Classification and Indirect Weighing of Sweet Lime Fruit through Machine Learning and Meta-heuristic Approach

In the past few decades, both academicians and industries have shown interest toward agricultural post-harvest operation aiming to reduce losses. order assist farmers in decision-making some effective innovative methodological frameworks are required. The fruit weight measurement is of prime importance many food processing during sorting, grading, packaging. this work, different Support vector machine (SVM) classifiers as well weighing models developed using optimized adaptive neuro-fuzzy inference system (ANFIS) coupled with a computer vision proposed. More precisely, based on hybrid ANFIS approach two well-known optimization algorithms analyzed. first approach, series GA-ANFIS been evaluated for population size. later PSO-ANFIS by varying most influential parameters. comprehensive self-built color image database has used calibration validation models. From an economic point view, indirect way fruits may be useful growers traders deciding market depending size before result shows higher reliability prediction capability proposed meta-heuristics (GA-ANFIS) model estimating sweet lime fruit.