Leaf area (LA) is an important parameter in many plant modeling studies.There is a need for a simple, accurate and non-destructive model to predict LA in physiological experiments in which destructive LA sampling is not allowed (e.g., rare plants or genetically segregating populations). In this study, a model for LA estimation was developed for Plumeria rubra L. using simple linear measurements of leaf length (L) and maximum width (W). Two experiments were carried out, one in 2012 (on seven commercial genotypes: ‘California Sunset’, ‘Christina’, ‘Elsie’, ‘Gina’, ‘J.J. Mini White’, ‘Panittas Red’, and ‘Pixie Dust’) and one in 2013 (on one genotype ‘Divine’) under greenhouse conditions, to test whether a model could be developed to estimate LAs of P. rubra across genotypes. Regression analysis of LA vs. L and/or W revealed several models that could be used to estimate the area of individual Plumeria leaves.To develop a model to estimate individual LA values accurately for all genotypes of P. rubra, measurements of both L and W should be included. The proposed linear model [LA = 4.15 _ 0.66 (L _ W)] was adopted for its accuracy, highest R2 value (> 0.96), smallest mean square error (MSE), and smallest predicted residual error in the sum of squares (PRESS), and whose PRESS value was close to the sum of squares for error (SSE). Validation of the model using L _W measurements of leaves was carried out on an independent dataset derived from another genotype (‘Divine’) in 2013. Correlation coefficients showed that there were strong relationships between predicted LAs and observed LAs, with an over-estimation of 4.0% in the prediction. This model can therefore be adopted reliably to estimate LAs in P. rubra, non-destructively.
It was interesting and challenging way but we have gone it. Finally, we have completely new Petiole.LeafArea, an Android based mobile application for biology, plant physiology, agriculture chemistry and ecology. We re-write it from scratch. Now Petiole measures not only the specific leaf area of an individual plant leaf but can measure total leaf area. Petiole will help to measure specific leaf area and total plant leaf area in taps, without any ImageJ or leaf area scanners.
Also, this version includes behind the scenes improvements to Petiole app stability and stamps of our pesky bugs.
By the way, our Petiole.LeafArea is absolutely FREE now. Don’t forget to take advantage of this.
The atmospheric [CO2] in which crops grow today is greater than at any point in their domestication history and represents an opportunity for positive effects on seed yield that can counteract the negative effects of greater heat and drought this century. In order to maximize yields under future atmospheric [CO2], we need to identify and study crop cultivars that respond most favorably to elevated [CO2] and understand the mechanisms contributing to their responsiveness. Soybean (Glycine max Merr.) is a widely grown oilseed crop and shows genetic variation in response to elevated [CO2].
Trees in big cities are adopted for the surrounded conditions more than the same species of wood put down their roots in the natural conditions. In other words, we are speaking again about the influence of salt-affected soil, metal heavy, gases, etc.
Level of development for a tree was defined by the degree of its organs’ development, particularly leaves. We researched differences between the natural territory, for example, parks, green areas – the leaves were larger and contain less heavy metals there. Instead, leaves grown in the streets were not so. We measured the individual surface area. For this task we scanned leaves, put images into a PC software, it calculates shaded pixels, then these pixels were transferred to the area and this process took a lot of time. By the way, we used the unlicensed program. It seemed that it worked, brought some results but we cannot prove them and think that these results were accurate. This method was the simplest. Other methods where scientific-based, for example, “carving” method, when we were doing die-cutting of leaves but again it was not precision. Taking the leaf and die-cutting from the other leaf, the measurements are not the same.
Grid count method using millimetre graph paper bring the same results. Human error is presented: one person has seen a small square but another person has not seen. Measurements of leaf area are influenced by a human factor too much.
Correlation index is also discussed in the scientific literature but not works perfectly for our tasks.
In other words, individual leaf surface measurement was very time-consuming and exhaustive work. We had 6 variations with 3 replicates. In fact, one breed of the tree gave 2400 leaves. We had 5 – 6 tree breeds. So we had to take 2400 leaves from each breed and measure leaf area of all leaves. We spent 2 – 3 days for 1 variation. We have done only sampling and have not measured leaf area of all 2400 leaves but calculated the average measurement. But with Petiole we measure the area of 10 – 15 leaves in a minute.
