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How Machine Learning can Revolutionize the Agricultural Industry


How Machine Learning can Revolutionize the Agricultural Industry

Machine learning has evolved over the last few years along with other big data technologies and advanced computing to transform industries all over the world, and the agricultural sector is no exception.

With its advancements, machine learning in agriculture has been able to address a number of issues that the industry has been encountering.

Businesses can achieve success only when they constantly outperform their rivals in decision-making, and the agricultural sector is no exception. Through machine learning in agriculture, farmers now have access to more advanced data and analytics tools, facilitating better decisions, increased productivity, and decreased waste in the production of food and fuels, all while reducing unfavorable environmental effects.

How is Machine Learning a Great Fit for Agriculture?

With the assistance of highly precise algorithms, the growing idea of “smart farming” boosts the efficacy and productivity of agriculture. Machine learning — a branch of science that allows machines to learn without being explicitly programmed — is the mechanism behind it. To open up new possibilities for unraveling, analyzing, and comprehending data-intensive processes in agricultural organizational settings, it has evolved in tandem with big data technologies and powerful computers. Farmers can now predict agricultural yield and evaluate crop quality, determine plant species, and diagnose plant diseases and weed infestations at seemingly unimaginable levels, using sensors in the farm in accordance with ML-enabled electronic innovations. Throughout the entire cycle of planting, growing, and harvesting, machine learning in agriculture is prominent. It starts with sowing a seed, goes through soil testing, seed breeding, and water supply measurement, and concludes with robots collecting the harvest and using computer vision to assess its degree of ripeness. The amount of data accessible to farmers today is overwhelming without the aid of machine learning technology. Lots of data can be promptly assessed by ML, with the help of which it recommends the most profitable strategy. For instance, it can advise on when to plant in order to ward off pests and diseases. The advantages of digital farming are legitimate; it may assist growers in making the best input decisions in order to enhance production and profit. Furthermore, it can assist farmers in determining actual expenses on a field-by-field basis rather than only on a farm-wide one.

Applications of Machine Learning in Agriculture

Machine learning has extensively grown in the agricultural sector in recent years. Following are its applications in the farming industry:


Species Breeding and Recognition

The laborious process of species selection entails looking for particular genes that will guarantee efficient responsiveness to water and nutrients. Ideal plant species will be able to withstand climatic change, be disease-resistant, have more nutrients, and taste better.

For a thorough investigation of crop performance, machine learning enables us to extract information from decades of field data. This data is used to create a probability model that predicts which traits will give a plant a desirable genetic advantage.

Species identification in crops has typically been carried out using straightforward comparisons, such as the color and shape of the leaves. Utilizing more advanced approaches, such as assessing leaves with the help of vein morphology, machine learning allows us to evaluate plants in a way that is much more sophisticated, accurate, and quick.

Soil and Water Management

Machine learning algorithms examine evaporation dynamics, soil moisture, and temperature to comprehend ecosystem processes and their impact on agriculture.

The deficiencies in the soil can be taken care of by ML strategies. For example, machine learning technologies can help farmers maintain optimal amounts of inorganic nitrogen. The nitrogen cycle in the soil and the environment is predicted through nitrogen modeling, which directs the farmer to optimum levels. Software simulations can detect whether nitrogen is available and determine when to add nitrogen to the soil. On the other hand, it can notify the farmer when there is too much nitrogen present, which might damage the crops.

The use of irrigation systems can be made more efficient too, thanks to ML-based applications that estimate daily, weekly, or monthly evapotranspiration and predict the daily dew point temperature, which aids in predicting expected weather events and calculates evapotranspiration and evaporation.

Yield Prediction and Crop Quality

One of the most significant and well-known areas of precision agriculture is yield prediction, which encompasses mapping and assessment of yields, matching crop supply and demand, and crop management. Modern methods go well beyond simple forecasting based on historical data, incorporating computer vision technologies to deliver data instantly and thorough multidimensional analyses of crops, weather and economic situations to maximize production for farmers and the public at large.

