Bio-inspired techniques and theirapplication to precision agriculture

Andrés Pérez-Uribe and Eduardo SanchezREDS Institute (http://reds.eivd.ch)

University of Applied Sciences of Western-Switzerland - EIVDEmails: andres.perez-uribe@eivd.ch, eduardo.sanchez@eivd.ch

The CO-CH project

Bio-inspired computational techniquescapable of producing complex modelsto predict/describe the site-specificbehavior of given crops

Bio-inspired techniquesThe philosophy of bio-inspired systemsconsists on studying nature’s “inventions”,“tricks” and “artifices” to develop engineeringsolutions endowed with life-like properties.

Chardon VELours et CROchet = VELCROTM

Bio-inspired systems: family album

• Artificial intelligence• Expert systems• Computational intelligence (IEEE)

• artificial neural networks• evolutionary computation• fuzzy systems

• Machine learning• Modern heuristics• Statistical learning• Data mining

Artificial neural networks (ANN)An ANN is a network of many simple processors (”neurons”).These processors are connected by communication channelswhich usually carry numeric (as opposed to symbolic) data,encoded by any of various means. The “neurons” generallyoperate only on their local data and on the inputs theyreceive via the connections.

inputsoutput

interconnection

Artificial neuroncorrelating input dataand local data (weights)

Biological and artificial neural networks

Some ANNs are modelsof biological neuralnetworks and some arenot, but historically,much of the inspirationfor the field of ANNscame from the desire toproduce artificial systemscapable of "intelligent"computations similar tothose that the humanbrain routinely performs.

Artificial neural network learning• Learning replaces programming• Learning is achieved by giving examples to the network and

adapting the local data stored by the “neurons” while tryingto reduce an error function. Other methods are based onself-organization or trial-and-error learning.

task/problem

representation responseserror measure

Artificial neural nets andstatistical methods

• There is considerable overlap between the fields ofneural networks and statistics. In neural networkterminology, statistical inference means learning togeneralize from noisy data. For example:

• Feed-forward nets with no hidden layer are basicallygeneralized linear models

• Feed-forward nets with one hidden layer (MLPs) areclosely related to projection pursuit regression

• Kohonen nets for adaptive vector quantization arevery similar to k-means cluster analysis.

• Hebbian learning is closely related to principalcomponent analysis.

Why use artificial neural networks ?

• There are many answers to that question dependingon what kind of neural net you're interested in!

• For example, the main advantage of MLPs overprojection pursuit regression is that computingpredicted values from MLPs is simpler and faster.Also, MLPs are better at learning moderatelypathological functions (i.e., with discontinuities) thanare many other methods with stronger smoothnessassumptions.

Artificial neural network modeling

numerical data

noisy data

non-numerical data

trainingtest

design loop

model

validation loop

modelinputsprediction

explanation/visualization

Some ANN agriculture applications

• McClendon et al. (2005) Decision support for freeze protection usingartificial neural networks (blueberries, peaches), Agr. Outlook Forum• Mi et al. (2005) Testing the generalization of artificial neural networks with cross-validation and independent-validation in modelling rice tillering dynamics (rice tiller is a specialized grain-bearing branch, an important trait for grain production), Ecological Modeling• Lahoche et al. (2003) An innovative approach based on neural networks for predicting soil component variability, Am. Soc. Agro.• Drummond et al. (2000) Predictive ability of neural networks forsite-specific yield estimation (Soybean, Sorghum, Corn), Intl. Geospatial Information in Agriculture and Forestry Conference• Vucetic et al. (1999) A Data Partitioning Scheme for Spatial Regression (predict wheat yield from spatial attributes in order to extrapolate this knowledge to different agricultural sites, or to the same site but in different years), Intl. Joint-Confrence on NN.

Evolutionary computation

Solving a problem by artificialevolution consists on generating apopulation of potential solutions tothe problem and simulating thenatural processes of selection andreproduction (replication with slightmodifications). A solution is represented by a bit-string emulating genes in livingorganisms.The « best » solutions are selectedby using a performance function

population

parents

offspring

selection

crossover

mutation

replacement

“survival of the fittest”

Why use evolutionary computation (EC) ?

problems

perf

orm

ance

Problem-tailoredsolution

EC

randomsearch

EC

Hybrid techniques: EC-ANN

Artificial evolution can replace learning by searchingthe weights for an artificial neural network. Moreinterestingly, it can be used to adapt the topology ofthe artificial neural network, or even finding theparameters of the learning algorithms associated tothe artificial neural network.

Fuzzy logicMathematical formalism for representing impreciseknowledge in a human-like way.Contrary to formal “crisp” logic where a propositionis either true or false, in the Fuzzy logic formalismthere is a “degree of truth” for the given proposition.Key concept: partial membership

Why use Fuzzy logic ?

• WHITE-BOX models (e.g., based on differential equations)• analytically obtained• formal description

• BLACK-BOX models (e.g., based on artificial neural nets)• obtained by learning from examples• no internal description

• GRAY-BOX models (e.g., based on fuzzy logic formalism)• less-formal• understandable description

Fuzzy logic modelingHow to build a gray-box model ?

This approach is known as knowledge engineering

Hybrid techniques: Fuzzy-ANN / Fuzzy-ECHow to build a gray-box model 2 ?

This approach is known as knowledge discovery

Concluding remarks

• By studying, understanding and exploiting nature’s“tricks” and “artifices”, engineers can provideinnovative solutions to engineering problems

• Artificial neural network techniques have proven to bepowerful tools for modeling and prediction out ofnoisy data. Moreover, they appear to perform quitewell without strong smoothness assumptions.

• Certain ANN techniques provide innovative ways toprocess and visualize highly-dimensional information.

• Evolutionary techniques can be used to reach a spaceof solutions not reachable with ordinary engineeringtechniques.

Concluding remarks (2)

• The non-formal Fuzzy logic formalism enables thegeneration of understandable descriptions of models.

• The development of hybrid models (Evo-ANN, Fuzzy-ANN, Evo-Fuzzy, and Evo-Fuzzy-ANN) should enableus to fully profit of the combined advantages ofthese approaches.

• Bio-inspired systems should enable us to developcrop models out of noisy data and non-numericalinformation. Differential modeling should enable usto discover crucial factors leading from high tohigher productivity and migration of species to novelregions based on environmental similarities.