Abstract:
Accurate and reliable yield forecasting is required for efficient planning and management of an important crop like apple.
Efforts have been made to predict apple yield, mostly through the use of statistical tools with limited indicator parameters. The proposed
neural network (NN) based system predicts yield of apple crops in an orchard based on identification, characterization, time of arrival
and duration of phenological stages interactively with soil and weather parameters. The task of automatic yield prediction in orchards
is challenging. Despite the significant amount of work that has been put into developing automated methods for estimating yields, the
majority of methods currently in use are based on fruit counting, which is only useful one to four weeks before harvest. Whereas,
in the proposed system, we will be predicting yield, during each phenological phase, among six classes, taking into account time of
phenological stage occurrence (i.e. early occurrence, normal occurrence, or delay occurrence), soil parameter, and parameter related to
weather conditions. This model will help the growers to timely take decision to execute contingency plans in case of average or low
yield. The f1-score of the proposed system is 0.94. It is compared with other popular machine learning (ML) algorithms like Logistic
regression, Support vector machines (SVM) and K-nearest neighbors (KNN).