Accurately identifying and categorizing agricultural quality attributes can raise product prices and minimize wastage. In contrast to human specialists, machines can employ seemingly pointless data and connections to expose and discover new attributes that contribute to the overall quality of crops.

Disease and Weed Detection

Significant amounts of pesticides must be sprayed over the crop area to combat disease, which frequently has a high financial cost and a considerable environmental impact. When using general precision agriculture management, ML is used to target the application of agrochemicals based on the time, location and plants that will be affected.

Weeds pose a serious threat to the growth of crops. Weeds are tricky to identify and distinguish from crops, which presents the biggest challenge in weed control. With minimal expense and no negative effects on the environment, computer vision and machine learning algorithms in agriculture can enhance the identification and discrimination of weeds. Future models of this technology will power weed-destroying robots, minimizing the need for herbicides.

Livestock Production and Animal Welfare

To maximize the economic effectiveness of livestock production systems, such as the production of cattle and eggs, machine learning enables precise prediction and prediction of farming aspects. For instance, 150 days before the day of slaughter, weight prediction systems can anticipate future weights, enabling farmers to adjust their diets and environmental factors accordingly.

Today’s livestock is increasingly viewed as animals who can be unhappy and worn out by their life on a farm rather than just as food carriers. Animals’ movement patterns, such as standing, moving, eating, and drinking, can determine how much stress an animal is exposed to and forecast its susceptibility to diseases, weight increase, and productivity. Animals’ chewing signals can be linked to the need for food adjustments.

Models Used

Agricultural machine learning is not some enigmatic gimmick or magic; rather, it is a collection of well-specified models that gather particular data and employ methodological approaches to get the desired outcome.

Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) are two very popular machine learning models being utilized for agriculture.

ANNs are models of biological neural networks that mimic complex activities like pattern production, reasoning, learning and judgment. They are inspired by how the human brain functions.

SVMs are binary classifiers that divide data instances into categories using a linear separation hyperplane. Clustering, regression and classification are all performed using SVMs. They are utilized in farming to estimate animal production and crop productivity and quality. 

Additionally, Farmer’s Chatbots are now under development. Instead of just providing numbers, these would be able to evaluate the data and consult farmers on complex issues, and hence are predicted to be even smarter than consumer-oriented Alexa and similar assistants.


Machine learning breakthroughs have incredible potential, much like software. Agriculture scientists are putting their theories to the test on a larger scale and assisting in the development of more precise, real-time prediction models pertaining to crops. Machine learning in agriculture has the capacity to provide even more solutions for sustaining the world’s population, coping with climate change and conserving natural resources.


Currently, machine learning solutions focus on specific issues, but as automated data collection, analysis, and decision-making are further integrated into a connected system, many farming activities will change to what is known to be knowledge-based agriculture, which will be able to boost output and product quality.  


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Vodafone Ireland turns to Amdocs to drive enhanced customer experience


Cloud Computing News

Duncan is an award-winning editor with more than 20 years experience in journalism. Having launched his tech journalism career as editor of Arabian Computer News in Dubai, he has since edited an array of tech and digital marketing publications, including Computer Business Review, TechWeekEurope, Figaro Digital, Digit and Marketing Gazette.

Vodafone Ireland has chosen Amdocs, a provider of software and services to communications and media companies, to transition its infrastructure and application workloads to the cloud, enabling an enhanced customer experience and rapid adoption of the latest 5G innovations.

Under the agreement, Amdocs Customer Experience Suite (CES) will migrate from Vodafone Ireland on-premise to the cloud, providing the Irish operator with greater flexibility and capacity to support its future growth.  

Mairead Cullen, CIO at Vodafone Ireland, said: “Moving to the cloud is a key part of our strategy as we look to become even more dynamic, agile and responsive to our customers’ needs. We have a long-standing relationship with Amdocs and we’re pleased to be collaborating with them on this important initiative.”

