Paul samx
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- Apr 20, 2024
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Figure 1. Source:EDUCBA
Figure 2. Source:ResearchGates
INTRODUCTION
Tanzania is an agricultural economy country with a substantial portion of its population engaged in farming activities. The agricultural sector in Tanzania plays a significant role in the Gross Domestic Product (GDP) of the country and provides employment to a considerable workforce. For example, between 2015 and 2021 agriculture contributed approximately 33.3 trillion Tanzanian shillings (TZS) or around 14.3 billion U.S. dollars to Tanzania's GDP. In the fiscal year 2022/2023, the agricultural sector accounted for 24.27% of the GDP.
Despite the substantial contributions made by agriculture, there are several challenges that hinder its development. These challenges are likely to be limited access to crucial information among Tanzanian farmers, such as weather forecasts, market prices, pest control methods, and optimal agricultural practices. Also, traditional farming methods lack precision and are often inefficient, failing to meet specific crop requirements or soil conditions. Inadequate management and utilization of resources, such as water, fertilizers, and pesticides, result in suboptimal crop yields. In order to address these challenges, this article explores how the application of AI technology can enhance productivity in the agricultural sector by overcoming obstacles and promoting better practices.
Artificial Intelligence (AI) refers to the computer systems that is capable of performing complex tasks that were traditionally performed by humans, such as reasoning, decision-making, and problem-solving. Through the utilization of AI technology tools and techniques, Tanzanian farmers can enhance their operations, make data-informed decisions and ultimately increase agricultural production to meet the rising demands of the country's population. Various AI instruments and their applications in agricultural production are discussed below.
Drones, are applied for the aim of monitoring growth crops, spraying pesticides, conducting soil analysis, livestock management, weeds identification and managing irrigation. It captures high-resolution images of fields, detect crops and livestock health issues early on, and optimize farming practices. By Applying drone technology effectively, farmers can optimize their operations, mitigate risks, and achieve sustainable agricultural practices.
Figure 3. Applications of Drones in Agriculture ( Discover Agriculture)
Internet of Things (IoT) sensors, These include soil moisture sensors, Weather Stations, Crop Health Sensors, GPS and Precision Guidance Systems, Livestock Monitoring Sensors and Tracking Collars and even smart greenhouse control. The integration of smart sensors with technologies like big data analytics and AI is important for optimizing crop management, resource utilization, and sustainability in agriculture. These innovations empower farmers to make data-driven decisions and enhance overall farm productivity.
Figure 4. Applications of IoT sensors in Agriculture (Discover Agriculture)
Autonomous Tractors, Always use a combination of GPS, radar, lidar, and computer vision to navigate fields, plow, plant, spray, and perform other farming tasks without a human driver. These tractors allow farmers to work longer hours and be more efficient, especially during critical planting and harvesting seasons. These tractors enhance efficiency, reduce labor costs, and optimize resource utilization.
Figure 5. Applications of Autonomous Tractors in Agriculture (Discover Agriculture)
Mobile phones, the adoption of mobile phone technology among farmers, especially in our country, can significantly enhancing famers' ability to obtain real-time information on market prices, weather forecasts, supply chain coordination for both inputs and outputs, agricultural techniques, and government policies. Farmers can easily communicate with agricultural experts, extension officers, and fellow farmers through calls, text messages, and social media platforms for advice and support. Both Figures below show Applications of Mobile phones in Agriculture
Figure 7. Source:ResearchGates.
Measures to Implement AI Technology in Tanzania Over the Next Decades
Investing in infrastructure development is a crucial step in implementing AI technology in the agricultural sector. One key aspect of this is building the necessary digital infrastructure to support the deployment and utilization of AI systems. For example, improving access to high-speed internet and reliable power supply in rural farming communities is essential. This allows farmers to connect their IoT sensors, cameras, and other devices to cloud-based AI platforms for real-time data processing and insights.
Another important infrastructure investment is establishing centralized data repositories and computing resources. This could involve setting up agricultural data centers that securely store and manage large volumes of crop, weather, soil, and market data. Farmers and agricultural agencies can then access this data to train and run AI models for tasks like yield prediction, disease detection, and autonomous farm machinery.
Also AI Technology can be implemented in Tanzania mainly by establishing public-private partnerships to fund AI research and development tailored to local farming needs. This could involve collaborations between the government, universities, and agribusiness companies to create AI-powered solutions for challenges like crop yield forecasting, pest and disease identification, and optimizing resource use.
Additionally, implementing training programs to educate farmers on the benefits and practical application of AI tools would be crucial, ensuring widespread adoption and integration of the technology across the agricultural sector.
Also Incentivizing smallholder farmers to embrace AI-driven precision farming practices through subsidies or financial assistance schemes could also accelerate the adoption of these transformative technologies in Tanzania's agricultural landscape over the coming years.
CONCLUSION, The future outlook of implementing AI technology in agricultural production over the next decades is promising, with the potential to revolutionize the industry. For example, AI-powered predictive analytics can help farmers optimize crop yield by forecasting weather patterns, identifying optimal planting and harvesting times, and detecting early signs of disease or pest infestations.
Autonomous drones and robotic systems equipped with computer vision and machine learning algorithms can automate labor-intensive tasks like spraying, weeding, and harvesting, improving efficiency and reducing reliance on manual labor.
Furthermore, AI-driven precision farming techniques can precisely monitor and adjust factors like irrigation, fertilizer application, and soil health, leading to more sustainable and resource-efficient agricultural practices. As AI continues to advance, integrating these intelligent systems into agricultural supply chains can also enhance logistics, inventory management, and market forecasting, ultimately improving the overall productivity, profitability, and resilience of the agricultural sector in the decades to come.
Figure 9. AN IMAGE OF REFERENCES
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