Crop Recommendation and Disease Detection Using Deep Neural Networks

Indian farmers use traditional practices and view-points to fully use their land. Taking into account the requirements of agriculture, the choice of crops is essential to farming. By suggesting a suitable crop based on various factors like the composition of phosphorus, potassium, and nitrogen in the soil, the soil's pH value, rainfall, temperature, and humidity using an artificial neural network, the suggested technique will assist farmers in maximizing agricultural productivity, reducing nutrient loss in crop fields, and using less fertilizer in crop production. The proposed work assists farmers in accurately selecting the crop for cultivation and achieving sustainability. Also, Plant diseases are major food threats that must be addressed before the entire field is destroyed. In this work, we made a plant leaves disease detection system using a 2D Convolutional Neural Network, which would monitor crop health digitally and significantly increase agricultural productivity and yield. Early detection of crop health and disease can aid in disease control through proper management strategies.