An innovative artificial neural network model for smart crop prediction using sensory network based soil data.

A thriving agricultural system is the cornerstone of an expanding economy of agricultural countries. Farmers' crop productivity is significantly reduced when they choose the crop without considering environmental factors and soil characteristics. Crop prediction enables farmers to select crops that maximize crop yield and earnings. Accurate crop prediction is mainly concerned with agricultural research, which plays a major role in selecting accurate crops based on environmental factors and soil characteristics. Recently, recommender systems (RS) have gained much attention and are being utilized in various fields such as e-commerce, music, health, text, movies etc. Machine learning techniques can help predict the crop accurately. We proposed an innovative artificial neural network (ANN) based crop prediction system (CPS) to address the farmer's issue. The parameters considered during sensor-based soil data collection for this study are nitrogen, phosphorus, potassium, temperature, humidity, pH, rainfall, electrical conductivity, and soil texture. Python programming language is used to design and validate the proposed system. The accuracy and reliability of the proposed CPS are assessed by using accuracy, precision, recall, and F1-score. We also optimized the proposed CPS by performing a hyperparameter Optimization analysis of applied learning methods. The proposed CPS model accuracy for both real-time collected and state-of-the-art datasets is 99%. The experimental results show that our proposed solution assists farmers in selecting the accurate crop and producing at their best, increasing their profit.