by gross cash farm income below $350,000 annually and make up about 89% of the population of farmers in the US [4]. These small family farms create food security and diversity through the variety of crops they generate, establishing agricultural biodiversity and reducing dependence on monocultures. In times of crop failure, diversity enhances food security by providing alternative non-effected crops. This diversity plays a role in environmental stability by cultivating soil health, supporting food-webs, and promoting pollination; in general, local farming practices are associated with taking better environmental stewardship over land. These smallholder farmers tend to employ and support local communities and their interests to a further extent than industrial farming.
[...] about 89 percent of all farms were small family farms. Small family farms operated 45 percent of U.S. agricultural land and produced 18 percent of the total value of production.
America’s Farms and Ranches at a Glance: 2022 Edition
Just as small and medium sized farms cannot afford to forgo precision agriculture, we as a community cannot afford to let these farms fall behind the industry.
Rapid advancements in hardware and artificial intelligence are enabling the creation of cost-efficient and user-friendly PAT tools. These developments have the potential to transform the current reality of farming, making these technologies more accessible to a broader range of agricultural operations.
Demographics Tell the Story
Socio-demographic variables (i.e., age, gender, education, farming experience, income, and farm size) and their correlation with PAT adoption can help explain the current state of precision agriculture in small to medium-sized farms. Studies show that individuals most likely to adopt PATs are between 25-50 years old, have post-secondary education, extensive farming experience, and manage large acreages [1]. Small-scale farmers are significantly underrepresented in this demographic compared to industrial farming operations. Examining the barriers to entry can help us understand how these demographic patterns emerged.
Cost, ease of use, reliability, power consumption, and signal range are the primary concerns affecting PAT adoption [1] [2]. While these challenges are more manageable for high-frequency adopters,they present significant barriers for small-scale farmers looking to implement PATs. At scale
Large-scale farming
What's Changed?
Cost and ease-of-use were by far the greatest barriers to entry for small-scale farms implementing PATs. Rapid advancements in computing, hardware, and AI make existing obstacles a thing of the past. The combination of low-cost remote sensors and machine learning offers an opportunity to drastically lower unit costs while maintaining data accuracy [3]. LoRa-enabled IoT devices allow for straightforward user interfaces and sensor pairing workflows, all while transferring high-throughput data over extended distances at minimal cost. These technological advances work together to lower the primary barriers to entry for PATs, enabling small-scale farms to leverage their advantages and remain competitive with large-scale operations.
References
- Factors Influencing Precision Agriculture Technology Adoption Among Small-Scale Farmers in Kentucky and Their Implications for Policy and Practice
- Exploring Barriers to the Adoption of Internet of Things-Based Precision Agriculture Practices
- Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications
- America’s Farms and Ranches at a Glance: 2022 Edition
The Internet of Things (IoT)
How do devices talk?
IoT is a gernalized term that refers to the network of communication created between devices, sensors, applications, etc. talking back and forth to each other. One might speculate that IoT devices share data by connecting to the internet; a large portion of devices do operatate this way, but there are numerous other types of communication protocols like bluetooth and LoRa that allow for IoT devices to communicate.
IoT in Plantos
IoT plays an essential role in gathering data in the field, especially when it comes to performing the real-time analytics required for successful precision agriculture. Plantos creates an IoT network between micro-controllers connected to sensors and a centralized gateway which relays data to a cloud database.