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Precision Agriculture: Revolutionizing Farming with Cutting-Edge TechnologiesWhat Is Project Discovery Phase, Why it Matters, and How to Run It?

Burning deadlines, climbing costs, unmet expectations of partners, and scope creep turn your project into a nightmare. No worries, we’ve got a remedy for that: the discovery phase. With this homework adequately done, you will enjoy it the smooth way. So, let us dive right into it. Before we delve into the discovery phase of software development, let’s start with the broader perspective to understand the fundamental idea behind it better. Usually, a project’s lifecycle includes the following stages: initiation, planning, execution, control, and closure. Of course, the better base for your project you provide within the initiation stage, the more smooth way you and your team can follow later. However, reality dictates: that you can be under pressure to provide the deliverables, upfront research is a luxury you can not afford, or you might think that you know enough for launching. And then the particular adrenaline-generating stage begins, not mentioned in the project manager’s tutorials: scope creep, burning deadlines, climbing costs, and unmet expectations of your teammates, clients, and even yours. Thus, it is worth investing time and effort in the discovery phase of your project, both for fixed costs and agile ones. By the way, check out our blog if you are new to Agile methodology. In essence, the discovery phase (or business discovery stage or scoping phase) of a project is all about the research and the project’s scope defining. In other words, it is a preparatory stage of your project, when you collect and analyze information, and research your audience and intended market. Technically, the business discovery page leads to the system requirements specification (SRS), including details needed for development. Given the project’s scope, the discovery phase can take from one week to two months. Usually, the scoping phase involves the project manager, business analyst, and account manager. Depending on the project, UI/UX designers, developers, and team leads can join the discovery phase to help with wireframe prototypes, SRS, or scope assessment. A couple of meetings are required initially, and then the later process does not need constant input from your side. It depends on the idea of your project, but we try to give you some gist so that you can outline your scope of work. For example, the business scoping phase might imply the following activities in most cases: We advise running user research to define their pain points and goals and go deeper than operating with demographics. The next component of this step is to present the results to all the stakeholders, and customer journey mapping is among the most appropriate tools for it. Well, running a project without clear goals is possible, but it’s like using a banana instead of a gun at the shooting gallery. Speaking more formally, projects with no goals established are prone to scope creep, missed deadlines, and overspent budgets. Of course, one of the most popular indicators of success is a return on investment (ROI), but what if some stakeholders focus on the engagement of digital products, not on leads? It is better to decide it at the outset. We recommend benchmarking the success of your project against the existing services (e.g., previous version) or establishing your key performance indicators with your team. At this stage, it is time to define what exact value your project provides to the end-user and what features of your product or service are delivering those benefits. Otherwise, it is useless to communicate your product to the end-user. Finally, any digital product can not exist in a vacuum, so you should consider things like internal politics, compliance issues, or technical limitations. Simultaneously, it is worth spending time analyzing the competitive landscape and reviewing existing services or products. Aside from feasible outputs like UX prototypes, SRS, MVP, and estimates, you can enjoy the following benefits after the successful scoping phase: Risks mitigating When all your teammates have a clear vision of the project’s goals and requirements, it is natural that budget overspending and missed deadlines are less likely to happen. Staying on the same page The discovery phase is the best time to clarify all the questions and understand the peculiarities of your project with vendors. Thus, you can be sure that all stakeholders get the desired result, and it is much better to synchronize all the questions during the scoping phase than to make a pricey Change Request later. Project roadmap creating When you successfully finish the project’s discovery phase, you can have a detailed plan with timelines from ideation to launch. It is also possible to flexibly align it to any possible changes. Testing your IT vendor The discovery phase is high time to reveal whether you and your IT vendor speak the same language so that you can rely on them entirely during the project implementation. The discovery phase of AI-driven projects differs from the usual ones. For instance, stages like data cleaning and data infrastructure setup are much more linear than typical software development processes. Consequently, you might need a different project management methodology and focus on more processes and assets to manage. We hope that our template for your discovery phase within the AI project will come in handy. Enjoy! Outline prerequisites: define the end-user of the