#Machine Learning (ML) in agribusiness

[pathfinderresearch]

Machine Learning-Based Predictive Model for Leaf Nutrient Deficiency Detection in Crops

Revolutionizing Agribusiness with AI-Driven Solutions

Agriculture is the backbone of many economies, and with the rapid advancement of technology, integrating Machine Learning (ML) in agribusiness is transforming the sector. One such innovation is Machine Learning-Based Predictive Models for detecting leaf nutrient deficiencies in crops, helping farmers optimize yield, reduce losses, and enhance profitability. At Pathfinder Research and Training Foundation (PRTF), Greater Noida, our MBA in Agribusiness Management aims to equip future agripreneurs and industry leaders with cutting-edge knowledge to bridge the gap between agriculture, technology, and market demands.

Understanding Leaf Nutrient Deficiency and Its Impact

Crops require essential nutrients like Nitrogen (N), Phosphorus (P), and Potassium (K), among others. A deficiency in these nutrients can lead to reduced crop productivity, poor quality produce, and economic losses. Traditionally, detecting deficiencies required soil testing and expert analysis, but these methods are time-consuming and expensive.

Machine Learning (ML) has emerged as a game-changer in agriculture by enabling real-time, cost-effective, and precise diagnosis of leaf nutrient deficiencies.

How Machine Learning Helps in Nutrient Deficiency Detection

1. Image Processing & Computer Vision:

Farmers can use smartphones or drones to capture images of crop leaves.

ML models analyze the color, texture, and patterns to detect deficiencies.

2. Predictive Analytics:

ML algorithms compare leaf symptoms against vast databases of plant health conditions.

Predictive models forecast potential deficiencies before symptoms become visible.

3. IoT and Smart Sensors:

Soil and leaf sensors collect real-time data on nutrient levels.

AI-powered systems recommend precise fertilizer application, reducing waste.

4. Integration with Agribusiness Marketing:

ML-based data enables farmers, agribusiness professionals, and MNCs to strategize better.

Predictive analytics support input companies (fertilizers, pesticides) in tailoring their offerings based on regional needs.

Agribusiness and Market Integration: Role of MNCs

The global agribusiness market is highly dynamic, with multinational corporations (MNCs) playing a vital role in agricultural input supply, processing, and marketing. The MBA in Agribusiness Management at PRTF focuses on:

✅Supply Chain Optimization: Companies leverage ML-driven insights to ensure efficient distribution of fertilizers, pesticides, and precision farming tools. ✅Market Intelligence & Consumer Demand Analysis: ML tools predict demand trends, enabling agribusiness firms to make data-driven decisions. ✅Sustainability & Cost Reduction: By minimizing nutrient wastage, businesses ensure cost-efficient and eco-friendly solutions. ✅Predictive Pricing Models: MNCs use ML-powered forecasts to determine optimal pricing strategies for agricultural products.

The Future of Agribusiness: AI, ML & Data-Driven Decision Making

With the increasing demand for sustainable and tech-driven agriculture, ML-based nutrient detection is reshaping agribusiness management. At PRTF’s MBA in Agribusiness Management, students learn how data science, AI, and ML are transforming agri-marketing, supply chains, and global trade.

By integrating agriculture, business, and technology, graduates are prepared to lead agribusiness enterprises, work with MNCs, and contribute to a smarter, more efficient agricultural ecosystem.

Conclusion

Machine Learning is not just a technological advancement; it is a strategic tool that is empowering farmers, agribusinesses, and multinational corporations. By adopting AI-driven predictive models, the agricultural sector can achieve higher efficiency, reduced costs, and enhanced sustainability. The MBA in Agribusiness Management at PRTF is committed to bridging the gap between technology and business, ensuring that the next generation of agribusiness leaders is well-equipped to handle the future of precision agriculture and smart farming.

Are you ready to be part of the future of agribusiness? Join us at Pathfinder Research and Training Foundation (PRTF), Greater Noida, and become a leader in agri-tech innovation!