Anthony Goonetilleke, group president of technology and head of strategy at Amdocs, said: “By migrating its IT services infrastructure to the cloud, Vodafone Ireland can ensure it has the foundations in place to achieve growth and further enhance the experience of its customers.

“We are excited to be taking such a central role in the company’s cloud strategy.”

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How to Align Data and Analytics Governance with Business Outcomes


How to Align Data and Analytics Governance with Business Outcomes

With access to large amounts of data made available to businesses, maintaining and governing the kind of data that is accessible to users have become significantly essential.

Proper data and analytics governance in organizations can help them in achieving on-point data and analytics processes.

The use of data and analytics is increasing across practically all industries. Due to the availability of inexpensive storage alternatives, organizations have access to more data. It’s not surprising that the usage of analytics due to access to extensive data has expanded to every part of the company when you take into account the growing number of user-friendly tools for managing, retrieving, and analyzing data. 

However, a lot of effort goes into managing data and analytics. Thus, organizations must ensure that their efforts are aligned with their business priorities, and the data is accurate in nature and thoroughly secured. Without analytics governance, even if the organization has a good hold on its data governance policies, the advantages of establishing policies and processes to govern the analytics process still stand. As data governance guarantees your business has processes and standards around the use of data, analytics governance provides the same level of oversight to the way analytics initiatives are built and delivered.

Aligning Data and Analytics Governance

Data and analytics governance initiatives must be closely related to organizational strategies. However, businesses frequently base their data and analytics governance processes on data rather than the business. Here are a few points on how businesses can align their data and analytics governance with their business outcomes.


Trusted Governance

Forming business decisions based on the notion that “all data is equal” is no longer a sound strategy because data and analytics capabilities exist across a company and differ in nature. Instead, create a paradigm of trust-based governance that allows for a dispersed data and analytics ecosystem and is able to help business executives make decisions that are more confidently appropriate to the circumstances.


With the essence of developing technology, digitization has taken over almost every business to stay relevant in the market. However, for businesses to gain the best outcomes from the digital space, digitization is essential. And for successful digitization, data and analytics governance must function based on factors like digital ethics and transparency. Therefore, ensuring that the values and concepts of digitization are reflected in the data and analytics governance is crucial to significantly align it with business outcomes.

Data Security

Today, organizations are aware of the potential risks associated with their businesses and securing data has become a necessity. This awareness implies that they address both the threats and the possibilities brought about by data and analytics. Organizations frequently manage risk and market potential independently, and they also do not really prioritize information security when assessing business results. Therefore, data and analytics governance authorities should have interdisciplinary teams capable of making decisions that are well-balanced, giving risk, opportunities, and security the appropriate weight while considering the organizations’ future interests in mind.


Today, businesses are aware of the fact that without effective data and analytics governance, their initiatives and investments in data and analytics won’t be able to satisfy important organizational goals like increased revenue, cost reduction, and improved customer experiences. Therefore, aligning it with business outcomes is critical for business success.


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IBM launches new way to partner through IBM Partner Plus


Cloud Computing News

IBM has developed IBM Partner Plus, a new program that reimagines how IBM engages with its business partners through unprecedented access to IBM resources, incentives, and tailored support to deepen their technical expertise and help speed time to market.

The program is designed to fuel growth for new and existing partners, including resellers, hyperscalers, technology providers, independent software vendors and systems integrators, by putting them in control of their earning potential. IBM Partner Plus is central to the company’s Hybrid Cloud and AI strategy and aims to empower partners to help clients automate, secure and modernize their businesses.

IBM Partner Plus offers partners a transparent, simple and modern experience. By growing technical expertise and demonstrating sales success, participants can progress to three tiers – Silver, Gold and Platinum – which unlock specialized financial, go-to-market support and education benefits. In the new program, badging will become the standardized measure of skills and validated solutions will demonstrate expertise. The enhanced IBM Partner Portal consolidates and tracks all expertise, revenue, and deals globally, offering each partner a clear line-of-site into their progression through the program.