Hello to all readers of Geektimes. Today we have a chance to chat about real development of own ideas with Andrii Seleznov, co-founder of startup Petiole. We will try to avoid winning relations and suggestions on how it is easy (or difficult) and enjoyable to feel media exposure on yourself.
Instead of this, we want to write about quite a lengthy process: about how a new idea is emerging, about the theoretical underpinning of author(s) knowledge, about how an idea gains its meaningful characters, and foremost, how it appears and imposes the auditory. We mean the honest story about finding investors and further development.
We would like to avoid any advertisements but should say a couple of words. What is meant here is technically special-purpose application Petiole, that allows measuring leaf area of plants and chlorophyll content with the help of a camera of a mobile phone. Let’s start from the very first jump.
At first sight, Petiole has a very specific task. How have you come up with the idea to get this problem? What is the potential auditory for this application?
Sure, Petiole addresses a quite specific problem. And this is our peculiarity. The idea had appeared when the problem had lived with me for three months. To be more exact, I had shared a room with my friend Vyacheslav Bykov. At that time he was working at the Institute of Evolutionary Ecology of the National Academy of Science of Ukraine. He was researching the influence of negative factors on Lombardy Poplar (Populus pyramidalis). For this task, he had measured leaf area.
The process looked like budget-friendly and not in an optimum way — a scanner, Photoshop, ImageJ via command line, tabulating data in Excel. Said that licensed software is used in the mode of “endless demo-version” and this had added salt to this type of research. Vyacheslav had come home late, having green fingers because of permanent contact with dried and fresh leaves.
I had wanted to help him and in one evening tried to be clever that this task can be solved using a simple mobile phone. One thing led to another, and things were off and rolling, and finally, we succeeded and realized this. Potential auditory for our application is у приложения various. First of all, it is biologists, ecologists and other scientists who research plant morphology and plant physiology. Then we have become interesting for scientists in agrochemistry. After that, we have added chlorophyll measurement and started to receive calls from the big agtech companies.
However, in numbers, our performance differs from the auditory of popular social networks. Now we have 88 active users every month. The growth is not very large from month to month but we have it and it is our small reason to be happy.
OK, so hang on the “this task can be solved using a simple mobile phone” and “One thing led to another, and things were off and rolling, and finally we succeeded and realized this”. Tell us, please, about your experience in programming and how much time has it taken to make the working code of Petiole?
Programming experience is different and quite exotic. I had written my first computer program when I was nine, using the programming language FoxPro 2.5. My parents worked at the Computation Centre of Donetsk Railway. After school, I liked to visit them and look at fat books with source code. Usually, I covered them with my drawings. After this, I had played with Pascal, Delphi, Qt, Ruby on Rails. I want to mention that I do not have the classical computer education; in my diploma, I am an engineer – a spatial planner.
And at the “galleys” (it is a jargon name of outsourcing IT-companies in Ukraine) I have worked not more than two years. But this does not hinder me and my young team to write working code for Petiole in approximately seven months (with breaks). Taking into account that we have rewritten the basic algorithm three times.
As the process progresses, was it clear that the agtech project will have its future? Have you launched the blog, were you seeking links to the investors, did you contact with your target auditory?
No, approximately until half of the way, the project was aimed to solve the problem of one person. But in June last year, we became a finalist at GIST Tech-I. And from this moment, the project has acquired global tasks and purposes. The blog was created three months ago. As for investors – we actively contacted them in autumn last year. And we try to keep constant contact with our target auditory and receive feedback from them. Mainly, our users are Ukrainian scientists.
Can you tell more about GISTech-I. Sure, it is possible to google everything but we are interested in your personal attitude to this. Why is it beneficial, cool, useful? By the way, how big is your team?