[distinguishedconnoisseurgarden]

Why GIS in Agriculture is the Future of Smart Farming

Introduction:

In today’s rapidly evolving agricultural landscape, technology plays a crucial role in optimizing productivity and sustainability. GIS is one such technology that is transforming farming by providing spatial insights, precision farming solutions, and better decision-making capabilities. This article explores the impact of GIS, its benefits, real-world applications, and case studies showcasing its effectiveness.

What is GIS in Agriculture?

GIS (Geographic Information System) is a technology that enables farmers and agribusinesses to collect, analyze, and interpret geographical data to improve agricultural practices. By integrating satellite imagery, field mapping, and spatial analysis, GIS helps farmers manage crops, monitor soil health, and optimize resource use.

Benefits of GIS in Agriculture:

Precision Farming: GIS enables farmers to apply fertilizers, pesticides, and water precisely where needed, reducing waste and increasing efficiency.

Crop Monitoring: Satellite imagery and remote sensing allow real-time monitoring of crop health and early detection of diseases or pest infestations.

Soil and Land Analysis: GIS provides insights into soil composition, moisture levels, and land suitability, helping farmers make informed decisions.

Yield Prediction: By analysing historical data and weather patterns, GIS aids in predicting crop yields and planning harvests effectively.

Disaster Management: GIS helps track natural disasters like floods, droughts, and storms, enabling farmers to take proactive measures.

Case Studies of GIS in Agriculture:

1.     Precision Agriculture in the USA:

A major farming enterprise in Iowa, USA, implemented GIS to enhance precision farming. Using satellite imagery and GPS-based soil analysis, the farm optimized fertilizer usage and irrigation schedules. This led to a 15% increase in crop yield and a 20% reduction in water consumption, showcasing how GIS can improve efficiency and sustainability.

2. GIS for Pest Control in India:

In Maharashtra, India, GIS was used to combat locust infestations affecting soybean crops. Researchers mapped affected areas using remote sensing and GIS tools, allowing authorities to deploy pesticides more efficiently. As a result, the infestation was controlled before significant crop damage occurred, protecting thousands of hectares of farmland.

3. Smart Farming in the Netherlands:

Dutch farmers integrated GIS with Internet of Things (IoT) sensors to create a smart farming system. GIS maps analyzed soil moisture levels and weather patterns to automate irrigation. This reduced water usage by 30% and ensured optimal growing conditions for greenhouse crops, demonstrating the power of geospatial technology in modern farming.

Challenges and Limitations of GIS in Agriculture:

While GIS offers numerous benefits, there are also challenges that farmers and agribusinesses face when implementing this technology:

High Initial Costs: Setting up GIS infrastructure, including satellite data access and software, can be expensive for small-scale farmers.

Technical Expertise: Effective use of GIS requires knowledge of geospatial analysis, which may necessitate training or hiring skilled professionals.

Data Accuracy Issues: GIS relies on accurate data input; errors in data collection or processing can lead to incorrect analyses and poor decision-making.

Connectivity Barriers: In remote agricultural areas, access to internet connectivity and real-time data transmission can be limited, affecting GIS efficiency.

Future of GIS in Agriculture:

The future of GIS is promising, with advancements in Artificial Intelligence (AI) and Machine Learning (ML) enhancing its capabilities. Farmers can expect even more precise predictive analytics, automated decision-making, and integration with drone technology for real-time monitoring.

Conclusion:

GIS is revolutionizing farming by improving efficiency, sustainability, and profitability. From precision farming to disaster management, its applications are diverse and impactful. As case studies show, integrating GIS into farming practices leads to higher yields, lower costs, and better resource management.

Bimmantra Academy is a leading institute for BIM education, offering expert-led courses, practical training, and real-world project exposure. With a focus on industry relevance, it equips professionals with the skills needed to excel in the evolving AEC sector.

As technology continues to evolve, GIS will play an even bigger role in shaping the future of global farming, ensuring food security and environmental sustainability.