machine learning (ML) product and available data pools. As a rule of thumb, remember that the better data you have, the better outcomes your ML model can generate. However, usually, businesses do not have access to clean and structured data, and data analysts, data engineers, and data scientists can help with it. Usually, they iteratively clean data, gradually increasing the volumes of datasets, leading this process in parallel with other ones. Define data scope and infrastructure: you should consider the data infrastructure for your AI project. For example, find out the answers to the following questions at the outset. Where should you deploy your MLml models?” Who will manage the data to exclude the data decay? Is the data locality problem applicable to your case? Build your dream team: data analysts, data engineers, data scientists, or infrastructure engineers? Besides being specialists in the same domain, each of them might have their particular skill set to meet your expectations. Also, define the single point of contact (SPOC) on the client’s side at the outset. Set goals and define success: how will you benchmark your AI product to measure its effectiveness? You might have some brainstorming sessions with your team to set your own KPIs for the project. We recommend prioritizing your MVPs, as a working prototype of a product is better than presenting an unfinished one. To make the best of the discovery phase, consider the following: Let the discovery phase be proportional to your project People often ignore the scoping stage as they think of this as a time-consuming practice. However, the trick is to make it proportional — a single short meeting with the crucial stakeholders for a quick project. Plan a couple of sessions for long-term projects. The output you get is precious in any case. Make sure everyone is included in the discovery phase By default, you will benefit when all of your team that can impact the project is involved in the scoping phase. Thus, it helps to establish communication at the outset and escape the misunderstandings further. Spare some time for user interview It is always better than relying on personas or rough demographic data only. Make sure that your colleagues are involved. At least they can watch the interview recordings. Communicate with all stakeholders It helps minimize the risks when some potential influencers can express their dissatisfaction in the middle of the project. Again, the scoping phase — is the time when all the expectations can be met. Let your discovery phase be a small project with defined deliverables It is beneficial for outside agencies, as you can try the working relationships with your new client and move to the first small goals set. The discovery phase is the practice of collecting and analyzing all the relevant information regarding your project, audience, and intended market, setting clear goals, and defining the indicators of success. In other words, it is the preparation stage within the initiation stage of your project’s lifecycle. When successfully running, you can enjoy outputs like UX prototypes, SRS, MVP, and time and cost estimates that could be invaluable at the beginning of your project.

Machine Learning in Agriculture: Applications and Techniques

Recently we have discussed the emerging concept of smart farming that makes agriculture more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is Machine Learning — the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments. Machine learning is everywhere throughout the whole growing and harvesting cycle. It begins with a seed being planted in the soil — from the soil preparation, seeds breeding and water feed measurement — and it ends when robots pick up the harvest determining the ripeness with the help of computer vision. Let’s discover how agriculture can benefit from Machine Learning at every stage: Our favorite, this application is so logical and yet so unexpected, because mostly you read about harvest prediction or ambient conditions management at later stages. Species selection is a tedious process of searching for specific genes that determine the effectiveness of water and nutrient use, adaptation to climate change, disease resistance, as well as nutrient content or a better taste. Machine learning, in particular, deep learning algorithms, takes decades of field data to analyze crop performance in various climates and new characteristics developed in the process. Based on this data they can build a probability model that would predict which genes will most likely contribute a beneficial trait to a plant. While the traditional human approach for plant classification would be to compare the color and shape of leaves, machine learning can provide more accurate and faster results in analyzing the leaf vein morphology which carries more information about the leaf properties. For specialists involved in agriculture, soil is a heterogeneous natural resource, with complex processes and vague mechanisms. Its temperature alone can give insights into the climate change effects on regional yield. Machine learning algorithms study evaporation processes, soil moisture and temperature to understand the dynamics of ecosystems and the impingement in agriculture. Water management in agriculture impacts hydrological, climatological, and agronomical balance. So far, the most developed ML-based applications are connected with an estimation of daily, weekly, or monthly evapotranspiration allowing for a more effective use of irrigation systems and prediction of daily dew point temperature, which helps identify