Artificial Intelligence for Precision Cultivation of Solanaceae Crops Enhancing Yield and Resource Efficiency

AI applications in precision agriculture have brought ground-breaking ways to improve yield, sustainability, and profitability in Solanaceae crop production. This chapter explains how various AI technologies, including but not limited to machine learning, computer vision, geo-informatics, and remote sensing, can contribute to the creative and sustainable cultivation of major Solanaceae crops - tomato, potato, eggplant, and chili pepper. Solanaceae crops are economically and nutritionally crucial for agriculture worldwide, yet they face significant challenges such as variable climate, pests and diseases, nutritional disorders, and wastewater use. This chapter describes how AI algorithms process ‘big’ data from sensors, satellites, and farm machinery. These data help inform specific crop management by site. There is a strong emphasis in the chapter on predictive modelling to predict disease risk, intelligent irrigation scheduling, automatic weeding and harvesting, growth stage-based nutrient recommendation, and so on. The chapter also describes AI-based decision-support systems and mobile apps that give farmers actionable information in real time, so they can minimize waste and maximize the efficient use of inputs. It also reviews AI applications related to Solanaceae production systems using examples from different geographical locations, such as yield prediction models, phenotyping platforms, and drone monitoring systems. Finally, the chapter turns to early challenges, including data standards, reaching smallholders, and ethics on using AI. For the future outlook, this chapter discusses the possible marriage of AI with robots, blockchain, and genomics to make Solanaceae farming more productive, resilient, and environmentally friendly.