[kisaanhelpline]

Trends and Innovations in Agricultural Practises - News and Analysis

The food system is about to break. The transition to a personalized and value-added food and ag ecosystem from a commodity-driven supply chain focused on scale is being driven by changing consumer food preferences and eating habits. We must reimagine a food system where technology enables new collaboration, consumer demand drives production, and sustainability shapes investment decisions in order to adapt to this dynamic environment. Smart strategic planning that is based on an unbiased assessment of the current situation, a data-driven analysis of market opportunities, and expert insight into potential future outcomes is necessary.

In order to deliver innovative approaches that help food companies thrive today and create long-term value in a reimagined food system, EY professionals look beyond vertical silos to understand the forces that are reshaping the industry—from ingredients and data to processing and products to behaviors and brands.

Let us learn more about trends and innovation in agricultural practices so that we are on top of the agricultural game.

Top 10 Agribusiness Trends for 2023

1. Internet of Things

In conventional farming, crop field monitoring necessitates a lot of labour, physical equipment, time, and effort. IoT offers an alternative to these established techniques. An Internet of Things (IoT) device has one or more sensors that gather data and deliver accurate information in real-time through mobile applications or other channels. Numerous tasks are carried out by these sensors, including plant and animal tracking, soil temperature and humidity sensing, and more. Additionally, IoT makes it possible for farms to be remotely monitored, which is more convenient for farmers. In addition, new irrigation systems automate water delivery to crops using IoT sensors.

These include, among others, rain sensors, on-site soil moisture sensors, and evapotranspiration sensors. Innovative sensor solutions from startups that integrate IoT and drone technology are being developed.

2. Farming Robotics

A major issue for farmers is a labour shortage, which is exacerbated by extensive field operations. In order to help farmers with fruit picking, harvesting, planting, transplanting, spraying, seeding, and weeding, startups are now producing agricultural robots. Robots are being used by farmers more frequently to automate boring fieldwork. They use intelligent farming equipment, such as fully and partially autonomous tractors, to harvest the crops. Additionally, auto-steer technology is available on tractors for simpler field navigation.

Additionally, automated systems for managing livestock also employ robots. This includes automatic feeders, milking machines, incubators, and weighing scales. Farmers can concentrate more on raising overall productivity thanks to robots because they no longer have to worry about sluggish farm operations. They also stop errors brought on by people.

3. Computerised Intelligence

AI in agriculture gives farmers timely information about the state of their fields, empowering them to take preventative action. AI helps farmers make informed decisions by providing predictive insights for predicting weather data, crop yield, and prices. Chatbots give farmers advice and suggestions for input. Automated anomaly and disease detection in plants and livestock is made possible by AI and ML algorithms. This makes it possible for prompt detection and, if necessary, corrective action. Additionally, ML algorithms are used in biotechnology to make suggestions for gene selection. Additionally, AI offers farmers who are turned down for credit by banks simple access to financing through alternative credit scoring.

Startups are making use of AI in a variety of ways to develop creative solutions that raise the standard of agriculture as a whole. Harvest Quality Vision (HQV), for instance, is a recent AgriTech innovation that scans and determines the quality and quantity of fruits and vegetables.

4. UAVs

It can be difficult to boost farm productivity while reducing expenses. Unmanned aerial vehicles (UAVs), also known as drones, assist farmers in efficiently getting past this inconvenience. Drones gather unprocessed data that can be converted into useful data for farm monitoring. Drones with cameras enable aerial imaging and surveying of both close-by and far-off fields. Precision agriculture is fueled by data that optimizes the application of seeds, water, fertiliser, and pesticides. Drones also make geofencing, grazing monitoring, and livestock tracking easier. They take pictures of fields while flying over them, from straightforward visible-light pictures to multispectral imagery that aids in crop, soil, and field analysis.