GIST Tech-I is a global competition in technical and scientific projects for developing countries. It is organized with the support of the U.S. Department of State and is implemented by AAAS (American Association for the Advancement of Science). Superlative of the competition takes a course as part of the Global Entrepreneurship Summit. Besides the point, we have become the first finalists from Ukraine for all the time of this competition from 2011. The main difference of this event from the other competitions is a participation of the teams only from the developing countries. Projects were assessed by the criteria of orientation to macroeconomic indexes for society and their improvement. Personally, the semifinal experience was priceless for us. It required gathering as many votes for our project as possible during the on-line voting for one month. The main point of this voting is that one person can vote every day.
We got 5775 votes and this helped us to win 15th place in the category “Ideas” and go to the final. The final consisted of two days training (how to establish a company, marketing, promotion in social networks) and three days of participation in the Summit with a pitch of the project on the big stage. Personally, I have obtained many impressions about Africa as well as about the summit and the competition. The main point – I have met great people from Africa, Mexico, Kazakhstan, Azerbaijan, Malaysia, Chile, Peru, broaden horizons and the first time in my life – flew in planes.
We have 8 — 10 people in our team, who greatly help us in different stages of project formation. Unfortunately, we do not have a budget to pay a salary. But despite this people have worked in their spare time and for our idea. Mainly, they are my friends from the scholarship program “Zavtra.UA”.
Where did this event take place? How was it? With whom have you been in competition? And how have you appeared on this list?
The event took place in Nairobi – the capital of Kenya. The first two days of the training were hosted by Conference Hall of the 5-stars hotel, Sarova Stanley. Other three days – at the territory of the United Nations Office. In general, a work environment was pretty good. Every finalist had a target to win. Constant training of pitch and preparations of the presentation till 3.00 AM. Our project entered in competition within the section “Ideas”. I believe that all 15 participants were well-matched. The rivalry was quite strong, and my weak speaking English a little bit decreased my chances for winning. Major awards were obtained by the teams from Africa and South America.
We have been selected to the list according to results of “Startup Open 2015”. This is a global startup competition, including developed countries. It was an unexpected pleasure for us. And I am proud, that our project was listed among such startups like Lishot, AeroAnalytics, AppleDoc, BethClip and agtech startup Smart Mobile Farming.
ОК, so let’s come back to the practical side of the question. The application existed even before you took part in the GIST Tech-I. After this, you participated in different events. What has been changed? On the contrary: what feedback are you receiving now? From the agtech industry, scientists or agronomists?
Sure, the working application existed even before we participated in GIST Tech-I. But, firstly, we used an algorithm with calibration square. It was providing not accurate measurements. We often experienced a systematic error in two square centimetres. Plus instability of the application in general. Also, we did not implement a function of chlorophyll content measurement using a dark green colour index. Secondly – in sprint 2015, active development of the application was postponed. Generally, I forgot to tell that we also have a data cloud for collecting measurements of all users. But this is a separate story.
Then we had a prototype with basic functionality. In autumn 2015 and in spring 2016 we took part in approximately ten events (conferences, fairs, exhibitions, scientific picnics), where we had told, shown how the app works and what is possible to do with this. Above all, we had demonstrations at the “Scientific Picnics” in Kyiv. In the narrowest sense, there we had our first public demonstration in summer 2014. The main change for us – people have started to understand that a smartphone could be used not only for games but for solving difficult tasks. We got requests from the different universities. Even we won the third-place award at the first Ukrainian agtech hackathon (in Ternopil).
Now we have greater feedback from the scientists. Recently I have got a mail from our customer in the Ternopil National Pedagogical University, Department of Biology and Chemistry. They have tested Petiole with a fine-tooth comb in the context of leaf area measurement. The result – an area statistically is correct, measurements are conducting in a convenient and quick way for users.
From the agronomists, we will receive feedback but only in 2016. The main reason is that generally, agronomists are not interested in agtech application. They suggest a competitor in it. Our client is an owner of agribusiness, who would like to make the agronomist’s work automatic and completely exclude him from the decision chain. But our application in existing form – to go through the fields with an app – is not particularly interesting for them (in Ukraine). The total area of an average agriculture company is starting from 10 000 hectares. If we consider South Korea, Petiole is an ideal solution for them since an average area of a farm is two hectares.
As it is clear from the photos, additionally to the application your user must have a calibration pad or something like that? Can you tell us the details? How much does this cost? Does it influence on the final result of using Petiole?
