Hybrid Deep Learning Implementation for Crop Yield Prediction

Year 2023, , 648 - 660, 28.06.2023

Halit Çetiner

https://doi.org/10.35414/akufemubid.1116187

Abstract

Agriculture producers should be supported technologically in order to continue production in a way that meets the worldwide food supply and demand. Automatic realization of crop yield estimation calculation is a desired need of farmers. Automatic yield estimation also facilitates the work of agricultural producers with different goals such as imports and exports. To achieve the stated objectives, deep learning models have been developed that estimated yield using parameters such as the amount of water per hectare, the average amount of sunlight received by the hectare, the amount of fertilization per hectare, the number of pesticides used per hectare, and the area of cultivation. With the hybrid model created by combining the strengths of the LSTM and CNN models developed within the scope of this article, the success rate of data prediction has increased with fine adjustments. Success rates of 89.71 R2, 0.0035 MSE, 0.0248 RMSE, 0.0461 MAE, and 10.10 MAPE have been achieved with the Proposed hybrid model. This model is competitive with similar studies with the stated values.

Keywords

Crop, Yield estimation, CNN, LSTM, Hybrid model

Hybrid Deep Learning Implementation for Crop Yield Prediction

Abstract

Tarım üreticilerinin dünya çapındaki gıda arz ve talebini karşılayacak şekilde üretime devam edebilmesi için teknolojik olarak desteklenmesi gerekmektedir. Mahsul verim tahmini hesaplamasının otomatik olarak gerçekleştirilmesi, çiftçilerin arzu ettiği bir ihtiyaçtır. Otomatik olarak verim tahmini gerçekleştirilmesi ithalat ve ihracat gibi farklı hedefleri olan tarım üreticisinin işlerini de kolaylaştırmaktadır. Belirtilen amaçlara ulaşabilmek için hektar başına su miktarı, hektar tarafından alınan ortalama güneş ışığı miktarı, hektar başına verilen gübreleme miktarı, hektar başına kullanılan pestisit miktarı, ekim yapılan alan bölgesi parametrelerini kullanarak verim tahmini gerçekleştiren derin öğrenme modelleri geliştirilmiştir. Bu makale kapsamında geliştirilen LSTM ve CNN modellerinin güçlü yanları birleştirilerek oluşturulan hibrit modelde ile veri tahmin başarı oranının ince ayarlamalar ile artırılmıştır. Önerilen hibrit model ile 89.71 R2, 0.0035 MSE, 0.0248 RMSE, 0.0461 MAE, ve 10.10 MAPE başarı oranlarına ulaşılmıştır. Bu model, belirtilen değerlerle benzer çalışmalarla rekabet edebilir seviyededir.

Keywords

Mahsul, Verim tahmini, CNN, LSTM, Hibrit model

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