5. Precision Farming

In agriculture, sustainability refers to the use of environmentally friendly practices and inputs that have no or very little adverse effect on the environment. Site-specific crop and livestock management, also known as precision agriculture, is an illustration of this. It is a technique whereby farmers increase the quality and productivity of the yield by using precise amounts of input, such as water, pesticides, and fertiliser. The field is divided into various plots, each with a different slope, sunlight exposure, and soil characteristics.

Therefore, applying the same treatment to the entire farm is ineffective and wasteful of time and resources. Many AgriTech startups are working on solutions in precision agriculture to solve this problem and increase profitability while addressing sustainability issues.

6. Applied Biotechnology

Pests and plant diseases cause a significant amount of crop yield to be lost. Despite the fact that agrochemicals are used in fields, they are not the most sustainable option. However, the quality of crops and livestock is improved by the use of biotechnology in agriculture. Plant breeding, hybridization, genetic engineering, and tissue culture are examples of scientific methods that make it easier to identify superior traits in plants.

With increased speed and precision, CRISPR-Cas9 is a genome editing technology that enables high target specificity. Transgenic plants with desired characteristics, such as disease resistance, drought tolerance, pest resistance, and high yield capacity, are produced. This increases the farm's profitability. Additionally, startups use agri-biotech techniques to produce products like biopesticides, bioherbicides, biofertilizers, and bioplastics.

7. Analytics and Big Data

Common farm data is transformed into useful insights by big data and analytics techniques. The foundation for the upcoming farming season is laid by statistics on crop area, production, land use, irrigation, agricultural prices, weather forecasts, and crop diseases. Analytical tools are used to extract information about farm operations from data on weather events, agricultural machinery, water cycles, and the quality and quantity of crops. This makes it possible for growers to spot patterns and connections that might otherwise go undetected. A number of startups are providing farm analytics solutions that help farmers make the most of their field data.

Analytical data, for instance, promotes knowledge of the soil's nutrient levels, acidity, alkalinity, and fertiliser needs and enables data-driven decision-making.

8. Agriculture in a Controlled Environment

The use of conventional farming techniques is constantly hampered by erratic and extreme weather events. Furthermore, it can be extremely difficult to grow crops in densely populated areas, deserts, or other unfavorable environments. Controlled-environment agriculture (CEA) helps to overcome this. Plants are exposed to a predetermined ratio of light, temperature, humidity, and nutrients in CEA. There are various growing environments, including greenhouses, indoor farming, and vertical farming, among others. Techniques like hydroponics and aeroponics, which grow soilless plants in a liquid nutrient medium or steam, are being used more frequently.

Another such method is aquaponics, which simultaneously raises fish and plants. Plants purify the water for the fish, while fish provide nutrients to the plants. CEA techniques increase yield, decrease pests and diseases, and create sustainable farming.

9. Regenerative Farming

Traditional farming methods cause soil to crust over and erode over time. Often, the overgrazing, tilling, and plowing don't give the soil much time to recover before the subsequent growing season. On the other hand, regenerative agriculture prioritizes improving soil biodiversity and topsoil revival while causing little to no soil disturbance. It includes a variety of techniques, including crop rotation, no-till farming, and reduced tillage. For instance, to reestablish soil fertility, cover crops are sown to cover the soil in between cropping seasons. Additionally, through sequestration, regenerative farming enables fields to act as carbon sinks. This has a lower effect on climate change and results in fewer carbon emissions into the atmosphere.

10. Technology for Connectivity

Without connectivity technologies like 5G, LPWAN, rural broadband, or satellite-enabled communication, smart farming is not possible. Robots, sensors, and Internet of Things (IoT) devices can communicate quickly thanks to 5G, which makes it easier for them to be adopted. Farmers can now more accurately and immediately monitor the data and take the necessary action. Real-time field data exchange is made possible by high-speed internet using fibre optic cables, which is essential for increasing accuracy. In the end, IoT and other technologies like connectivity support each other to create connected farms.