expected weather phenomena and estimate evapotranspiration and evaporation. Yield prediction is one of the most important and popular topics in precision agriculture as it defines yield mapping and estimation, matching of crop supply with demand, and crop management. State-of-the-art approaches have gone far beyond simple prediction based on historical data, but incorporate computer vision technologies to provide data on the go and comprehensive multidimensional analysis of crops, weather, and economic conditions to make the most of the yield for farmers and population. The accurate detection and classification of crop quality characteristics can increase product prices and reduce waste. In comparison with human experts, machines can make use of seemingly meaningless data and interconnections to reveal new qualities playing a role in the overall quality of the crops and to detect them. Both in open-air and greenhouse conditions, the most widely used practice in pest and disease control is to uniformly spray pesticides over the cropping area. To be effective, this approach requires significant amounts of pesticides which results in a high financial and significant environmental cost. ML is used as a part of general precision agriculture management, where agro-chemicals input is targeted in terms of time, place, and affected plants. Apart from diseases, weeds are the most important threats to crop production. The biggest problem in weed fighting is that they are difficult to detect and discriminate from crops. Computer vision and ML algorithms can improve the detection and discrimination of weeds at low cost and with no environmental issues or side effects. In the future, these technologies will drive robots that will destroy weeds, minimizing the need for herbicides. Similar to crop management, machine learning provides accurate prediction and estimation of farming parameters to optimize the economic efficiency of livestock production systems, such as cattle and egg production. For example, weight predicting systems can estimate the future weights 150 days prior to the slaughter day, allowing farmers to modify diets and conditions respectively. In present-day settings, the livestock is increasingly treated not just as food containers, but as animals who can be unhappy and exhausted of their life at a farm. Animal behavior classifiers can connect their chewing signals to the need for diet changes and by their movement patterns, including standing, moving, feeding, and drinking, they can tell the amount of stress the animal is exposed to and predict its susceptibility to diseases, weight gain, and production. This is an application that can be called a bonus: imagine a farmer sitting late at night and trying to figure out the next steps in the management of his crops. Whether he could sell more now to a local producer or head to a regional fair? He needs someone to talk through the various options to make a final decision. To help him, companies are now working on developing specialized chatbots that would be able to converse with farmers and provide them with valuable facts and analytics. Farmers’ chatbots are expected to be even smarter than consumer-oriented Alexa and similar helpers since they would be able not only to give figures, but analyze them and consult farmers on tough matters. Though it is always fascinating to read about the future, the most important part is the technology that paves the way for it. Agricultural machine learning, for instance, is not a mysterious trick or magic, but a set of well-defined models that collect specific data and apply specific algorithms to achieve expected results. So far, the distribution of machine learning is unequal throughout the agriculture. Mostly, machine learning techniques are used in crop management processes, followed by farming conditions management and livestock management. The literature review shows that the most popular models in agriculture are Artificial and Deep Neural Networks (ANNs and DL) and Support Vector Machines (SVMs). ANNs are inspired by the human brain functionality and represent a simplified model of the structure of the biological neural network emulating complex functions such as pattern generation, cognition, learning, and decision making. Such models are typically used for regression and classification tasks which prove their usefulness in crop management and detection of weeds, diseases, or specific characteristics. The recent development of ANNs into deep learning has expanded the scope of ANN applications in all domains, including agriculture. SVMs are binary classifiers that construct a linear separating hyperplane to classify data instances. SVMs are used for classification, regression, and clustering. In farming, they are used to predict yield and quality of crops as well as livestock production. More intricate tasks, such as animal welfare measurement, require different approaches, such as multiple classifier systems in ensemble learning or Bayesian models — probabilistic graphical models in which the analysis is undertaken within the context of Bayesian inference. Though still in the beginning of its journey, ML-driven farms are already evolving into artificial intelligence systems. At present, machine learning solutions tackle individual problems, but with further integration of automated data recording, data analysis, machine learning, and decision-making into an interconnected system, farming practices would change into the so-called knowledge-based agriculture that would be able to increase production levels and product quality.

