Changing dynamics in climate, population, and dramatic weather events continue to adversely affect food and forestry systems.However, on the upside, the exponential growth in artificial Intelligence (AI) and machine learning(ML) promises new innovative solutions to realize efficiencies, sustainability, and precision across the board. The impact is already evident with leaders like Dejan Jakovlevic, FAO’s chief information officer, admitting that AI will impact the agri-foods systems. Studies show that there will be continued pressure on natural resources, with the world population set to breach the 9 billion mark by 2050. This will require a 70% increase in agri-food production. It’s estimated that about 600 million people will be facing hunger by 2030. Additionally, about 22.8 million hectares of forest were lost yearly between 2010-2020. Marine resources also continue to be depleted. AI helps stakeholders in the agri-food and forestry sectors draw multiple advantages while pushing boundaries in these areas. The Agriculture AI market is projected to reach $1.7 billion by the close of 2023. It’s expected to have a CAGR of 22.50%, expanding to $4.70 billion by 2028. There are multiple use cases for AI technology, including optimization, forecasting and adaptive systems. This article discusses how AI impacts agricultural productivity, forestry, and fishing. We also uncover the benefits and challenges of using AI in these areas.
Identifying a ship’s characteristics, such as its size and activity at sea, is possible. This aids conservationists and other stakeholders identify and classify fishing vessels on high seas and what's being fished.
Platforms like the Global Fishing Watch use AI to analyze data from automatic identification systems (AIS), vessel monitoring systems, and satellites to understand global fishing patterns.The platform uses CNN to classify each vessel as fishing or non-fishing. Additionally, the platform monitors long-line fishing and fish behavior, which helps authorities track compliance.
It's possible to analyze real-time imagery and video feeds using deep learning models to identify, count, and determine the density of the fish. Estimating the number of fish is critical to promoting sustainable fishing, avoiding overfishing, and enforcing regulations.
Using AI-driven tools in fish recognition helps in various operational aspects of fishing, like cost reduction and manual labor. It promotes trust by helping aquaculture farmers and other fisheries stakeholders build harmony and manage the resource.
AI has the potential to significantly impact agriculture, forestry, and fishing, resulting in multiple benefits. However, different challenges impede the use of this technology.
Optimizing resource use: Precision farming, autonomous machines, predictive analytics, and predictions help increase productivity through resource optimization. Autonomous machinery can weed expansive fields within short periods, significantly reducing the need for manual effort and herbicide/pesticide.
Enhancing yields: Using AI to identify patterns and give recommendations makes it possible to increase yields exponentially. Using such insights to identify planting patterns, harvesting, and even selecting the most suitable plant species goes a long way.
Assist in sustainability: Using AI-powered tools to track resources in forests and fishing ecosystems helps avoid over-exploitation. Using remote sensing and live feed helps detect illegal fishing and logging. Additionally, by monitoring species, stakeholders can proactively manage resources and preserve biodiversity.
Reduced costs: Precision farming leads to optimal use of resources like water, herbicides, and fertilizers. This reduces wastage and the cost of inputs. Additionally, it’s possible to reduce wastage in the supply chain, logistics, monitoring, and manual labor using AI-powered tools.
Early warning: AI is crucial in helping mitigate and reduce the impact of events like disease outbreaks and weather-based risks. By analyzing satellite imagery and remote sensing data, farmers can adequately adjust their activities to avoid adverse events. This also enables them to plan effectively around irrigation and disease control.In livestock production, AI-powered tools get data from sensors attached to animals that monitor their well-being. Subsequently, any signs of a disease outbreak are dealt with promptly.
Despite the many advantages and exciting prospects AI brings to agriculture, forestry, and fisheries, several challenges continue to impede its uptake.
Expensive initial cost: Purchasing equipment and machinery for precision agriculture or forest management can be costly. This may include remote sensing equipment and autonomous machinery. For instance, an autonomous robot for weeding may cost upwards of $12,000. The cost would be prohibitive for small-scale farms or farmers in third-world countries.
However, efforts continue to develop low-cost autonomous robotics solutions for weeding. ROMI is looking to develop smaller autonomous robotics for micro-farms at cheaper rates.
Transparency: Machine learning algorithms have varied transparency levels. How AI-powered tools determine outcomes may need to be clarified. The explainability of outcomes, classifications, and predictions made by AI systems need to be easily justified.
Application users should be able to get additional information for AI systems' decisions. Additionally, it should be possible for users to contest the outcome of the AI-powered system in case a farmer feels decisions are not correct.
Lack of skilled workforce: Many stakeholders in the agriculture, forestry, and fisheries industries need more skills and education to use AI-powered tools and machines effectively. It’s paramount to bridge the education and skill gap to help adoption.