Conclusion

The demand for sustainable and effective farming practices, as well as developments in technology and scientific research, all drive ongoing trends and innovations in agricultural practices. For farmers and other stakeholders in the agriculture industry, staying current on news and analysis in this area is essential.

Sustainable agriculture, which includes techniques like organic farming, precision agriculture, and regenerative farming, has received more attention in recent years. These methods prioritize preserving biodiversity, soil health, and natural resources while minimising the negative effects of agriculture on the environment.

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[terminusdb]

Why analytics is the key to accelerating business success

If business leaders have learned anything from the past two years, it is that resilience and agility are imperative to success in our 'new normal'. We have seen how the past no longer reliably predicts the future. Companies that switch quickly can disrupt their markets and take advantage of this.

TerminusDB is an open-source document graph database featuring collaboration and workflow tools to build concurrently with your team.

This greater flexibility is necessary to do well in changing times. This is made possible by data, in particular by putting more relevant data into analytics workflows faster. And it is worth the investment. According to analytics and AI leaders outperform laggards by 3.4 times. To see this success, leaders need to know which elements are most important to a robust, forward-looking strategy to accelerate success through analytics.

Becoming more agile for sustainable business success requires a focus on four dimensions The four dimensions that drive agility through analytics are; people, processes, tools and data. Data is the key here. When organizations harness the power of their data, they are ready to innovate, collaborate and grow. But turning data into a valuable asset that leads to improved resilience and agility takes more than just data and IT efforts.

To gain value, business leaders and stakeholders must collaborate with IT on these four dimensions. Business leaders who focus organizational strategy on transformative outcomes are in a unique position to contribute to the 'people' and 'process' dimensions.

Analytics: the refinery that turns data into business value If data is the new oil, analytics processes are the refineries that provide the fuel for growth and optimization. But not all end products from that refinery will be equally valuable to business success. By aligning analytics with business strategy, expressed in KPIs, it becomes clearer which datasets, tools and skills are most relevant for new insights, resilience and agility.

The refinery should include artificial intelligence (AI) and machine learning (ML), as these technologies are key to automating analytical insights at scale and enabling continuous closed-loop intelligence.Lleaders with robust machine learning "increase the value they realize from their AI work by as much as 60 percent."

The key to accelerating business success: it's not all about technology There is tangible value in being a leader in analytics and AI. According to leaders in their respective industries outperform laggards about 8%. 

Why the drop? While many discussions about AI and ML focus on the technologies and technical skills needed, that's only part of the picture. 

On the contrary, 93% of respondents identify people and processes as the obstacle ."

Accelerating business success through analytics requires executive sponsorship, alignment with business strategy, and a willingness to view analytics, AI, and ML as strategies for future optimization and innovation. Business leaders and stakeholders should help define successes; they also need to identify use cases that are most relevant to their industry and most valuable to their business. Every company is different and needs a different approach.

Let's look at two practical examples. In the manufacturing industry, Hemlock Semiconductor has transformed its business through analytics. This provides the insights needed to generate revenue and save millions of dollars in costs through quality management and reduced energy consumption. Looking at agribusiness, Bayer is using Crop Science analytics to transform drone image data, historical data and streaming data into a precise approach to farming. The results include operational efficiency (20% higher throughput and lower costs), as well as innovation for market disruption (new crop varieties for precision farming).

Recommended next steps Here are a few next steps to increase the positive impact of analytics and accelerate business success. • Take on a leadership role. If your company doesn't already have an Analytics Center of Excellence, or something similar, with business stakeholders in leadership roles, do it. The success of analytics rests on the 'people' and 'process' facets of closed-loop continuous learning. • Align analytics results with business strategy. Business leaders can and should define a vision: the most valuable use cases, the most salient metrics. Specific metrics can include the overall impact and ROI of AI, including market disruption and innovative capacity, as well as specific optimization results. Using analytics to accelerate business success is a journey that takes persistence and time. With your leadership as a business partner in this journey, the odds are in your organization's favor.

