Agriculture crop recommendation based on productivity and seasons

Precision agriculture is important in maximizing crop yields through the delivery of customized advice based on environmental and soil conditions. This paper presents a sophisticated crop recommendation system that employs machine learning methods to examine soil characteristics, past yield data, and seasonal trends. It utilizes a convolutional neural network (CNN) for predicting appropriate crops for any particular field based on weather forecasting to enable decision making by the farmer for enhancing output and maximizing resources. High predictability by combining weather parameters with past yields enhances resource reduction for wastage to a high percentage. Testing in experimental processes is shown to surpass conventional outcomes based on efficiency improvement and enhancement in precision with minimal variability during tests under a range of different climates. The findings obtained from this research help in further developing intelligent farm systems to ensure global food security through data-intensive agricultural practices. Image-based extraction of NPK can attain a 83% accuracy rate if trained efficiently using actual soil samples. The crop suggestion model generally has a 91% accuracy, although this also varies with the quantity and quality of the training set and how well the model has been tuned.