The calibration pad is a necessary and honey-drop addition to the mobile application. In our marketing materials, I always forget to explain that it is essential only for leaf area measurement. For measurement of chlorophyll content as an agtech feature of our app, it is not required at all. Why have we decided to make the calibration pad for area measurement? It helps to decrease the complexity of an algorithm many times. And also it increases overall performance. In my presentations, I like to say in my presentations that having the stand you can do three things with two hands. Keep smart-phone, keep a plant leaf and press the buttons on the screen of the smart-phone. The first version was made using aluminium. Quite expensive but it was possible to change the height of the stand.
Experienced folks advised me casting plastics. But after hearing the production price of the shape for casting, we decided to wait a little bit with this idea. Then, by chance, I went to the big construction retail shop and had seen steps of beech and anti-glare sheets of polystyrene. And understood that this is what I need. All of these materials are cutting on a frazer, and is united with velcros.
The production cost of this stand is our commercial secret. Selling price — thirty (30) USD. For academic use we provide stands free of charge. Eight stands have already gone. Sure, its availability influences on the decision to use Petiole. All we dream simply to put a smartphone’s camera to a plant and get just about the complete chemical makeup. But if a person wants to solve this problem, there are some hidden ways. We also help with this and offer to download a file with check-board and instead of a special stand to use a stack of books. Two weeks ago our agtech app was reviewed by Greenappsandweb. Using this method they could make the whole cycle of measurements without a stand.
Ok. Broadly speaking, what is the algorithm of the mobile app? What libraries are in use? What problems have you had and what sources or agtech consultants had you used?
On Android-level we use OpenCV library for computer vision. In the application, at this moment we use two algorithms. The first is measuring leaf area. The second is defining the dark-green index. For the leaf area, we are checking the matrix of homography between the flat white area of receiving pad and matrix flat area of a smartphone. The process is very similar to the calibration of camera lenses with a chess-board calibration pad. Then we process the image from the camera (a leaf on the white background) with the purpose to get a good outline.
The coordinates of outline points are recalculating from the camera’s coordinates to the real ones. Using these meanings, we find a leaf area. Simultaneously, we work with a shade minimizing it and with petiole, cutting it out. For dark green index, we change the modes from RGB to HSV. Then we divide channels and using the colour grade, we find the average value for the leaf within the required range. After that – we normalize it to the range and get index. Using the dependencies between the chlorophyll and dark green index (in general, linear analogues like y = ax + b), obtained in our agtech laboratory, we re-calculate chlorophyll content directly in the app. Additionally, we use Fabric, Volley, Material Design.
On the early stage, we got problems with the accuracy of leaf area measurement but we have solved them after adding search of the matrix of homography into the measurement process. After that, we had some issues with productivity. Unfortunately, a smartphone is not the same as a computer; in general, it has quite a weak processor. Google will solve this problem simply sending data into the cloud. But there are places with no Internet coverage, 3G and other delights of civilization. Particularly, in the field within the experimental plots. The algorithm worked slowly and had been broken many times with errors like the full memory void*cv:: OutOfMemoryError(std::size_t). However, after re-writing it for the third time, we solved this problem and changed its strange behaviour.
Regarding resources – we got great help from the e-books on OpenCV (Packt Publishing), the knowledge base of answers.opencv.org, public repositories on Github and strong documentation exactly in the website of OpenCV library. Sure, we needed knowledge in Android. In particular, for setting up a compilation of native algorithms in libraries, using NDK for smooth work. But we have filled this gap thanks to amazing resource Udacity.
In other words, a part of the code is written not in Java, but using С++?
Yes. Code for work of the app, saving data in the database, has been written in Java within Android SDK. Code for work of the algorithm has been written in C++ with Android NDK transformed into a static library. At the start, the app is uploading the library and uses native calls for functions of OpenCV library. In general, there is Java-library for using OpenCV directly in the code of an app, without JNI. However, productivity is different comparing with native calls of code. Now we have productivity approximately 5FPS when measuring leaf area in real-time. Using java-library I suggest something near 2FPS.
