Boosting of fruit choices using machine learning-based pomological recommendation system.

Pomology, also known as fruticulture, is a significant contributor to the economies of many nations worldwide. While vertical farming methods are not well-suited for fruit cultivation, substrate-based cultivation is commonly practiced. Vertical farming methods use no soil for cultivation of the plants, and the cultivation is done in vertically stacked layers. Therefore, smaller herbs are best suited for such cultivation, whereas, the majority of the fruit trees are big and woody. Therefore, vertical farming methods are not well suited for fruit trees. However, to maximize fruit production, smarter substrate cultivation methods are needed. Utilizing remote sensing techniques, such as Internet of Things (IoT) devices, agriculture sensors, and cloud computing, allows for precision agriculture and smart farming in autonomous systems. Nevertheless, a lack of understanding of fruit nutrient requirements, growing conditions, and soil health conditions can result in reduced fruit production. To address these challenges, this paper proposes an intelligent model based on machine learning that recommends the best fruit to grow based on prevailing soil and climatic conditions. The system is trained on a dataset that includes details on eleven different fruits, such as Nitrogen (N), Phosphorous (P), Potassium (K), temperature, humidity, pH, and rainfall. The model takes into account the soil type and nutrient contents to recommend the most suitable fruit to grow in the prevailing climate. To enhance the model's efficiency, two novel techniques, Gradient-based Side Sampling (GOSS) and Exclusive Feature Bundling (EFB), have been incorporated. The results show that the proposed system has achieved 99% accuracy in recommending the right fruit based on the given environmental conditions. As a result, this system has the potential to significantly improve the profitability of the pomology industry and boost national economies.Article Highlights: This article aims at the creation of an efficient recommendation model for fruit cultivation in soil medium by analyzing the soil nutrient contents and the existing climatic conditions. The most suitable fruit plant corresponding to the existing conditions and soil type is recommended for an enhanced yield of the plant. Three climatic parameters, i.e., temperature, humidity, and rainfall; along with four soil-based parameters, i.e., pH, N content, P content, and K content are considered as the required growing condition for eleven varieties of fruits. To ensure enhanced accuracy, a hundred entries for each fruit type is entered in the dataset. The created dataset is then divided in the proportion of 7:3 as training data: testing data and Light Gradient Boosting Machine (Light GBM) model is applied to the created dataset. The correlation of all the parameters is checked for an efficient recommendation of fruits. Finally, the model is evaluated and its efficiency is checked. [ABSTRACT FROM AUTHOR]