How to use java.time.LocalDate in Datasets (fails with java.lang.UnsupportedOperationException: No Encoder found)?

• Spark 2.1.1
• Scala 2.11.8
• Java 8
• Linux Ubuntu 16.04 LTS

I'd like to transform my RDD into a Dataset. For this, I use the implicits method toDS() that give me the following error:

Exception in thread "main" java.lang.UnsupportedOperationException: No Encoder found for java.time.LocalDate
- field (class: "java.time.LocalDate", name: "date")
- root class: "observatory.TemperatureRow"
    at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:602)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$9.apply(ScalaReflection.scala:596)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$9.apply(ScalaReflection.scala:587)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
    at scala.collection.immutable.List.flatMap(List.scala:344)

In my case, I must use the type java.time.LocalDate, I can't use the java.sql.data. I have read that I need to informe Spark how transforme Java type into Sql type, I this direction, I build the 2 implicits functions below:

implicit def toSerialized(t: TemperatureRow): EncodedTemperatureRow = EncodedTemperatureRow(t.date.toString, t.location, t.temperature)
implicit def fromSerialized(t: EncodedTemperatureRow): TemperatureRow = TemperatureRow(LocalDate.parse(t.date), t.location, t.temperature)

Below, some code about my application:

case class Location(lat: Double, lon: Double)

case class TemperatureRow(
                             date: LocalDate,
                             location: Location,
                             temperature: Double
                         )

case class EncodedTemperatureRow(
                             date: String,
                             location: Location,
                             temperature: Double

val s = Seq[TemperatureRow](
                    TemperatureRow(LocalDate.parse("2017-01-01"), Location(1.4,5.1), 4.9),
                    TemperatureRow(LocalDate.parse("2014-04-05"), Location(1.5,2.5), 5.5)
                )

import spark.implicits._
val temps: RDD[TemperatureRow] = sc.parallelize(s)
val tempsDS = temps.toDS

I don't know why Spark search an encoder for java.time.LocalDate, I provide implicit conversions for TemperatureRow and EncodedTemperatureRow...