Automation of crop management by machine learning

在设施栽培中，要获得较高的产量，尤其是将环境因子控制在最优水平或对作物进行最优管理，需要做大量的工作。该项目旨在开发一种系统，以减少管理作物所需的工作量。作物的管理取决于当地的条件，如地下水位或当地气候。因此，我们是否能将通用算法应用于这种相当局部规定的管理是值得怀疑的。考虑到这一点，我们正在开发一种系统，它首先试图从农民管理的测量条件(例如，规则)中学习农民管理的方法或原则。气候条件)和农民的经营行为。在从测量中归纳出规则后，系统将规则应用于管理，将农民从管理劳动中解放出来。农民对环境因素的控制不一定是基于实际测量的值，而是在大多数情况下根据他们的感觉。农民的行为也不像机器那样准确。这意味着学习算法必须能够应对模糊性和噪声，这将使学习策略变得相当困难。尽管作出了相当大的努力，我们在这一点上还没有取得成功。然而，我们开发的学习算法被证明能够完全复制管理行为，如果它不包含模糊性或噪声。该算法适用于大多数作物管理的情况。

In protected cultivation, much work is needed to get higher yield especially in controlling the environmental factors at the optima or in managing the crops optimally. This project aims at developping a system which reduced amount of work needed in managing the crops. Management of the crops are dependent on local conditions such as water table level or local climate. It is, therefore, questionable if we can apply a general algorithm to such a quite locally prescribed management. Taking this point into consideration we are developping a system which first tries to learn the methods or principles of management of the farmer (rules, hereafter) from the measured conditions prescrbing the management (eg. climate conditions) and the management behavior of the farmer. After inducing the rules from the measurements, the system applies the rules to the management, which releases the farmer from the management labor. Farmers control the environmental factors not necessarily based on the physically measured values but in most cases according to what they feel. Farmers also do not act as accurately as machines. These mean that the learning algoritjm should be able to cope with fuzziness and noise, which will make the learning strategy quite difficult. In spite of considerable effort we have not yet been successful in this point. However, the learning algorithm we have developped proved to be able to completely copy the management behavior, if it containes no fuzziness nor noise. This algorithm can be applied to most cases of the crop management.