Alternative segmentation techniques other than watershed for soil particles in images

You could try using Connected Components with Stats already implemented as cv2.connectedComponentsWithStats to perform component labeling. Using your binary image as input, here's the false-color image:

The centroid of each object can be found in centroid parameter and other information such as area can be found in the status variable returned from cv2.connectedComponentsWithStats. Here's the image labeled with the area of each polygon. You could filter using a minimum threshold area to only keep larger polygons

Code

import cv2
import numpy as np

# Load image, Gaussian blur, grayscale, Otsu's threshold
image = cv2.imread('2.jpg')
blur = cv2.GaussianBlur(image, (3,3), 0)
gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Perform connected component labeling
n_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(thresh, connectivity=4)

# Create false color image and color background black
colors = np.random.randint(0, 255, size=(n_labels, 3), dtype=np.uint8)
colors[0] = [0, 0, 0]  # for cosmetic reason we want the background black
false_colors = colors[labels]

# Label area of each polygon
false_colors_area = false_colors.copy()
for i, centroid in enumerate(centroids[1:], start=1):
    area = stats[i, 4]
    cv2.putText(false_colors_area, str(area), (int(centroid[0]), int(centroid[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1)

cv2.imshow('thresh', thresh)
cv2.imshow('false_colors', false_colors)
cv2.imshow('false_colors_area', false_colors_area)
cv2.waitKey()

I used U-Net for another application, and your case is very similar to what U-Net do. You can find more information here. But generally, it is a convolutional neural network for medical image segmentation.

To start using U-Net, you can find a pre-trained model and apply it on your images and see the result.