[robertcharley]

5 Innovative Machine Learning Trends in 2020 - Machine Learning

5 Innovative Machine Learning Trends in 2020 – Machine Learning

AI is on target to be worth around USD 9 billion by 2023. This is because of the way that the Machine Learning  business is blasting at a fast rate. The effect of such innovation goes past the web business. They will arrive at the agribusiness, legitimate, vehicle, and well-being enterprises. Individuals are progressively concerned and keen on knowing how the joining of ML can profit their…

[pathfinderresearch]

IoT-Enabled Soil Fertility Assessment and Crop Selection for Sustainable Agribusiness

The agribusiness sector is rapidly evolving, with technology playing a critical role in improving productivity and sustainability. In today’s world, data-driven decision-making is essential for optimizing farming practices, enhancing crop yield, and ensuring long-term soil health. At Pathfinder Research and Training Foundation (PRTF), we are dedicated to fostering agri-entrepreneurs and future agribusiness leaders by providing incubation support and an MBA in Agribusiness Management to equip students with industry-relevant skills.

One of the most promising technological advancements in modern agriculture is the integration of IoT (Internet of Things) and Machine Learning (ML) for soil fertility assessment and crop selection. These technologies empower agribusiness professionals to make informed decisions, maximize farm profitability, and promote sustainable agricultural practices.

The Need for Smart Agriculture in Agribusiness

Agriculture-based businesses require accurate data on soil quality, weather patterns, and crop suitability to ensure efficient supply chain management and profitability. Many agribusiness startups face challenges such as: ✅Inconsistent crop yields due to poor soil management ✅Excessive use of fertilizers leading to soil degradation ✅Inefficient resource utilization, affecting production costs ✅Lack of real-time data for decision-making

By leveraging IoT and ML, agribusiness professionals can develop smart farming solutions that improve productivity, reduce costs, and ensure sustainable agricultural practices.

How IoT and Machine Learning Are Transforming Agribusiness

1. IoT for Real-Time Soil Monitoring

IoT-based smart sensors can be deployed on farms to monitor:

Soil fertility (NPK levels, pH, organic matter)

Moisture levels for optimized irrigation

Weather conditions affecting crop growth

This real-time data is critical for agribusiness startups, as it helps predict crop yield, input costs, and profitability.

2. Machine Learning for Data-Driven Decision-Making

ML algorithms analyze soil and environmental data to:

Recommend the most suitable crops for maximum yield

Optimize resource utilization (water, fertilizers, pesticides)

Predict market trends and crop demand, helping agribusinesses plan better

This enables entrepreneurs in the agri-startup ecosystem to make smart business decisions and reduce financial risks.

3. Smart Solutions for Agribusiness Incubation

At PRTF, we provide incubation support to agribusiness start-ups working on:

IoT-based precision farming solutions

AI-powered soil testing applications

Market-driven crop selection tools

By integrating IoT and ML-based analytics, agribusiness start-ups can offer innovative solutions that address farmers' challenges while ensuring business profitability.

Impact on the MBA in Agribusiness Management

Our MBA in Agribusiness Management program prepares students for leadership roles in agriculture technology, farm management, and sustainable agribusiness models. Students gain insights into: 📌Technology-driven decision-making in agriculture 📌Sustainable farming models for long-term profitability 📌Financial planning and risk management in agribusiness 📌Entrepreneurial opportunities in IoT-based agritech solutions

By understanding IoT-driven soil assessment and crop selection, students can develop innovative business models for precision agriculture, making them industry-ready professionals.

Conclusion

The integration of IoT and Machine Learning in agriculture is revolutionizing the agribusiness sector by improving soil health, enhancing crop selection, and ensuring sustainability. At Pathfinder Research and Training Foundation, we are committed to incubating agritechstartups and equipping MBA students with the skills to leverage technology for profitable agribusiness ventures.

🚀Join us atPathfinder Research and Training Foundationto explore the future of smart agribusiness and sustainable agriculture!