Tomato Crop Yield Prediction in Indoor Environment with A Novel ABC Enhanced CNN with SDL Architecture
Keywords:
Tomato, Yield prediction, ABC, CNNAbstract
More over half of India's population relies on agriculture for their livelihood. Population growth means more people need to eat, thus growing food inside is essential. Tomatoes is one of the world's most extensively grown vegetable crops. It's on par with the fourth-best veggies in the world. During the development of a tomato, it is susceptible to a wide range of illnesses and pests. Reduced yields or even crop loss might occur if management measures are delayed. Accurately identifying the source and impact of crop output is crucial for figuring out how to manage diseases and pests efficiently and helping vegetable growers boost tomato yield. Multiple variables, including genetics, environment, and their interactions, impact crop yield, making it a highly complicated characteristic. Predicting yields with any degree of precision calls for first gaining a deep grasp of the functional connection between yield and various interaction components. In this research, we use a dataset from kaggle to forecast the yield of tomatoes grown indoors using a hybrid approach: the ABC-CNN classification algorithm. The prediction method makes use of the Convolutional Neural Network (CNN) model and the Artificial Bee Colony (ABC) algorithm with the Adam optimizer. The results demonstrate that our suggested technique outperforms competing algorithms in terms of accuracy and efficiency.
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