diff --git a/src/io/tree.cpp b/src/io/tree.cpp
index ce45d20cf454..fd33335c8260 100644
--- a/src/io/tree.cpp
+++ b/src/io/tree.cpp
@@ -416,12 +416,16 @@ std::string Tree::ToJSON() const {
   str_buf << "\"num_cat\":" << num_cat_ << "," << '\n';
   str_buf << "\"shrinkage\":" << shrinkage_ << "," << '\n';
   if (num_leaves_ == 1) {
+    str_buf << "\"tree_structure\":{";
     if (is_linear_) {
-      str_buf << "\"tree_structure\":{" << "\"leaf_value\":" << leaf_value_[0] << ", " << "\n";
-      str_buf << LinearModelToJSON(0) << "}" << "\n";
+      str_buf << "\"leaf_value\":" << leaf_value_[0] << ", " << '\n';
+      str_buf << "\"leaf_count\":" << leaf_count_[0] << ", " << '\n';
+      str_buf << LinearModelToJSON(0);
     } else {
-      str_buf << "\"tree_structure\":{" << "\"leaf_value\":" << leaf_value_[0] << "}" << '\n';
+      str_buf << "\"leaf_value\":" << leaf_value_[0] << ", " << '\n';
+      str_buf << "\"leaf_count\":" << leaf_count_[0];
     }
+    str_buf << "}" << '\n';
   } else {
     str_buf << "\"tree_structure\":" << NodeToJSON(0) << '\n';
   }
@@ -731,6 +735,12 @@ Tree::Tree(const char* str, size_t* used_len) {
     is_linear_ = false;
   }
 
+  if (key_vals.count("leaf_count")) {
+    leaf_count_ = CommonC::StringToArrayFast<int>(key_vals["leaf_count"], num_leaves_);
+  } else {
+    leaf_count_.resize(num_leaves_);
+  }
+
   #ifdef USE_CUDA
   is_cuda_tree_ = false;
   #endif  // USE_CUDA
@@ -793,12 +803,6 @@ Tree::Tree(const char* str, size_t* used_len) {
     leaf_weight_.resize(num_leaves_);
   }
 
-  if (key_vals.count("leaf_count")) {
-    leaf_count_ = CommonC::StringToArrayFast<int>(key_vals["leaf_count"], num_leaves_);
-  } else {
-    leaf_count_.resize(num_leaves_);
-  }
-
   if (key_vals.count("decision_type")) {
     decision_type_ = CommonC::StringToArrayFast<int8_t>(key_vals["decision_type"], num_leaves_ - 1);
   } else {
diff --git a/tests/python_package_test/test_engine.py b/tests/python_package_test/test_engine.py
index b46526bcfaf6..4ef72888e767 100644
--- a/tests/python_package_test/test_engine.py
+++ b/tests/python_package_test/test_engine.py
@@ -3986,12 +3986,36 @@ def test_reset_params_works_with_metric_num_class_and_boosting():
     assert new_bst.params == expected_params
 
 
+def test_dump_model_stump():
+    X, y = load_breast_cancer(return_X_y=True)
+    # intentionally create a stump (tree with only a root-node)
+    # using restricted # samples
+    subidx = random.sample(range(len(y)), 30)
+    X = X[subidx]
+    y = y[subidx]
+
+    train_data = lgb.Dataset(X, label=y)
+    params = {
+        "objective": "binary",
+        "verbose": -1,
+        "n_jobs": 1,
+    }
+    bst = lgb.train(params, train_data, num_boost_round=5)
+    dumped_model = bst.dump_model(5, 0)
+    print(dumped_model)
+    assert len(dumped_model["tree_info"]) == 1
+    tree_structure = dumped_model["tree_info"][0]["tree_structure"]
+    assert "leaf_value" in tree_structure
+    assert "leaf_count" in tree_structure
+    assert tree_structure["leaf_count"] == 30
+
+
 def test_dump_model():
     X, y = load_breast_cancer(return_X_y=True)
     train_data = lgb.Dataset(X, label=y)
     params = {
         "objective": "binary",
-        "verbose": -1
+        "verbose": -1,
     }
     bst = lgb.train(params, train_data, num_boost_round=5)
     dumped_model_str = str(bst.dump_model(5, 0))
@@ -4000,6 +4024,7 @@ def test_dump_model():
     assert "leaf_const" not in dumped_model_str
     assert "leaf_value" in dumped_model_str
     assert "leaf_count" in dumped_model_str
+
     params['linear_tree'] = True
     train_data = lgb.Dataset(X, label=y)
     bst = lgb.train(params, train_data, num_boost_round=5)