Thank you for answers with technical details. Now I suggest systematizing. Can you briefly make the list of steps for guys who do not have professional support in agtech or any other area? But they would like to show off their technical solution to a real problem. In the way to let potential investors know about them.
It is quite a tough question. But let’s try. First of all, we need to understand what does “professional support” mean. If we are talking about money – the best way to start is just to earn them in a full-time or freelance job before starting the whole startup story. If we are talking about kind of protection – it is a bit of pure luck. Suggesting media coverage, it is very important to have a good network of journalists (but the liver will be harmed) or good media subject (ie, win at the agtech or other niche hackathon). Having at least one but in the best cases – all of these options will increase chances to be known by potential investors. However, we have one small “but”. To be known does not mean to be invested in. Almost all potential Ukrainian investors know about our project. But they do not invest any money in it. Why? This is a philosophical question.
The measure leaves surface areas, save your measurements, see the comparison of leaves surface areas, and add experimental crops and fields Source: Android description
Useful information about Petiole
Download: Android 4.2 (3.4MB) Price: free (there is a paid option) Language: English Runs offline: no Last update: Android 15/03/2016 Website of the developer: Petiole
This application allows you to calculate in a simple way the surface area of the leaves (what is known as foliar area), an important variable for estimating plant growth or agricultural potential.
The use of the application requires opening a user account (if you only want to try it, you have a possibility to use a demo version). Once implemented this requirement, you access a screen in which the leaves whose surface has been calculated previously are stored. To perform the measurement of a new leaf, Petiole will ask you first to install OpenCV Manager, a small application that, among other features, allows detection and object recognition. You also have to print this file containing a chessboard, which it will allow you to calibrate the camera of the mobile device and get the value of the surface area (to start calibration, click the button with the + symbol). It is advisable to use some kind of plastic cover to crush the leaf (a transparent cover of a CD, for example). Also, if you need accurate measurements, from Petiole recommend using a stand to eliminate vibrations transmitted by hand and realize the calculations always from the same distance.
The foliar area “is an important variable for most ecophysiological studies in terrestrial ecosystems concerning light interception, evapotranspiration, photosynthetic efficiency, fertilizers, and irrigation response and plant growth”, as Pandey et al. remember in the study “A simple, cost-effective method for leaf area estimation“. However, it is an extremely laborious and often expensive task if you opt for the use of complex equipment to carry out a direct measurement.
Petiole, like previous initiatives, seeks to facilitate this work using a previously calibrated mobile as a measuring tool for obtaining these parameters. It has not been compared the degree of accuracy provided by this application in relation to the usual methods of work or professional equipment traditionally used, although it is a tool that is being received with interest by the scientific community.
The most important task in one’s life is to find your own way and follow it. I had been seeking my life journey for almost 15 years, working in different fields and countries. I had started many endeavours, but most of them failed. Now I know why: I am completely sure that entrepreneurship in technology is the area in which I’m meant to achieve my goals.
This decision was never based on a desire to be an IT girl, but rather a deep belief that informational technology is one of the greatest tools to create change in the world. My co-founder, Andrii Seleznov, and I had a friend who is an agricultural scientist and came to us with a problem, which prompted us to want to solve it.
Now, I am inspired by the value and efficiency of our products and extremely thankful for my team at Petiole. Actually, I think that the team is a beating heart of any startup. When you are surrounded by the “right people” and work hard – sooner or later success will come to you.
Maryna Kuzmenko, 31, lives in Kiev and holds a PhD in Business Law. Previously, she worked in the legal field with particular interest in IT. In late 2013, together with Andrii Seleznov, she co-founded Petiole – a mobile based recommendation system for precision agriculture to monitor dynamics of plant growth using leaf area measurement and level of chlorophyll. In November 2015, this project was ranked among the 20 Hottest Startups of the Year by CNBC.
Q: What inspires you?
A: I am inspired by the idea to make something better and to make someone’s life easier and more positive.
Vyacheslav Bykov, a graduate of Agrotechnology and Ecology Department of the Tavria State Agrotechnological University (TSAU), a leading engineer at the Institute of Evolutionary Ecology of the National Academy of Science of Ukraine shares his experience of measuring leaf area with Petiole. He shares his feedback and provides insights about his scientific work.