Enhancing Tomato Cultivation for Quality Yield and Demand Fulfillment

Tomato farming is critical to meeting the growing demand for high-quality tomato products. With a growing population and shifting dietary tastes, there is an increased demand for sustainable and efficient farming practices that can ensure the production of premium-quality tomatoes. This study article provides a comprehensive strategy for optimizing tomato growing, from the farming process to the final product, to fulfil the ever-expanding market demands. Damage caused by nutrient deficiencies, damage caused by various insects, damage caused by various diseases affecting the crop and damage caused by wild animals can be mentioned as the reasons for reducing the optimum yield of tomato cultivation. The main objective of this research is to reduce the damage caused to tomato cultivationdue to these reasons. For this, models have been trained with the help of machine learning, algorithms, and image processing. The final product is a user-friendly mobile application. Through this mobile application, the user can get information about the nutritional deficiency, whether the insectthat has come to the plantation is harmful to the plantation or not, and what the disease is, by uploading an image of the relevant part of the plant showing signs of suspected nutritional deficiency, uploading images of insects coming to the plantation, uploading an image of the diseased part of the plant if a disease has developed. The results of this research demonstrate the successful development of a user-friendly mobile application that utilizes machine learning, algorithms, and image processing to provide information about nutrient deficiencies, harmful insects, and diseases affecting tomato plants, thereby reducing damage to tomato cultivation.