Smart Farming, or the Future of Agriculture

We are a Ukraine-based company which means that our parents and grandparents lived in the era of infamous Soviet collective farms, where tractors were considered to be the ultimate technology. For them, a smart farm will sound like a fairy tale. So let it be, a fairy tale of a smart farm. First of all, what is a smart farm? Smart Farming is a concept of farming management using modern Information and Communication Technologies to increase the quantity and quality of products. Among the technologies available for present-day farmers there are _Observation_ _— sensors record observational data from the crops, livestock, the soil, or the atmosphere._ _Diagnostics_ _— the sensor values are fed to specific software with predefined decision rules and models that ascertain the condition of the examined object and any deficiencies or needs._ _Decisions_ _— after issues are revealed, the software determines whether location-specific treatment is necessary and if so, which._ _Implementation_ _— the treatment needs to be performed by means of the correct operation of machines._ After evaluation, the cycle repeats from the beginning. It is believed that the IoT can add value to all areas of farming from growing crops to forestry. In this blog post, we’ll talk about two big spheres where IoT systems can revolutionize agriculture: precision farming and farming automation/robotization. Precision farming, or precision agriculture, is an umbrella notion for IoT-based approaches that make farming more controlled and accurate. In simple words, plants and cattle get precisely the treatment they need, determined with great accuracy. The biggest difference from the classical approach is that precision farming allows decisions to be made per square meter or even per plant/animal rather than for a field. By precisely measuring variations within a field, farmers can boost the effectiveness of pesticides and fertilizers, or use them selectively. Like in the case of precision agriculture, Smart Farming techniques, enable farmers to better monitor the needs of individual animals and adjust their nutrition correspondingly, thereby preventing disease and enhancing herd health. Besides, large farm owners can use wireless IoT applications to monitor the location, well-being, and health of their cattle. With this information, they can identify animals that are sick so they can be separated from the herd, and prevent the spread of disease. Traditional greenhouses control the environmental parameters through manual intervention or a proportional control mechanism which often results in production loss, energy loss, and increased labor cost. An IoT-driven smart greenhouse intelligently monitors as well as controls the climate, eliminating the need for manual intervention. To do so, different sensors that measure the environmental parameters according to the plant requirement are used and stored in a cloud for further processing and control with minimal manual intervention. Agriculture is one of the major industries to incorporate both ground-based and aerial drones for crop health assessment, irrigation, crop monitoring, crop spraying, planting, soil and field analysis, and other spheres. Since drones collect multispectral, thermal, and visual imagery during the flight, the collected data provide farmers with insights into plant health indices, plant counting and yield prediction, plant height measurement, canopy cover mapping, field water ponding mapping, scouting reports, stockpile measuring, chlorophyll measurement, nitrogen content in wheat, drainage mapping, weed pressure mapping, and so on. Importantly, IoT-based smart farming targets not only large-scale farming operations, but can also add value to growing trends in agriculture like organic farming, family farming, including breeding particular cattle and/or growing specific cultures, preservation of particular or high-quality varieties, etc., and enhance highly transparent farming to consumers, society and market consciousness. If we have the Internet of Things and the Internet of Medical Things, why not have one for food? The European Commission project Internet of Food and Farm 2020 (IoF2020), a part of Horizon 2020 Industrial Leadership, explores through research and regular conferences the potential of IoT technologies for the European food and farming industry. It is believed that the potential of a smart web of sensors, actuators, cameras, robots, drones, and other connected devices brings an unprecedented level of control and automated decision-making and makes it possible to build a lasting innovative ecosystem. Smart Farming and IoT-driven agriculture pave the way for what can be called a Third Green Revolution. Following the plant breeding and genetics revolutions, the Third Green Revolution is taking over agriculture based upon the combined application of Information and Communication Technologies such as precision equipment, the Internet of Things, sensors and actuators, geo-positioning systems, Big Data, Unmanned Aerial Vehicles (UAVs, drones), robotics, etc. In the future depicted by this revolution, pesticide and fertilizer use will drop while overall efficiency will be optimized. IoT technologies will enable better traceability of food which in turn will lead to increased food safety. It will also be beneficial for the environment, for example, through more efficient use of water, or optimization of treatments and inputs. Therefore, Smart Farming has a real potential to deliver a more productive and sustainable agricultural production, based on a more precise and resource-efficient approach. New farms will finally realize the eternal dream of mankind and feed our growing population that may reach 9.6 billion by 2050.