“IBM Partner Plus introduces a new way for IBM to deliver value to new and existing partners by helping them gain skills, grow faster and earn more,” said Kate Woolley, GM, IBM Ecosystem. “We’ve heard from partners that they want a simplified experience that helps them win with clients. I’m confident these changes and our continued investment in our ecosystem will make IBM the partner of choice across the industry, and together we can drive growth for partners, clients, and IBM.”

IBM Partner Plus results from the company’s journey to put partners at the centre of IBM’s go-to-market strategy and act as a growth engine to help capture the $1 trillion hybrid cloud and AI market opportunities. IBM has invested in elevating the role of partners and accelerating partner-led sales by enabling the ecosystem to become a preferred route to market, offering clients an optimal mix of technology, services, and consulting expertise. To drive continued growth, IBM will increase its capacity to support partners by doubling the number of partner-facing brand and technical specialists to help them prospect and win additional client business.

“The new IBM Partner Plus program provides an enhanced experience that sets our company up for success by offering employees access to skills and opportunities, so we can help more clients utilise IBM’s technology portfolio to modernise their operations,” said Bo Gebbie, President, Evolving Solutions. “IBM is more serious than ever about putting partners first. They’ve listened to our feedback, and it is reflected in the new partner experience that makes it easy for us to collaborate, rewards our investments and fuel growth.”

IBM Partner Plus brings all partner types and programs together – whether they sell, build on or with, and/or provide services for IBM technology – into one integrated ecosystem. For example, to help broaden the market opportunity and create new revenue streams for its ecosystem, IBM recently enabled partners in North America to resell IBM products through other cloud marketplaces. This allows for independent software vendors to embed IBM Software from partner marketplaces into their own solutions. All partner sales through the marketplace accumulate towards their progression in IBM Partner Plus. 

Competitive incentives

Partners can advance through tiers to unlock benefits and demand generation programs which could offer them up to a threefold increase in total investment from IBM. The IBM Partner Portal gives partners real-time visibility into the incentives they are eligible for, predictability into potential earnings, and includes an automated deal share engine that helps them surface quality leads. This has improved deal registration and introduced partners to more than 7,000 potential deals valued at over half a billion dollars globally.*  IBM investments in co-marketing campaigns and co-sell support with partners can also help bring solutions to market and generate demand.

Insider access

IBM Partner Plus builds on the successful release of its October badging and selling enablement materials to partners, which has driven more than 15,000 partner enrollments in sales and technical badges. Offering partners the training, enablement, and experiential selling resources available to IBMers at no cost can help better equip them to win with clients. Additionally, access to IBM’s seller tools can help them generate competitive and transparent pricing. Partners can also attend IBM’s quarterly Sales Kickoffs together with IBM sellers, and participate in live training sessions and other global technical advocacy events to help upskill, increase eminence, and engage with technical experts. For new partners, IBM is launching the IBM New Partner Accelerator, which provides onboarding, training, and other benefits during their first six months in the program to help accelerate their path to profitability.

Enhanced support and benefits

Partners can grow skills, develop solutions, and build sales expertise with technologies like AI, security, and cloud on an open hybrid cloud platform by leveraging technical experts from IBM. IBM will also assist partners in the development of minimal viable products, proofs of concept, and custom demos to help them win client business and accelerate growth. In addition, as partner businesses grow with IBM, they can unlock additional benefits designed to help them expand capabilities and find new clients.

PartnerWorld will transition to a new IBM Partner Plus experience on January 4, 2023, with the new incentive program taking effect on April 1, 2023. Registered PartnerWorld members will maintain their current tier through July 1, 2023 and can progress to the new tiering system during this time as they meet criteria.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London.

Explore other upcoming enterprise technology events and webinars powered by TechForge här.



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